Patent Publication Number: US-2021174438-A1

Title: Computer network systems for electronic market estimation of forward looking term rate composed form real-world funding transaction data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 16/987,075, filed Aug. 6, 2020, which is a continuation-in-part of U.S. application Ser. No. 15/690,171, filed Aug. 29, 2017, which is a continuation of International Application PCT/US2015/018739, filed Mar. 4, 2015. U.S. application Ser. No. 15/690,171 is also a continuation-in-part of U.S. patent application Ser. No. 13/570,930, filed Aug. 9, 2012. The entire content of each of these applications is incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     The present invention is directed to a system and method for more accurately determining and providing market terms for conducting business transactions which results in benchmarks. The present invention proposes a transparent transaction based and rules based system for arriving at interbank interest rates that may be applicable for large, intermediate and small sized banks and other market participants. The present invention thus addresses a business challenge that is particular to the banking industry, in that it is able to apply computer processing in a new and useful way to fine tune and more accurately determine in real time an optimum and actual and verifiable benchmark such as an interest rate that banks can use in conducting loans or other transactions based on the analysis of actual transactions. 
     The London Interbank Offered Rate, or LIBOR, was created to provide an interest rate based on an average of daily estimates from participating banks. LIBOR is now used for derivatives contracts, as well as many credit cards, corporate loans and mortgages around the world. The average interest rate is estimated by leading banks in London that they would be charged for if borrowing from other banks. LIBOR, today, is the primary benchmark for short term interest rates around the world for banks of all sizes, but it has been criticized because it is based on estimates rather than on actual transactions. 
     The inter-bank lending market for midsized banks is set for transformational changes. These changes are triggered by shifts in regulatory environment, need for interest rate benchmarks and alternate interbank-funding opportunities for the banking industry. While these issues are true for the banking industry in general, the midsized banking sector, comprised of banks with assets ranging between $150 Billion to $1 Billion, will be particularly affected. 
     The Inter-bank funding market is fractured with multiple actors, interest rates and products. Interbank funds transactions are done through federal agencies, brokers and between banks involving different rates and tenures. At present, there does not exist a single organized market for interbank funding&#39;s causing higher transaction costs and market inefficiencies. In addition, the existing interbank market has not performed particularly well during times of financial crisis. During the financial crisis issues including uncertainty on counterparty credit worthiness, information asymmetry or potential lender&#39;s doubts on their own ability to borrow in the future causing severe constraints in interbank funding market. The daily average interbank lending volume has been declining since the 2008 financial crisis to about $114 billion at present from $145 billion in 1998. From a regulatory standpoint, there also exists uncertainty with regard to bank reporting requirements and funding levels that may impact adversely the interbank lending market. It is anticipated that as the economy emerges from the unique interest rate regime and Federal Reserve measures in place currently, an organized market for interbank lending will be critical. 
     While the current global benchmark for interbank offers is LIBOR, many midsize American banks do not necessarily benchmark their assets through LIBOR alone. Other benchmarks include FHLB, Eurodollar, repurchase agreements and other such measures. These multiple rates are priced based on varying standards, sources and credit criteria. This suggests lack of true price discovery in the interbank market that is relevant for the Midsized banking sector. 
     A single global benchmark may not provide the best asset/liability management for regional and midsized bank risk managers. This is especially true when interest rates normalize as the interbank rates that are relevant for larger banks will not be for the midsized banking sector. The inefficiencies with the current estimation of LIBOR, the global benchmark for interbank offer rate are well documented. LIBOR is an estimated rate and not a market determined rate. Further from a risk management standpoint, the uncertainty associated with the Libor estimates causes inefficiency and higher costs. 
     Further, LIBOR is being sunsetted and will no longer be used by the end of 2021. LIBOR is being phased out because it has been tainted by fraud and collusion. Unlike other financial instruments, LIBOR is an anomaly as it is not based on real transactions and it has existed as an asset class without choice. 
     AMERIBOR is an innovative benchmark interest rate with an ON (overnight) rate and a futures market developed by the American Financial Exchange (AFX) that replaces LIBOR. AMERIBOR is a volume-weighted average of real, daily transactions in the AFX&#39;s overnight unsecured, self-regulated, rules-based, and transparent loan market. There are many advantages to AMERIBOR. It is a regulated solution that has been approved by the International Organization of Securities Commissions (IOSCO) and adheres to all 19 principles established by IOSCO for a benchmark rate. Unlike LIBOR, AMERIBOR is based on historical transactions on the AFX, which has just under 200 members. AFX is already used by a third of the US banking sector and is already being used to price corporate loans. Additionally, AMERIBOR is the most stable reference benchmark interest rate with 99.74% correlation to LIBOR. Beyond AMERIBOR, the Federal Reserve and several firms are also working on alternative interest rate benchmarks. The most prominent is SOFR. There are other efforts by exchanges and trading platforms, but none currently exist. 
     AMERIBOR, unlike LIBOR, does not have a term structure. There are a few alternatives to create term structures for AMERIBOR. One alternative is to use an overnight index swap (OIS) to swap the AMERIBOR overnight (ON) rate for a longer-term rate. Another alternative is to use a futures market for the average ON rate in the nearest contract month. For example, at the end of June the July futures contract represents the average ON rate for the month of July. However, an averaged rate is clearly different from a forecasted rate, or the actual rate for the end of June. The term structure is also different from one generated from loans with greater maturity, i.e., 30-day, 90-day, etc. A few such markets that rely on this mechanism for generating term structures are 30-day federal fund futures, 30-day AMERIBOR futures, and 30-day SOFR futures. Accordingly, what is needed in the art is the ability to provide a correct model that is able to generate a 30-day, 90-day, or longer forecasted rate (an “indicative rate) that is accurate. 
     SUMMARY OF THE INVENTION 
     The present invention now overcomes some of the problems associated with the problems associated with calculating LIBOR rates and other market rates with cloud computing networks. The present invention provides a transparent transaction based and rules based system for arriving at interbank interest rates that is applicable for large, intermediate and small sized banks as well as other market participants. The invention is embodied in a system and method for determining market measures or benchmarks with market-based estimates preferably conducted on cloud computing or other computer networks. 
     The invention more accurately determines and provides market terms for conducting business transactions. The system generally comprises a plurality of computers at qualified institutions in electronic association in a network. One of the computers is a server network device that maintains and/or provides: 
     an electronic participation database for listing qualified institutions that have previously agreed to be bound by a pre-determined set of regulations to participate in conducting business and processing transactions based on market term estimates that are calculated by the system; 
     a first network interface for privately communicating with the qualified institutions; 
     a processing engine for receiving data at a predetermined time from the listed institutions regarding market terms or benchmarks currently being proposed by such institutions; analyzing the received data and calculating one or more market term estimates within a set time after receipt of the data; 
     a communications module associated with the first network interface for promptly and simultaneously transmitting the calculated market term estimate(s) via a secure data feed to the computers of each of the qualified institutions that are listed in the electronic participation database; 
     an auction module associated with the first network interface for conducting an auction between the listed institutions to determine the institutions&#39; response to the market term estimate(s) as suitable for implementation, wherein the institutions indicate acceptance of or modification of the provided market term estimate; 
     a verification module associated with the first network interface for adjusting the calculated market term estimate(s) based on the auction and for communicating the adjusted market term estimate(s) to the qualified institutions; and 
     a second network interface for publicly communicating and displaying the adjusted market term estimate(s) after providing the same to the qualified institutions. 
     The processing engine preferably includes a data collection database for receiving the data from the listed institutions and a market term estimate calculation engine for calculating the one or more market term estimates. The server network device is preferably a cloud server network device with one or more processors from plural network devices for communication with one or more processors or computers located at the qualified institutions. The auction module preferably receives offer quantities from the qualified institutions that will increase by a factor between 1.25 and 4 for each tier of the offer curve derived from the offer amounts provided. Next, bid quantities are increased at the same factor for each tier of the bid curve derived from the bid quantities provided. Then, the cloud server device calculates in real-time a market term estimate to create a calculated set of market term estimates using less bandwidth and less processing cycles on the cloud communications network than on a non-cloud communications network. The market term estimate calculation module transmits the one or more market term estimates or benchmarks to the institutions within 1 to 2 minutes after receipt of the data. The system advantageously includes a records database for storing the adjusted market term estimates that are provided to the institutions and the public. 
     Another embodiment of the invention is a method for avoiding fraud and more fairly and accurately conducting business transactions at lower costs, with all steps conducted by a server network device that is in electronically association in a network with a plurality of computers at qualified institutions. The method comprises: 
     maintaining an electronic participation database comprising a listing of qualified institutions that have previously agreed to be bound by a pre-determined set of regulations to participate in conducting business and processing transactions based on market term estimates that are calculated by the server network device; 
     privately communicating with the qualified institutions over a first network interface in order to receive data at a predetermined time regarding market terms or benchmarks currently being proposed by such institutions; 
     analyzing the received data; 
     calculating one or more market term estimates within a set time after receipt of the data; 
     providing a private communications network comprised of the qualified institutions; 
     promptly and simultaneously transmitting the calculated market term estimates via a secure data feed over the first network interface to the computers of each of the qualified institutions that are listed in the electronic participation database; 
     conducting an auction over the first network interface between the institutions to determine the institutions&#39; response to the market term estimate(s) as suitable for implementation, wherein the institutions indicate acceptance of or modification of the provided market term estimate(s); 
     adjusting the calculated market term estimate(s) based on the auction; and 
     publicly communicating and displaying the calculated market term estimates over a second network interface after providing the same to the qualified institutions. 
     Yet another embodiment of the invention relates to the use of a server network device in a computer system comprising a plurality of computers at qualified institutions in electronic association in a network for more accurately determining and providing market terms for facilitating the conduct of business transactions by the qualified institutions. The server network device maintains and/or provides the components of the system as described herein. 
     Thus, the present invention facilitates and enables the qualified institutions to conduct business transactions based on the calculated market term estimate(s) or benchmarks in order to avoid fraud and to more fairly and accurately conduct such business at lower transaction costs than business conducted without use of the present system. 
     Further, because the market rate calculation described herein, which is currently used by AFX to calculate the AMERIBOR ON at the end of each business day, has been widely adopted in recent years, it has also been determined that the AMERIBOR ON can also be utilized to price a 30-day and 90-day (or other time period) indicative AMERIBOR rate. The indicative rate is calculated by using a non-linear regression model for 30-day and 90-day interest rates using ON data. The model is then utilized to estimate a term structure for the indicative rate. 
     Another embodiment describes a computer-implemented method for electronic market calculation of an indicative term structure for an interest rate benchmark with market-based measures on a cloud communications network, the method comprising: 
     calculating, using a processor, the dollar basis point value of relevant transactions occurring in commercial paper, commercial deposit, and AMERIBOR unsecured lending markets, so that the dollar basis point value is equal to the principal amount of a specific issuance multiplied by the days to maturity divided by 360 and multiplied by 0.01; 
     wherein the relevant transactions are all commercial paper, commercial deposit, or AMERIBOR® unsecured lending transactions originated during a specified period of time by USA-based banks, financial institutions, or corporates with notional values of $1,000,000 or multiples thereof and fixed interest rates. The relevant range shall be normalized around a specific term, i.e. 30-day, 90-day, etc. The range shall be predetermined to reflect the specific term. These relevant transactions were selected for inclusion in order to calculate representative wholesale funding costs around the specific term for American financial entities; 
     storing, in a memory module, all relevant transactions and dollar basis point values previously calculated and updating them on a real-time basis; 
     retrieving the transactions data and preceding dollar basis point value calculations from the memory module in real-time using a cloud-based application for subsequent calculation of a term structure interest rate; 
     calculating, by means of the processor, a term structure interest rate composed of the interest rates of all relevant transactions (as defined above) weighted according to their transaction-level dollar basis point values for each predetermined period; 
     determining, by means of an algorithm, whether or not the underlying transaction volumes have reached a certain threshold for each relevant range, and making the necessary adjustments in such cases that volume thresholds are not met by extending the specific period of time in which relevant transactions may be originated; 
     this process is replicable, verifiable, unique amongst existing calculations of term interest rates, and can be easily adapted across different time frames (30-day, 90-day, 180-day . . . etc.) to generate additional term interest rates across additional predetermined periods as market participants demand to satisfy their varying business needs or any new business needs which may arise in the future; 
     and sending securely benchmark rate data, on a daily basis at a specified time, via a cloud communications network to a plurality of target network devices to provide electronic information as an indication of how qualified institutions have agreed to participate in establishing, conducting business, and processing transactions based on the calculated benchmark rate. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Further features of this invention, its nature, and various advantages will become more apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  is a high-level functional block diagram of a system and network for implementing the systems and methods of the present invention; 
         FIG. 2  is a functional block diagram illustrating a computer system configured to be in communication with other remote systems; 
         FIG. 3  is a diagram of a functional block diagram illustrating an embodiment in which administration of the account and updating of transmitted market estimates to the account can be operated on different computer systems; 
         FIG. 4  is block diagram illustrating an exemplary cloud communications network; 
         FIG. 5  is a block diagram illustrating an exemplary cloud storage object; 
         FIGS. 6A, 6B and 6C  are flow diagrams illustrating a method for determining market estimates with market-based measures; and 
         FIGS. 7A, 7B and 7C  are flow diagrams illustrating a method for determining market estimates with market-based measures on a cloud communications network. 
         FIG. 8  is a chart illustrating the plotting of the non-linear regression model equation, the 30-day LIBOR, and the predicted indicative values. 
         FIG. 9  is a chart showing the close agreement between the predicted 30-day Indicative AMERIBOR rate, the ON AMERIBOR, and the 30-day LIBOR. 
         FIG. 10  is a chart showing the close agreement between the predicted 90-day Indicative AMERIBOR rate, the ON AMERIBOR, and the 90-day LIBOR. 
         FIG. 11  is a chart showing the close agreement between a calculated 30-day rate and the 30-day LIBOR. 
         FIG. 12  is a chart showing the close agreement between a calculated 90-day rate and the 3-month LIBOR. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     As explained herein, the present invention is directed to a system and method for more accurately determining and providing market terms for conducting business transactions which results in benchmarks such as interest rates, commodity rates (e.g., gold or silver), foreign exchange rates and other market indexes or other rates based on market estimates. As used herein the term “benchmark” is used to mean a standard or set of standards, used as a point of reference for evaluating performance, level of quality or a minimum term of acceptance for buying or selling general or specific items. Benchmarks may initially be drawn from a company&#39;s own experience, from the experience of many companies in the industry, or in some cases from legal or governmental requirements or mandates (e.g., environmental regulations). The present invention processes all this data to provide a more accurate benchmark or market estimate. 
     In particular, the present method utilizes: 
     Market-based benchmarks for pricing various commodities and equity instruments; 
     An independent benchmarking process for interest rate that is transaction based; and 
     Use of the benchmarking method for creating stratified sets of interest rates that serve the needs according to the size of the participating banks. 
     The present invention also provides solutions to the challenges to midsized banks by providing an organized, transparent and regulated platform for inter-bank lending and borrowing. Further, it utilizes a market-based approach to arrive at an interbank lending rate—called AMERIBOR—which is specifically targeted at the midsized banking sector. In doing so, the present invention prices inter-bank funding by establishing a benchmark based on actual market transactions. 
     The establishment of a regulated market for interbank funding along with the establishing of a transparent, market-based benchmark for inter-bank borrowing and lending provides significant improvements over the current systems. By bringing many buyers and sellers together in a centralized regulated and transparent marketplace, the costs of doing business for market participants is significantly lowered. A further benefit is that the mid-sized banks are able to establish their own inter-bank rate separately from LIBOR, based on actual transactions, rather than rely on an “estimated” rate that is subject to speculation. 
     In a preferred embodiment relating to interest rates, the computer systems of the present invention are able to control the spread between the highest bids and offers of interest rates to within a finite and narrow basis point range at current levels. To do this, the server receives information from between 10 to 20 or even more participating banks of what their actual interest rates are and this data is then used to construct an offer curve in tiers that increases by a finite, small amount of basis points (e.g., by two basis points), while also constructing a bid curve in tiers that decrease by the same finite amount of points. A total of at least three offer tiers and three bid tiers are constructed from the collected data. The offer quantities and bid quantities are entered prior to any trades. The participating banks will also offer quantities that will double for each step of offer curve from the offer amount. The method is flexible however in that member banks have the option to input the tier values that satisfy the rules of the Exchange, and they can put in bids or offers that are tighter than the finite basis point value that is initially selected and they also can input quantities that are greater than the minimum required in each tier. 
     In particular, the participant banks are simply required to submit a bid and offer curve for 30-day interbank loans while also providing pre-determined credit approval and limits for all counterparties in the group. The submitted schedule includes an offer rate and offer quantity for a 30-day interbank loan or other tenors and a bid quantity to accept a 30-day interbank loan. The data submitted from the participating banks is submitted at a predetermined time and is processed relatively quickly to construct an offer and bid curve based on a set of established and transparent rules. 
     The particular rules require that the banks submit rates wherein: 
     the spread between the bid rate and offer rate must be within two basis points of current interest rate levels; and 
     the banks must offer or borrow amounts according to those established in the minimum offer/bid quantity tiers. The following Table 1 is an example of the latter requirement: 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Minimum Offer/Borrow Tiers are based on Bank Assets 
               
            
           
           
               
               
               
            
               
                   
                 Assets 
                 Tier 
               
               
                   
                   
               
               
                   
                 500 Million to 10 Billion 
                  1 Million 
               
               
                   
                 11 to 25 Billion 
                  2 Million 
               
               
                   
                 26 to 75 Billion 
                  5 Million 
               
               
                   
                 76 to 125 Billion 
                 10 Million 
               
               
                   
                   
               
            
           
         
       
     
     As noted, the offer curve is constructed in tiers that increase by a narrow range of e.g., two basis points, while the bid curve is constructed in tiers that decrease by the same two basis points. A total of at least three offer tiers and three bid tiers are constructed from the collected data. The participating banks will also offer quantities that will increase based on predefined rules (e.g., doubling, tripling or other factors ranging from 1.25 to 4) for each tier of the offer curve, and offer quantities that increase for each tier in a similar way based on the bid curves. 
     Next, to calculate the mean or average rate, an auction is electronically conducted. The auction includes two periods: (1) a trade match and initial pricing period and (2) a price confirmation period. In the trade match period: potential matches are made between the submitted bid and offer rates, and this generally leads to actual transactions taking place. The matching process is conducted for fifteen minutes. During that period, the participating banks agree to not amend or cancel any existing submissions. Based on the matching that is analyzed, an initial interest rate is calculated and published to the participating banks. The rate may be calculated as a mean rate or a volume weighted rate of the matching transactions. 
     Once the initial rate has been established, an auction platform is opened for participating banks to submit new bids and offers to the existing positions although the existing bids or offers cannot be amended. Trades are conducted for another fifteen minutes. Based on the total transactions in both periods, a volume weighted rate is computed as the final rate. 
     Thereafter, an open trading session is conducted which is open to all participating banks where they may borrow or lend 30-day interbank loans at the final rate for thirty minutes. At the end of the open trading session, the total volume transacted and established final rate is compiled and transmitted to each of the participating banks. 
     Accordingly the present invention defines a technical solution to a technical problem through the implementation of computer software in the context of accurately determining interest rates for various commercial transactions. As can be appreciated, the more accurate the interest rate can be calculated and applied for use, the greater the cost savings to the buyers and in turn the general public. 
     The system utilizes a number of electronic components to carry out the interest rate determination in a highly accurate way. The components are part of a server network device and include: 
     an electronic participation database for listing qualified institutions that have previously agreed to be bound by a pre-determined set of regulations to participate in conducting business and processing transactions based on market term estimates that are calculated by the system; 
     a first network interface for privately communicating with the qualified institutions; 
     a processing engine for receiving data at a predetermined time from the listed institutions regarding market terms currently being proposed by such institutions; analyzing the received data and for calculating one or more market term estimates within a set time after receipt of the data; 
     a communications module associated with the first network interface for promptly and simultaneously transmitting the calculated market term estimate(s) via a secure data feed to the computers of each of the qualified institutions that are listed in the electronic participation database; 
     an auction module associated with the first network interface for conducting an auction between the institutions to determine the institutions&#39; response to the market term estimate(s) as suitable for implementation, wherein the institutions indicate acceptance of or modification of the provided market term estimate(s); 
     a verification module associated with the first network interface for adjusting the calculated market term estimate(s) based on the auction and for communicating the adjusted market term estimate(s) to the qualified institutions; and [0063] a second network interface for publicly communicating and displaying the adjusted market term estimate(s) after providing the same to the qualified institutions. 
     In a preferred embodiment, the processing engine includes a data collection database for receiving the data from the listed institutions and a market term estimate calculation engine for calculating the one or more market term estimates. Also, the system preferably includes a records database for storing the adjusted market term estimates that are provided to the institutions and the public. 
     As noted herein, the communication network of the invention should be a cloud communications network with the server network device being a cloud server network device with one or more processors from plural network devices for communication with one or more processors or computers located at the qualified institutions. This allows the cloud server device to calculate in real-time a market term estimate for certain time periods to create a calculated set of market term estimates using less bandwidth and less processing cycles on the cloud communications network than on a non-cloud communications network. By “real time,” what is meant that the market term estimate calculation module calculates and transmits the one or more market term estimates to the institutions within 1 to 2 minutes after receipt of the data by the data collection module. 
     In a preferred embodiment, the invention provides a computerized, multi-step electronic loan transaction loan transaction trading system. This system includes: 
     an application server, wherein during a first electronic trading step, said application server receives first trading party data from a first trading party computerized system, said first trading party data including: 
     first trading party identity data; and 
     first trading party interest rate data for an electronic loan transaction. 
     The application server receives second trading party data from a second trading party computerized system, said second trading party data including: 
     second trading party identity data; and 
     second trading party interest rate data for said electronic loan transaction. 
     The application server also includes a predetermined, stored computerized listing of a plurality of trading party identity data in addition to said first trading party identity data and said second trading party identity data, wherein said computerized listing represents the only trading party computerized systems from which trading party data will be accepted during said first electronic trading step and from which trading party data is required to be received during said first electronic trading step. Thus, when said application server automatically electronically determines that: 
     a) said first trading party identity data matches a trading party identity data included in said predetermined, stored computerized listing, 
     b) said second trading party identity data matches a trading party identity data included in said predetermined, stored computerized listing, and 
     c) interest rate data for said electronic loan transaction has been received from all trading party computerized systems representing a trading party identity data included in said predetermined, stored computerized listing, the application server determines an average market rate data for said electronic loan transaction representing an average market rate determined by averaging said interest rate data received from all trading party computerized systems having a trading party identity data included in said predetermined, stored computerized listing. 
     The submission of said first trading party data by said first trading party computerized system represents an irrevocable command to execute a trade in said electronic loan transaction at said average market rate once it is determined, even when said average market rate differs from said first trading party interest rate data for said electronic loan transaction. Also, the submission of said second trading party data by said second trading party computerized system represents an irrevocable command to execute a trade in said electronic loan transaction at said average market rate once it is determined, even when said average market rate differs from said second trading party interest rate data for said electronic loan transaction. 
     The system includes an electronic trading platform, wherein said electronic trading platform receives from said application server: 
     said first trading party identity data; 
     said second trading party identity data; and 
     said average market rate data for said electronic loan transaction. The electronic trading platform automatically executes an electronic trade in said electronic loan transaction at said average market rate between said first trading party computerized system and said second trading party computerized system. 
     During a second electronic trading step, the electronic trading platform transmits data representing said electronic trade and said average market rate data to a plurality of wider market computerized systems, wherein said wider market computerized systems include computerized systems in addition to said trading party computerized systems representing a trading party identity data included in said predetermined, stored computerized listing. Thus, the electronic trading platform accepts from said wider market computerized systems trade data representing revocable trading commands to execute trades in said electronic loan transaction. 
     This application server advantageously includes a predetermined, stored computerized listing of a set of electronic loan transactions representing a plurality of maturities, wherein said first trading party data includes first trading party interest rate data for a plurality of electronic loan transactions and said second trading party data includes second trading party interest rate data for said plurality of electronic loan transactions. 
     The application server automatically electronically determines that interest rate data for said plurality of electronic loan transactions has been received from all trading party computerized systems representing a trading party identity data included in said predetermined, stored computerized listing. This is achieved by the application server determining an average market rate data for each of said plurality of electronic loan transactions representing an average market rate for each of said plurality of electronic loan transactions determined by averaging said interest rate data for each of said plurality of electronic loan transactions received from all trading party computerized systems having a trading party identity data included in said predetermined, stored computerized listing. Also, the submission of said first trading party data by said first trading party computerized system represents an irrevocable command to execute a trade in at least one of said plurality of said electronic loan transactions at said average market rate once it is determined, even when said average market rate differs from said first trading party interest rate data for said electronic loan transaction. 
     Another preferred embodiment of the invention relates to a computerized, multi-step electronic loan transaction trading system. This system is similar to the prior embodiment but the application server receives: 
     first trading party identity data; 
     first trading party electronic loan transaction trade amount data; 
     first trading party interest rate data for an electronic loan transaction, 
     second trading party identity data; 
     second trading party electronic loan transaction trade amount data; and 
     second trading party interest rate data for said electronic loan transaction, 
     The application server automatically electronically determines that electronic loan transaction trade amount data has been received from all trading party computerized systems representing a trading party identity data included in said predetermined, stored computerized listing, and determines an average market rate data for said electronic loan transaction representing an average market rate determined by calculating a weighted average based on said electronic loan transaction trade amount data and said interest rate data received from all trading party computerized systems having a trading party identity data included in said predetermined, stored computerized listing. The submission of such data represents an irrevocable command to execute a trade in said electronic loan transaction at said average market rate once it is determined, even when said average market rate differs from said second trading party interest rate data for said electronic loan transaction. The electronic trading platform automatically executes an electronic trade initially in said electronic loan transaction at said average market rate between said first trading party computerized system and said second trading party computerized system, and then during a second electronic trading step, transmits data representing said electronic trade and said average market rate data to a plurality of wider market computerized systems, wherein said wider market computerized systems include computerized systems in addition to said trading party computerized systems representing a trading party identity data included in said predetermined, stored computerized listing This allows the electronic trading platform to accept from said wider market computerized systems trade data representing revocable trading commands to execute trades in said electronic loan transaction. 
     In this system, the application server includes a predetermined, stored computerized listing of a set of electronic loan transactions representing a plurality of maturities and the first trading party data includes first trading party interest rate data for a plurality of electronic loan transactions and said second trading party data includes second trading party interest rate data for said plurality of electronic loan transactions. 
     The application server automatically electronically determines that interest rate data for said plurality of electronic loan transactions has been received from all trading party computerized systems representing a trading party identity data included in said predetermined, stored computerized listing. Additionally, the application server determines an average market rate data for each of said plurality of electronic loan transactions representing an average market rate for each of said plurality of electronic loan transactions determined calculating a weighted average based on said electronic loan transaction trade amount data and said interest rate data for each of said plurality of electronic loan transactions received from all trading party computerized systems having a trading party identity data included in said predetermined, stored computerized listing. As noted, the submission of said first trading party data by said first trading party computerized system represents an irrevocable command to execute a trade in at least one of said plurality of said electronic loan transactions at said average market rate once it is determined, even when said average market rate differs from said first trading party interest rate data for said electronic loan transaction. 
     Another embodiment of the invention relates to a method for electronic market estimation with market-based measures on a cloud communications network wherein all steps are conducted by one or more computer processors. This method includes a number of steps, including: 
     receiving a plurality of electronic agreements from a plurality of network devices each with one or more processors for a plurality of qualified institutions on a cloud application on a cloud server network device with one or more processors on a cloud communications network, the plurality of electronic agreements including a pre-determined set of regulations for the plurality of qualified institutions to participate in establishing, conducting business and processing transactions based on market term estimates calculated on the application on the server network device the cloud communications network comprising: one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks; 
     receiving a plurality of market estimates for a pre-determined set of time periods on a cloud application on a the cloud server network device with one or more processors on a the cloud communications network from a the plurality of network devices each with one or more processors for a plurality of qualified institutions; 
     calculating in real-time on the cloud application on the cloud server network device a market term estimate for each time period in the pre-determined set of time periods to create a calculated set of market term estimates using less bandwidth and less processing cycles on the cloud communications network than on a non-cloud communications network, wherein the calculated set of market term estimates are compiled and made available in real-time at a time-1 only to the plurality of qualified institutions until one or more actual transactions have been completed between the one or more qualified institutions using one or more market term estimates from the calculated set of market term estimates; 
     storing securely with the cloud application on the cloud server network device the calculated set of market term estimates in a cloud storage object on the cloud communications network, wherein the cloud storage object is located anywhere on the one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks of the cloud communications network; 
     sending securely in real-time at time-1 from the cloud application on the cloud server network device via the cloud communications network the calculated set of market term estimates in the cloud storage object to the plurality of network devices for the plurality of qualified institutions via the cloud communications network, wherein the cloud storage object is sent securely from one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks anywhere on the cloud communications network; 
     presenting and displaying the securely sent set of compiled calculated market term estimates on computer displays of the qualified institutions for viewing and determining how to conduct business or transactions, wherein the displayed estimates improve the functioning and performance of the server network devices by providing the estimates resident for immediate use by the qualified institutions, wherein the plurality of qualified institutions have agreed to be obligated to conduct all business and make all transactions based on the calculated set of market term estimates calculated on, sent from the application on the server network device, and viewed on the computer displays, wherein the qualified institutions conduct business by requiring some institutions to offer amounts of funds at higher rates above the market term estimates, requiring other institutions to borrow amounts of funds at lower rates below the market term estimates, and requiring all institutions to transact a certain amount of funds with other institutions, with all transactions conducted and cleared electronically; 
     receiving one or more messages on the cloud application on the cloud server network device via the cloud communications network from two or more of the network devices for two or more selected ones of qualified institutions from the plurality of qualified institutions 
     receiving confirmations of one or more actual transactions that have been completed between the two or more selected ones qualified institutions using one or more market term estimates from the calculated set of market term estimates to ensure that the institutions are complying with the established market term estimates; 
     sending securely in real-time at a later time-2 the calculated set of market term estimates in the cloud storage object from the cloud application on the cloud server network device via the cloud communications network to a plurality of other server network devices and to a plurality of other target network devices each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to further conduct business based on the calculated set of market term estimates, wherein the cloud storage object is sent securely from one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks anywhere on the cloud communications network: and 
     displaying in real-time at a later time-3 from the cloud application on the cloud server network device via the cloud communications network on a plurality of graphical user interfaces on the plurality of other server network devices and the plurality of target network devices, the calculated set of market term estimates in the cloud storage object to provide further requirements of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates for each time period in the pre-determined set of time periods. 
     The method may also include: 
     calculating a set of non-market term estimates for each time period in another pre-determined set of time periods to create a calculated set of non-market term estimates, wherein the non-market estimates include estimate values of goods and services that are not bought and sold or traded in defined financial or trading markets; and 
     requiring the plurality of qualified institutions to conduct all business and make all transactions based on the calculated set of non-market term estimates calculated on the application on the server network device. 
     Typically, the qualified institution includes financial institutions, industrial institutions, utility institutions, trading institutions, data providing institutions, environmental institutions and other institutions that provide goods or services, and wherein the calculated set of market terms includes market terms and indexes for stocks, bonds, commodities, hedge funds, goods or services sold, traded or exchanged via a defined market. 
     Preferably, in this method, wherein the calculating step includes: 
     arranging the plurality of received market estimates in ascending order; eliminating a top 20% and a bottom 20% of the plurality of received market estimates; and 
     calculating a term estimate for each time period in the pre-determined set of time periods as a simple arithmetic average of remaining entries per time period or by a volume weighted average of the plurality of received market estimates and an accompanying size of the of the plurality of received market estimates. 
     Alternatively, and preferably, the calculating step includes: 
     receiving from participating qualified financial institutions bids and offers based on an overnight interest rate; 
     conducting trades between participants based on matching bids and offers that are received in order to calculate an initial a market term estimate; 
     allowing further trading to occur based on the initial market term estimate; and 
     establishing a final market term estimate as weighted average of all trades. 
     The cloud storage object may include one or more of a Representational State Transfer (REST) or Simple Object Access Protocol (SOAP), Lightweight Directory Access Protocol (LDAP) cloud storage objects, portions thereof, or combinations thereof and the sending securely steps include securely sending using a Wireless Encryption Protocol (WEP), Wireless-Wi-Fi Protected Access (WPA), Robust Security Network (RSN), Advanced Encryption Standard (AES), Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), Secure Hash Algorithm (SHA), Message Digest-5 (MD-5), Electronic Code Book (ECB), Diffie and Hellman (DH), HyperText Transport Protocol Secure, (HTTPs), Secure Sockets Layer (SSL), Transport Layer Security (TLS) security method. 
     The cloud server network device and the target network device preferably include a wireless networking interface comprising a Worldwide Interoperability for Microwave Access (WiMax) wireless networking interface with 4th generation (4G) wireless speeds for communicating with the cloud communications network, wherein the cloud communications network includes an electronic market term estimate calculation service, a cloud computing platform for the electronic market term estimate calculation service and a cloud computing infrastructure for the market term estimate calculation service and wherein the cloud application offers a cloud computing Infrastructure as a Service (IaaS), a cloud Platform as a Service (PaaS) and offers a Specific cloud software service as a Service (SaaS) including a specific cloud software service for electronic market term estimate calculations. 
     Also, the calculated set of market terms may include a London Interbank Offered Rate (LIBOR) interest rate, a Singapore Interbank Offered Rate (SIBOR) interest rate or a Hong Kong Interbank Offered Rate (HIBOR) interest rate and other equivalents, market terms and indexes for stocks, bonds, commodities, hedge funds, goods or services sold, traded or exchanged via a defined market, and the method further comprises displaying from on a graphical user interface from another cloud application on the plurality of target network devices the calculated set of market term estimates to provide information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. 
     Referring now to the Figures, the system of the invention is operated by an independent party who preferably is not one of the qualified institutions. The independent party is in charge of the network server device which is an integral component of the computer system. The inventive computer system  100  comprises one or more computers, servers, laptops, tablets, smartphones, peripherals, etc. Computer system  100  receives the market information through network  99  at its communication hardware  140 . Communication hardware  140  may be internal components of a computer or separate distributed hardware that connects the computer system to a network. Communication hardware  140  receives the transaction information, which includes amounts and interest rates from each of the qualified institutions at a predetermined time, such as by 10:30 AM. Communication hardware  140  is also configured to transmit the calculated market rates back to the qualified institutions for use. 
       FIG. 1  illustrates computer system  100 , which may comprise computer processor  110  having one or more central processing units, and non-transient memory  120  for storing data and software. Computer system  100  is configured to carry out the steps encoded in software instructions for manipulating the data received from the institutions over the lines of communication, for calculating the market estimate(s) from such data and for storing the initially received and calculated data in memory  120 . The computer system may further comprise transient memory, for example, RAM, for processing the data and instructions, and peripherals such as displays, printers, keyboards, mice, and interface devices known to those in the computer arts. Memory  120  can be an internal or external database. If desired, one or more computers and storage systems can be used to assist the server device in implementing the overall processing and operation of the computer system  100 . 
     An embodiment of computer system  100  is illustrated in  FIG. 2 . With reference now to  FIG. 2 , computer system  100  can include processor  110  associated with memory  115 , and memory  120 . Memory  120  can be configured to include software comprising instruction codes  150  (e.g., a resident executable software application), and a structured database  125  for storing data in records. In implementation, memory  120  may be implemented using multiple computers containing structured databases in order to provide sufficient database resources to the system. Memory  115  would typically be RAM or other form of cached resources that can hold executable code and related data in computer operation. Memory  120  will typically be structured to contain non-transient computer readable medium for use in operation of the system. Database  125  can include information provided by each qualified institution as to market data, i.e., amounts and interest rates and contain a database that stores such data and data structures. For example, as shown, data fields  230 - 239 ,  240 ,  242 ,  244 ,  248  are included in database  125  for each business day that the information is provided to the independent party. 
     A general purpose system may encounter problems with managing large amounts of data in a plurality of qualified institution accounts and daily data records spread over dispersed components. To handle the number of qualified institution accounts and daily data records and to assure that the accounts properly include daily entries without duplications or separation of data over disconnected storage sites, the system is advantageously configured to use a unique identification code for each qualified institution account. Furthermore, to avoid the possibility of inadvertently created duplicate records, the system could also be configured to search the system storage for the identification code to confirm that the record(s) used to maintain the data values of a particular account exists in the system and are the proper record(s) for the specific account. The system also could be configured to utilize the identification code for the transmission of the calculated market values back to the qualified institution to assure each receives the necessary information at the same time as the other institutions. The system may be configured to require the institution to also utilize the identification code when transmitting or receiving the account information or calculated market values for security purposes. 
     The information relating to the account can be entered into computer system  100  through an interface and stored in database  125 . Database  125  may be, for example, a relational, hierarchical, object-oriented, network, or correlation database. Transmitted information of amounts and interest rates may be quickly relayed to each institution&#39;s account where the information may be stored in records. The database may be localized on a single machine or distributed over multiple machines at the same or different locations. 
     Communication hardware  140  allows the computer system  100  to transmit and receive data over communication lines connected to one or more networks, which may be for example, the Internet, LAN, WAN, and MAN networks, telecommunication networks, satellite networks, and/or wireless networks. Communication hardware  140  may handle the packetizing or depacketizing of data and handle protocol requirement necessary for transmitting and receiving packets. Communication hardware  140  can transmit or receive data or information to or from computer system  100 , which it is serving. 
     Problems may be encountered by general computing systems with delays in communication of transaction and account data between various disperse system components. To handle the volume of data communicated from multiple physical localities at essentially the same time, the system should have a sufficient capacity of parallel communication channels that can both receive the expected volume of data from transactions, identify the correct hardware installations and/or components where the corresponding data records are stored, and transfer the data to the correct computer system component(s) for calculation and record updating, all in a timely manner. Transmissions to the system  100  through the communication hardware  140  may comprise addresses identifying the correct hardware installations and/or components where the corresponding data records are stored. 
     Institution accounts  230 ,  232 , and  238  can be stored in memory  120  for association with daily provided data relating to amounts and interest rates for various actual transactions, and this data can be temporarily stored in a physically or logically separate memory before being added to the institution accounts. 
     In operation, after receipt of additional daily information, database  125  is updated to reflect the additional information provided. Database may also be update with the provision of the calculate market estimates that are transmitted back to the institution so that a complete historical record is available in each account. These records may also be viewed by the qualified institution logging onto a website of the independent party with access by its identification code and an institution selected password. 
     In some embodiments, a system and method can be implemented that divide the storage and administration of account information between two entities or systems. For example, a first entity or system can be responsible for creating and administering the institution account while another entity can be responsible for storing the transmitted market values and updating of the account to include that information. An illustrative embodiment of this arrangement is shown in  FIG. 3 , wherein computer system  100  can include multiple computer systems  420 ,  430  that communicate with each other and with one or more other devices  440 ,  450  that are remote from the computer system  100  over data communications lines. The data communications can be used for sending and receiving data related to information received from the institutions and data for updating the accounts with the calculated market estimates for the various amounts and applicable interest rates for the borrowers or lenders. 
     Problems with maintaining and managing multiple diverse records at the same time may arise, such that an embodiment of the system could be configured to identify all of the accounts, storage records, and transactions as belonging to the same account regardless of where the information is being communicated or stored. This may be accomplished by an application and one or more identification table(s) that assigns the unique identification code when an account is created and stores the identification code with at least a portion of the record storage addresses in other components of the system  420 ,  430  associated with the account. Data communications could also include the address of the component of the system  420 ,  430  intended to receive the data. Backup records stored on separate systems could also be identified by their addresses and stored in the table. 
     In another embodiment, use of a single look-up table in a central, accessible, location containing storage addresses could allow coordination of data storage across all components  420 ,  430 ,  440 ,  450  of a system by identifying the correct information with the correct account, flagging a record as being updated by a transaction, and locking access to other associate records by other system components until updating of a record is complete. 
     Computer system  100  may comprise separate computer(s)  420  for opening and administering the institution accounts with the data provided thereby, and separate computer(s)  430  for updating the institution accounts with the calculated market estimates. Computer(s)  420  and computer(s)  430  can communicate over a data line  431  (e.g., wired, wireless, WAN, LAN, Internet, etc.). These computers may also be in communication with one or more database servers  440 ,  450  over data communication lines  441 ,  451  for storage and retrieval of historical or backup data for the institution accounts. 
     Computer system  100  may further comprise communication hardware  140  that could be integral with one of the computers e.g.,  420 , or hardware separate from any particular computer, that connects computer system  100  over one or more communication line(s)  461  to remote devices  460 . Remote devices  460  can include a user interface for the institutions. Computer system  100  is configured to send information to the remote device  460  to display market estimates to the institutions and to receive information regarding the transmittal of institutional data so that the market estimates may be calculated by the server device. 
       FIG. 4  is a block diagram  60  illustrating an exemplary cloud computing network  18 . The cloud computing network  18  is also referred to as a “cloud communications network”  18 . However, the present invention is not limited to this cloud computing model and other cloud computing models can also be used to practice the invention. The exemplary cloud communications network includes both wired and/or wireless components of public and private networks. 
     In one embodiment, the cloud computing network  18  includes a cloud communications network  18  comprising plural different cloud component networks  72 ,  74 ,  76 ,  78 . “Cloud computing” is a model for enabling, on-demand network access to a shared pool of configurable computing resources (e.g., public and private networks, servers, storage, applications, and services) that are shared, rapidly provisioned and released with minimal management effort or service provider interaction. 
     This exemplary cloud computing model for electronic information retrieval promotes availability for shared resources and comprises: (1) cloud computing essential characteristics; (2) cloud computing service models; and (3) cloud computing deployment models. However, the present invention is not limited to this cloud computing model and other cloud computing models can also be used to practice the invention. 
     Exemplary cloud computing essential characteristics include the following: 
     On-demand electronic market estimate calculation computing services. 
     Electronic market estimators can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each network server on the cloud communications network  18 . 
     Broadband network access. Electronic market estimators capabilities are available over plural broadband communications networks and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, smart phones, tablet computers, laptops, PDAs, etc.). The broadband network access includes high speed network access such as 3G and/or 4G wireless and/or wired and broadband and/or ultra-broad band (e.g., WiMAX, etc.) network access. 
     Resource pooling. Electronic market estimators computing resources are pooled to serve multiple requesters using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to electronic market estimator calculation demand. There is location independence in that a requester of electronic content has no control and/or knowledge over the exact location of the provided by the electronic market estimator calculation resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or data center). Examples of pooled resources include storage, processing, memory, network bandwidth, virtual server network device and virtual target network devices. 
     Rapid elasticity. Capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale for electronic market estimation calculation. To the electronic market estimator calculation services, the electronic market estimator calculation capabilities available for provisioning appear to be unlimited and can be used in any quantity at any time. 
     Measured Services. Cloud computing systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of electronic market estimators service (e.g., calculating, processing, bandwidth, custom electronic market estimators applications, etc.). Electronic market estimation calculation usage is monitored, controlled, and reported providing transparency for both the electronic market estimator calculations and the electronic market estimation information providers of the utilized electronic market estimators service. 
     Exemplary cloud computing service models illustrated in  FIG. 4  follow. These include: Cloud Computing Software Applications  62  for an Electronic Market Estimation Calculation Service (CCSA  64 ). The capability to use the provider&#39;s applications running on a cloud infrastructure  66 . The cloud computing applications  62 , are accessible from the server network device from various client devices through a thin client interface such as a web browser, etc. The user does not manage or control the underlying cloud infrastructure  66  including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Cloud Computing Infrastructure  66  for the Electronic Market Estimation Calculation Service (CCI  68 ). The capability provided to the user is to provision processing, storage and retrieval, networks  18 ,  72 ,  74 ,  76 ,  78  and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The user does not manage or control the underlying cloud infrastructure  66  but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls, etc.). 
     Cloud Computing Platform  70  for the Electronic Market Estimation Calculation Service (CCP  71 ). The capability provided to the user to deploy onto the cloud infrastructure  66  created or acquired applications created using programming languages and tools supported servers, etc. The user not manage or control the underlying cloud infrastructure  66  including network, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Exemplary cloud computing deployment models are provided below. These include: Private cloud network  72 . The cloud network infrastructure is operated solely for electronic market estimation calculations. It may be managed by the electronic content retrieval or a third party and may exist on premise or off premise. 
     Community cloud network  74 . The cloud network infrastructure is shared by several different organizations and supports a specific electronic market estimation content community that has shared concerns (e.g., mission, security requirements, policy, compliance considerations, etc.). It may be managed by the different organizations or a third party and may exist on premise or off premise. 
     Public cloud network  76 . The cloud network infrastructure such as the Internet, PSTN, SATV, CATV, Internet TV, etc. is made available to the general public or a large industry group and is owned by one or more organizations selling cloud services. 
     Hybrid cloud network  78 . The cloud network infrastructure  66  is a composition of two and/or more cloud networks  18  (e.g., private  72 , community  74 , and/or public  76 , etc.) and/or other types of public and/or private networks (e.g., intranets, etc.) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds, etc.). 
     The foregoing embodiments of the present invention are not limited to these particular characteristics, service models and deployment models, and more, fewer or other characteristics, service models or deployment models can also be used to practice the invention. 
     Cloud software  64  for electronic market estimation takes full advantage of the cloud paradigm by being service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability for electronic content retrieval. However, cloud software services  64  can include various states. 
     Cloud storage of desired electronic content on a cloud computing network includes agility, scalability, elasticity and multi-tenancy. Although a storage foundation may be comprised of block storage or file storage such as that exists on conventional networks, cloud storage is typically exposed to requesters of desired electronic content as cloud objects. 
     In one exemplary embodiment, the cloud application offers cloud services for electronic market estimation calculations. The application offers the cloud computing Infrastructure  66 ,  68  as a Service  62  (IaaS), including a cloud software infrastructure service  62 , the cloud Platform  70 ,  71  as a Service  62  (PaaS) including a cloud software platform service  62  and/or offers Specific cloud software services as a Service  62  (SaaS) including a specific cloud software service  62  for electronic market estimation. The IaaS, PaaS and SaaS include one or more of cloud services  62  comprising networking, storage, server network device, virtualization, operating system, middleware, run-time, data and/or application services, or plural combinations thereof, on the cloud communications network  18 . 
       FIG. 5  is a block diagram  80  illustrating an exemplary cloud storage object  82 . The cloud storage object  82  preferably includes an envelope portion  84 , with a header portion  86 , and a body portion  88 . The envelope portion  84  uses unique namespace Uniform Resource Identifiers (URIs) and/or Uniform Resource Names (URNs), and/or Uniform Resource Locators (URLs) unique across the cloud communications network  18  to uniquely specify, location and version information and encoding rules used by the cloud storage object  82  across the whole cloud communications network  18 . For more information, see IETF RFC-3305, Uniform Resource Identifiers (URIs), URLs, and Uniform Resource Names (URNs), the contents of which are incorporated by reference. 
     The envelope portion  84  of the cloud storage object  82  is followed by a header portion  86 . The header portion  86  includes extended information about the cloud storage objects such as authorization and/or transaction information, etc. The body portion  88  includes methods  90  (i.e., a sequence of instructions, etc.) for using embedded application-specific data in data elements  92 . The body portion  88  typically includes only one portion of plural portions of application-specific data  92  and independent data  94  so the cloud storage object  82  can provide distributed, redundant fault tolerant, security and privacy features described herein. 
     Cloud storage objects  82  have proven experimentally to be a highly scalable, available and reliable layer of abstraction that also minimizes the limitations of common file systems. Cloud storage objects  82  also provide low latency and low storage and transmission costs. 
     Cloud storage objects  82  are comprised of many distributed resources, but function as a single storage object, are highly fault tolerant through redundancy and provide distribution of desired electronic content across public communication networks  76 , and one or more private networks  72 , community networks  74  and hybrid networks  78  of the cloud communications network  18 . Cloud storage objects  82  are also highly durable because of creation of copies of portions of desired electronic content across such networks  72 ,  74 ,  76 ,  78  of the cloud communications network  18 . Cloud storage objects  82  includes one or more portions of desired electronic content and can be stored on any of the  72 ,  74 ,  76 ,  78  networks of the cloud communications network  18 . Cloud storage objects  82  are transparent to a requester of desired electronic content and are managed by cloud applications. 
     In one embodiment, cloud storage objects  82  are configurable arbitrary objects with a size up to hundreds of terabytes, each accompanied by with a few kilobytes of metadata. Cloud objects are organized into and identified by a unique identifier unique across the whole cloud communications network  18 . These cloud storage objects  82  present a single unified namespace or object-space and manages desired electronic content by user or administrator-defined policies storage and retrieval policies. Cloud storage objects includes Representational state transfer (REST), Simple Object Access Protocol (SOAP), Lightweight Directory Access Protocol (LDAP) and/or Application Programming Interface (API) objects and/or other types of cloud storage objects. 
     REST is a protocol specification that characterizes and constrains macro-interactions storage objects of the four components of a cloud communications network  18 , namely origin servers, gateways, proxies and clients, without imposing limitations on the individual participants. 
     SOAP is a protocol specification for exchanging structured information in the implementation of cloud services with storage objects. SOAP has at least three major characteristics: (1) Extensibility (including security/encryption, routing, etc.); (2) Neutrality (SOAP can be used over any transport protocol such as HTTP, SMTP or even TCP, etc.), and (3) Independence (SOAP allows for almost any programming model to be used, etc.). 
     LDAP is a software protocol for enabling storage and retrieval of electronic content and other resources such as files and devices on the cloud communications network  18 . LDAP is a “lightweight” version of Directory Access Protocol (DAP), which is part of X.500, a standard for directory services in a network. LDAP may be used with X.509 security and other security methods for secure storage and retrieval. X.509 is public key digital certificate standard developed as part of the X.500 directory specification. X.509 is used for secure management and distribution of digitally signed certificates across networks. 
     An API is a particular set of rules and specifications that software programs can follow to communicate with each other. It serves as an interface between different software programs and facilitates their interaction. 
     The computer systems of the present invention thus include components and operating characteristics that improve the functioning of a computer in an environment of attempting to accurately and expeditiously calculate and disseminate market interest rate estimates in an improved and unexpected manner compared to the prior art. This is much more than simply using a computer to make an existing process run more efficiently. 
     In one embodiment, as further disclosed in U.S. application Ser. No. 13/570,930, the invention provides an electronic market estimation with market-based measures. Plural market estimates are received for a pre-determined set of time periods on an application server network device with one or more processor on a communications network from plural network devices each with one or more processor from plural qualified institutions. The plural qualified institutions have agreed to a pre-determined set of regulations to participate in establishing, conducting business and processing transactions based on calculated market term estimates. The application on the server network device calculates in real-time a market term estimate for each time period in the pre-determined set of time periods to create a calculated set of market term estimates. Next, the application on the server network device securely sends the calculated set of market term estimates to the plural network devices for the plural qualified institutions via the communications network. The qualified institutions are required to conduct business and make transactions based on the calculated set of market term estimates. The calculated set of market term estimates are securely sent from the application on the server network device via the communications network to plural other network devices each with one or more processors to provide one or more electronic markets or trading markets information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. The application on the server network device provides a secure data feed via the communications network with the calculated set of market term estimates for displaying the calculated market term estimates on other server network devices. This allows the calculated set of market term estimates to be securely sent from the application on the server network device via the communications network to plural target network devices each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to conduct business based and process transactions on the calculated set of market term estimates. 
     As an example to illustrate the foregoing method, consider Banks A to J that represent participating qualified financial institutions who set (i.e., provide rates and size) an overnight interest rate. Such banks may submit data as an interest rate alone or as an interest rate and size as a market estimate. Table 2 illustrates exemplary market estimates received from the exemplary Banks A to J. 
     
       
         
           
               
               
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 S. 
                   
                 Overnight Rate 
                 Overnight 
               
               
                   
                 No 
                 Bank Name 
                 Submissions 
                 Night Size ($) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 1 
                 Bank A 
                 0.050% 
                 $100,000  
               
               
                   
                 2 
                 Bank B 
                 0.067% 
                 $50,000 
               
               
                   
                 3 
                 Bank C 
                 0.090% 
                 $150,000  
               
               
                   
                 4 
                 Bank D 
                 0.088% 
                 $25,000 
               
               
                   
                 5 
                 Bank E 
                 0.068% 
                 $50,000 
               
               
                   
                 6 
                 Bank F 
                 0.050% 
                 $250,000  
               
               
                   
                 7 
                 Bank G 
                 0.045% 
                 $65,000 
               
               
                   
                 8 
                 Bank H 
                 0.072% 
                 $55,000 
               
               
                   
                 9 
                 Bank I 
                 0.050% 
                 $75,000 
               
               
                   
                 10 
                 Bank J 
                 0.062% 
                 $75,000 
               
               
                   
                   
               
            
           
         
       
     
     The application on the server network device calculates in real-time (i.e., in about a few seconds or less, etc.) a market term estimate for each time period in the pre-determined set of time periods to create a calculated set of market term estimates. This includes arranging the plural market estimates for each period are arranged in ascending order. The top 20% of the received entries and bottom 20% of the received market-based estimates are eliminated. A term estimate for each period is then calculated as a simple arithmetic average of a remaining entries per period. Table 3 illustrates such an exemplary calculation using the received market term estimates illustrated in Table 2. As shown in Table 3, the received market estimates from Table 2 are arranged in ascending order. The top and bottom 20% are eliminated. The averages of the remaining received market estimates are used to calculate an arithmetic average of the remaining received market estimates. 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                   
                 Overnight 
                   
                   
                   
               
               
                   
                   
                 Rates (in 
                   
                   
                   
               
               
                   
                 Bank 
                 ascending 
                 Overnight 
                   
                   
               
               
                   
                 Name 
                 order) 
                 Size ($) 
                 Action 
                   
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 7 
                 Bank G 
                 0.045% 
                  $65,000 
                 Eliminated 
                   
               
               
                 1 
                 Bank A 
                 0.050% 
                 $100,000 
                 Eliminated 
                   
               
               
                 6 
                 Bank F 
                 0.050% 
                 $250,000 
                   
                 Rate Based on 
               
               
                 9 
                 Bank I 
                 0.050% 
                  $75,000 
                   
                 Simple Average: 
               
               
                 10 
                 Bank J 
                 0.062% 
                  $75,000 
                   
                 0.0614% Rate 
               
               
                 2 
                 Bank B 
                 0.067% 
                  $50,000 
                  {close oversize brace}  
                 based on Volume 
               
               
                 5 
                 Bank E 
                 0.068% 
                  $50,000 
                   
                 Weighted 
               
               
                 8 
                 Bank H 
                 0.072% 
                  $55,000 
                   
                 Average: 0.0569% 
               
               
                 4 
                 Bank D 
                 0.088% 
                  $25,000 
                 Eliminated 
                   
               
               
                 3 
                 Bank C 
                 0.090% 
                 $150,000 
                 Eliminated 
               
               
                   
               
            
           
         
       
     
     As illustrated in Table 3, an overnight interest rate is calculated using the simple average of overnight rates. The calculated value is 0.0614%. 
     As an alternate method, a term estimate for each period may be arrived as a volume weighted average of received entries and its accompanying size. However, the present invention is not limited to such calculations and other calculations can be used to practice the invention. The alternative method with volume weighted average using size and rates is 0.0569% for the same entries in Table 3. 
     In another embodiment, the calculated set of market term estimates includes LIBOR, SIBOR and/or HIBOR estimates. In another embodiment, the created set of market term estimates includes estimates such as interest rates, indices, buy and or sell prices for stocks, bonds, options, commodities, hedge funds and/or any other goods and/or services sold, traded or exchanged via a defined market. The defined market may be regulated or unregulated markets. The calculated set of market term estimates can be used on a regulated trading exchange or an unregulated trading exchange. Non-market estimates can also be used to create the set of market term estimates. 
     The application on the server network device sends the calculated set of market term estimates to the plural network devices for the plural qualified institutions via the communications network. The qualified institutions are required to conduct business and make transactions based on the calculated set of market term estimates. 
     Once the calculated set of market term estimates has been established, the qualified institutions must make actual transactions using the calculated set of market term estimates. This is illustrated with an exemplary supply-demand curve in Table 4. The example in Table 4 assumes the use of the simple average method discussed previously although it is also operable with the alternate method of a term estimate for each period arrived as a volume weighted average of received entries and its accompanying size. 
     
       
         
           
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Overnight 
                 Offered Amount 
                 Borrowed Amount 
               
               
                 Rate 
                 ($) 
                 ($) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 0.081% 
                 $250,000 
                   
               
               
                 0.076% 
                 $200,000 
                   
               
               
                 0.071% 
                 $150,000 
                   
               
               
                 0.066% 
                 $100,000 
                   
               
               
                 0.061% 
                 Equilibrium  
                   
               
               
                   
                 Overnight Rate 
                   
               
               
                 0.056% 
                   
                 $100,000 
               
               
                 0.051% 
                   
                 $150,000 
               
               
                 0.046% 
                   
                 $200,000 
               
               
                 0.041% 
                   
                 $250,000 
               
               
                   
               
            
           
         
       
     
     As illustrated in Table 4, members of the group of qualified financial institutions (i.e., Banks A through J) must be willing to offer greater and greater amounts of funds above equilibrium rate. Similarly, members of the group of qualified institutions must be willing to borrow greater and greater amounts for successive rates below the equilibrium rates. All qualified institutions members have to be involved in fund transactions (i.e., borrow or lend to other members and others) that satisfy the above supply-demand curve in Table 4. Such transactions are done electronically and are cleared electronically to ensure the qualified institutions comply with the established market term estimates. Once a given level of transactions adds credibility to an established equilibrium rate illustrated in Table 4, it is published widely. 
     The calculated set of market term estimates are securely sent from the application on the server network device to plural other network devices each with one or more processors to provide one or more electronic markets or trading markets information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. The application on the server network device thus provides a secure data feed via the communications network with the calculated set of market term estimates for displaying the calculated market term estimates on other server network devices. 
     The calculated set of market term estimates are securely sent from the application on the server network device to plural target network devices each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to conduct business based on the calculated set of market term estimates. These calculated set of market term estimates are displayed from on a graphical user interface from another application on the plural target network devices to provide information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. 
     A preferred embodiment is shown in  FIGS. 6A, 6B and 6C , wherein the systems and method of the present invention provide further improvements to the way that current market estimates are made using market-based measures. 
     In this embodiment, participants representing qualified financial institutions, including banks, submit a series of offers (rates and size) and bids (rate and size) to build an offer curve and a bid curve according to a set or pre-determined rules. Such predetermined rules may include but not limited to rules around the offer size and bid size, incremental tiers on the offer curve and the bid curve etc. The institutions typically submit market estimates for a set of loans with different maturities. 
     When the participants have completed building individual offer and bid curves, an electronic matching engine matches bids and offers according to pre-set trading rules, resulting in a series of trades occurring. Each consummated trade provides market-based information on the price and quantity that was traded. An initial market-based rate is established based on the simple average; volume weighted average or other method and published to the participants. 
       FIGS. 6A, 6B and 6C  combined are a flow diagram illustrating a method  96  for electronic market estimation with market-based measures. In  FIG. 6A  at step  98 , plural market estimates are received at a pre-determined time on an application server network device with one or more processor on a communications network from plural network devices each with one or more processor from plural qualified institutions. The plural qualified institutions have agreed to a pre-determined set of regulations to participate in establishing, conducting business and processing transactions based on calculated market term estimates. At Step  100 , the application on the server network device calculates in real-time (i.e., within 1 to 2 minutes) an initial market term estimate to create a calculated set of market term estimates. At Step  101 , the server network device securely sends the calculated set of market term estimates to the plural network devices for the plural qualified institutions via the communications network. 
       FIG. 6B  illustrates the next three steps. Once the initial market rate has been established, participants will be able to view the initial rate as well as the market offer and bid curves that were submitted by all participants in Step  102 . Participants are then allowed to a) add additional bids and offers to the existing submissions or b) modify existing submissions or c) a combination of a) and b) resulting in further trades being consummated. The final market-based rate is established based on the simple average or volume weighted average of the trades in Steps  103  and  104 . 
     Examples of such calculations are provided in Tables 5, 6 and 7. To illustrate these Steps of Method  96 , consider Banks 1 to 3 that represent participating qualified financial institutions who set (i.e., provide rates and size) an overnight interest rate. 
                     TABLE 5                  Member Banks                                 Minimum Required Bid           Bank Assets ($)   and Offer Quantities               Bank 1   10B   $1 Million       Bank 2   15B   $2 Million       Bank 3   25B   $5 Million                    
Such banks may submit data as an interest rate alone or as an interest rate and size as a market estimate.
 
     Table 6 illustrates exemplary market estimates  13  received at Step  98  from the exemplary Banks 1 to 3. Each of the participant Banks separately submit their bid and offer curves as shown in the table, where the Bid and Offer Quantities are in Millions, with Bid and Offer rates in basis points. 
     
       
         
           
               
             
               
                 TABLE 6 
               
               
                   
               
               
                 Bid and Offer Quantities 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                 Bid Qty 
                 Bid Rate 
                 Offer Rate 
                 Offer Qty 
               
               
                   
                   
                 (in 
                 (basis 
                 (basis 
                 (in 
               
               
                   
                 Bank 1 
                 millions) 
                 points) 
                 points) 
                 millions) 
               
               
                   
                   
               
               
                   
                 AMERIBOR 
                 1 
                 28 
                 30 
                 1 
               
               
                   
                 Rate 
                   
                   
                   
                   
               
               
                   
                 Tier 1 
                 2 
                 26 
                 32 
                 2 
               
               
                   
                 Tier 2 
                 4 
                 24 
                 34 
                 4 
               
               
                   
                 Tier 3 
                 8 
                 22 
                 36 
                 8 
               
               
                   
                   
               
               
                   
                 Bank 2 
                 Bid Qty 
                 Bid Rate 
                 Offer Rate 
                 Offer Qty 
               
               
                   
                   
               
               
                   
                 AMERIBOR 
                 2 
                 32 
                 33 
                 2 
               
               
                   
                 Rate 
                   
                   
                   
                   
               
               
                   
                 Tier 1 
                 4 
                 30 
                 35 
                 4 
               
               
                   
                 Tier 2 
                 8 
                 28 
                 37 
                 8 
               
               
                   
                 Tier 3 
                 16 
                 26 
                 39 
                 16 
               
               
                   
                   
               
               
                   
                 Bank 3 
                 Bid Qty 
                 Bid Rate 
                 Offer Rate 
                 Offer Qty 
               
               
                   
                   
               
               
                   
                 AMERIBOR 
                 5 
                 29 
                 31 
                 5 
               
               
                   
                 Rate 
                   
                   
                   
                   
               
               
                   
                 Tier 1 
                 10 
                 27 
                 33 
                 10 
               
               
                   
                 Tier 2 
                 20 
                 25 
                 35 
                 20 
               
               
                   
                 Tier 3 
                 40 
                 23 
                 37 
                 40 
               
               
                   
                   
               
            
           
         
       
     
     The processing steps include the following: 
     1. Bank 1 initial submissions are presented as shown above. 
     2. Bank 1 AMERIBOR bid rate is 28 basis points and bid quantity of $1 million. 
     3. Bank 1 AMERIBOR offer rate is 30 basis points and offer quantity is $1 million. 
     4. Note that AMERIBOR-Bid and AMERIBOR-Offer are 2 basis points apart. 
     5. Based on AMERIBOR-Bid and AMERIBOR Offer, a ladder of offers and bids are constructed. 
     6. Three tiers are constructed that are 2 basis points apart (bid rate decrease by 2 bps and offer rate increase by 2 bps). 
     7. Across each tier, the offer quantity doubles. 
     At Step  100  of  FIG. 6 , the application on the server network device calculates in real-time (i.e., in about a few seconds or less, etc.) a market term estimate for each time period in the predetermined set of time periods to create a calculated set of market term estimates. The results are shown in Table 7. 
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Calculated Market Term Estimates 
               
               
                 Stage 1: AMERIBOR Offer and Bid Curve Submission 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Bid  
                 Bid  
                 Offer  
                 Offer  
                 Bank 
               
               
                   
                 Bank 
                 Qty 
                 rate 
                 rate 
                 Qty 
                 Offer 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 39 
                 16 
                 Bank 2 
               
               
                   
                   
                   
                   
                 37 
                 8 
                 Bank 2 
               
               
                   
                   
                   
                   
                 37 
                 40 
                 Bank 3 
               
               
                   
                   
                   
                   
                 36 
                 8 
                 Bank 1 
               
               
                   
                   
                   
                   
                 35 
                 4 
                 Bank 2 
               
               
                   
                   
                   
                   
                 35 
                 20 
                 Bank 3 
               
               
                   
                   
                   
                   
                 34 
                 4 
                 Bank 1 
               
               
                   
                   
                   
                   
                 33 
                 2 
                 Bank 2 
               
               
                   
                   
                   
                   
                 33 
                 10 
                 Bank 3 
               
               
                   
                 Bank 2 
                 2 
                 32 
                 32 
                 2 
                 Bank 1 
               
               
                   
                 Bank 2 
                 4 
                 30 
                 31 
                 5 
                 Bank 3 
               
               
                   
                 Bank 3 
                 5 
                 29 
                 30 
                 1 
                 Bank 1 
               
               
                   
                 Bank 1 
                 1 
                 28 
                   
                   
                   
               
               
                   
                 Bank 2 
                 8 
                 28 
                   
                   
                   
               
               
                   
                 Bank 3 
                 10 
                 27 
                   
                   
                   
               
               
                   
                 Bank 1 
                 2 
                 26 
                   
                   
                   
               
               
                   
                 Bank 2 
                 16 
                 26 
                   
                   
                   
               
               
                   
                 Bank 3 
                 20 
                 25 
                   
                   
                   
               
               
                   
                 Bank 1 
                 4 
                 24 
                   
                   
                   
               
               
                   
                 Bank 3 
                 40 
                 23 
                   
                   
                   
               
               
                   
                 Bank 1 
                 8 
                 22 
               
               
                   
                   
               
            
           
         
       
     
     Next, trade matching and initial pricing are determined: 
     In Stage 1, Bank 2&#39;s two million bid at 32 basis points matches with bank 1 (1 million offer at 30) and bank 3, (5 million offered at 31). This results in the following trades: 
     Trade 1: 1 million at 30 basis points; Buyer is Bank 2 and seller is Bank 1 
     Trade 2: 1 million at 31 basis points; buyer is Bank 2 and Seller is Bank 3 
     The Initial AMERIBOR Rate is the weighted average of the above trades, or 30.5 bps. 
     Table 8 shows the remaining bids and offers after the initial matches have occurred at the initially established AMERIBOR rate of 30.5: 
     
       
         
           
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                 Remaining Bids and Offers Prior to Auction 
               
               
                 Stage 1: AMERIBOR Offer and Bid Curve Submission 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Bid  
                 Bid  
                 Offer 
                 Offer  
                 Bank 
               
               
                   
                 Bank 
                 Qty 
                 rate 
                 rate 
                 Qty 
                 Offer 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 39 
                 16 
                 Bank 2 
               
               
                   
                   
                   
                   
                 37 
                 8 
                 Bank 2 
               
               
                   
                   
                   
                   
                 37 
                 40 
                 Bank 3 
               
               
                   
                   
                   
                   
                 36 
                 8 
                 Bank 1 
               
               
                   
                   
                   
                   
                 35 
                 4 
                 Bank 2 
               
               
                   
                   
                   
                   
                 35 
                 20 
                 Bank 3 
               
               
                   
                   
                   
                   
                 34 
                 4 
                 Bank 1 
               
               
                   
                   
                   
                   
                 33 
                 2 
                 Bank 2 
               
               
                   
                   
                   
                   
                 33 
                 10 
                 Bank 3 
               
               
                   
                   
                   
                   
                 32 
                 2 
                 Bank 1 
               
               
                   
                   
                   
                   
                 31 
                 4 
                 Bank 3 
               
               
                   
                 Bank 2  
                 4 
                 30 
                   
                   
                   
               
               
                   
                 Bank 3  
                 5 
                 29 
                   
                   
                   
               
               
                   
                 Bank 1  
                 1 
                 28 
                   
                   
                   
               
               
                   
                 Bank 2  
                 8 
                 28 
                   
                   
                   
               
               
                   
                 Bank 3  
                 10 
                 27 
                   
                   
                   
               
               
                   
                 Bank 1  
                 2 
                 26 
                   
                   
                   
               
               
                   
                 Bank 2  
                 16 
                 26 
                   
                   
                   
               
               
                   
                 Bank 3  
                 20 
                 25 
                   
                   
                   
               
               
                   
                 Bank 1  
                 4 
                 24 
                   
                   
                   
               
               
                   
                 Bank 3  
                 40 
                 23 
                   
                   
                   
               
               
                   
                 Bank 1  
                 8 
                 22 
               
               
                   
                   
               
            
           
         
       
     
     At this point, further trading is allowed to occur for a finite time of, e.g., 30 minutes to 2 hours. Bank 2 puts in a new Bid for 4 million at 31 basis points which results in a trade with Bank 3, for 4 million traded at the 31 basis points bid. This in turn results in a final AMERIBOR rate that is established as weighted average of trades in initial and price confirmation period. These calculations result in a final AMERIBOR rate of 30.833 basis points. 
     As an alternate method, a term estimate for each period may be arrived as a volume weighted average of received entries and its accompanying size. The established rate based on one of the above procedures is then available and is provided back to the banks for further trading. 
     Another embodiment of the above methodology could include the communication of the market-based estimate from the above steps to a separate market where the established benchmark estimates could serve as a market information function. 
     In  FIG. 6C  at Step  105 , the calculated set of market term estimates are securely sent from the application on the server network device via the communications network to plural other network devices each with one or more processors to provide one or more electronic markets or trading markets information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. At Step  106 , the application on the server network device provides a secure data feed via the communications network with the calculated set of market term estimates for displaying the calculated market term estimates on other server network devices. At Step  107 , the calculated set of market term estimates are securely sent from the application on the server network device via the communications network to plural target network devices each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to conduct business based and process transactions on the calculated set of market term estimates. 
     In an exemplary embodiment, in  FIG. 6A  at Step  98 , plural market estimates are received for a pre-determined set of time periods on an application on a server network device with one or more processor on a communications network  18  from plural network devices each with one or more processors for plural qualified institutions. The plural qualified institutions have agreed to a pre-determined set of regulations to participate in establishing and conducting business based on calculated market term estimates. 
     The qualified institutions include, but are not limited to, financial institutions (e.g., banks, etc.), industrial institutions (e.g., public and private companies in a specific industry (e.g., automobile, housing, manufacturing, food processing, etc.), utility institutions (e.g., electric, natural gas, heating oil, etc.) trading institutions (e.g., stock, bonds, commodities, options, etc.) data providing institutions (e.g., news services Thomson Reuters New Services, Dow Jones News Service, social networking sites, other trading news services, financial news services etc.), environmental institutions and other institutions that provide any type of goods and/or services. The qualified institutions may be public and/or private qualified institutions. 
     In another embodiment of the invention, the calculated set of market term estimates includes LIBOR, SIBOR and/or HIBOR estimates. In another embodiment, the created set of market term estimates includes estimates such as interest rates, indices, buy and or sell prices for stocks, bonds, options, commodities, hedge funds and/or any other goods and/or services sold, traded or exchanged via a defined market. The defined market may regulated or unregulated markets. The calculated set of market term estimates can be used on a regulated trading exchange or an unregulated trading exchange. Non-market estimates can also be used to create the set of market term estimates. 
     At Step  105 , the application on the server network device sends the calculated set of market term estimates to the plural network devices for the plural qualified institutions via the communications network  18 . The qualified institutions are required to conduct business and make transactions based on the calculated set of market term estimates. 
     Once the calculated set of market term estimates has been established, the qualified institutions must make actual transactions using the calculated set of market term estimates. Once a given level of transactions adds credibility to an established equilibrium rate, it is published widely as is illustrated by Steps  105 - 107 . 
     In  FIG. 6C  Step  105 , the calculated set of market term estimates are securely sent from the application on the server network device to plural other network devices each with one or more processors to provide one or more electronic markets or trading markets information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. 
     At Step  106 , the application on the server network device provides a secure data feed via the communications network  18  with the calculated set of market term estimates for displaying the calculated market term estimates on other server network devices. 
     At Step  107 , the calculated set of market term estimates are securely sent from the application on the server network device to plural target network devices each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to conduct business based on the calculated set of market term estimates. 
     The calculated set of market term estimates are displayed from on a graphical user interface from another application on the plural target network devices to provide information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. 
     The transactions at Steps  100 ,  105  and  107  may be done through an electronic trading platform similar to a commodities exchange (e.g., Chicago Board of Trade (CBOT). Chicago Mercantile Exchange (CME), etc.), stock exchange, an options exchange, a Designated Contract Market (DCM), etc. The transactions may be completed through a regulated (Security and Exchange Commission (SEC), Commodities Futures Trading Commission (CFTC), etc. or non-regulated entity. The same thing applies to equivalent steps of Method  110 . 
     The actual transactions based on these steps can also cleared by a regulated clearing entity similar to a Designated Clearing Organization (DCO) under the CFTC or a non-regulated clearing entity. 
     In another embodiment, the steps of Method  96  can be practiced manually. In such an embodiment, qualified institutions can be polled manually (e.g., via telephone calls, facsimile, etc.), and transactions be conducted over-the counter. Based on these transactions the calculated set of market term estimates completed with a calculator, in a spreadsheet, etc. and the results published in a non-electronic format (e.g., published in newspaper, returned by facsimile, etc.). Therefore, the present invention can be practiced directly as a new business method as well. 
     Electronic Market Estimation with Market-Based Measures with Cloud Computing 
       FIGS. 7A, 7B and 7C  are flow diagrams illustrating a Method  110  for electronic market estimation with market-based measures on a cloud communications network. In  FIG. 7A  at Step  112 , plural market estimates are received for a pre-determined set of time periods on a cloud application on a cloud server network device with one or more processor on a cloud communications network from plural network devices each with one or more processors for a plural qualified institutions. The plural qualified institutions have agreed to a pre-determined set of regulations to participate in establishing, conducting business and processing transactions based on calculated market term estimates, the cloud communications network comprising: one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks, At Step  114 , the cloud application on the cloud server network device calculates in real-time a market term estimate for each time period in the pre-determined set of time periods to create a calculated set of market term estimates using less bandwidth and less processing cycles on the cloud communications network than on a non-cloud communications network. 
     In  FIG. 7B  at Step  115 , members are then allowed to a) add additional bids and offers to the existing submissions or b) modify existing submissions or c) a combination of a) and b) resulting in further trades being consummated. These trades are then reported back to the independent party or agency so that a more accurate and final calculated set of market term estimates are determined. These estimates are securely stored in the cloud and are communicated back to the member institutions in Steps  116  and  118 . At Step  116 , the cloud application on the cloud server network device securely stores the calculated set of initial market term estimates in a cloud storage object on the cloud communications network. The cloud storage object is located anywhere on the one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks of the cloud communications network. At Step  118 , the cloud application on the cloud server network device securely sends via the cloud communications network the calculated set of market term estimates in the cloud storage object to the plural network devices for the plural qualified institutions via the cloud communications network. 
     The qualified institutions are required to conduct business and make transactions based on the calculated set of market term estimates. The cloud storage object is sent securely from one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks anywhere on the cloud communications network, 
     In  FIG. 7C  at Step  120 , the calculated set of market term estimates in the cloud storage object is securely sent from the cloud application on the cloud server network device via the cloud communications network to plural target network devices each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to conduct business based on the calculated set of market term estimates. The cloud storage object is sent securely from one or more public communication networks, one or more private networks, one or more community networks and one or more hybrid networks anywhere on the cloud communications network. 
     In an exemplary embodiment, in  FIG. 7A  at Step  112 , plural market estimates are received for a pre-determined set of time periods on a cloud application on a cloud server network device with one or more processors on a cloud communications network  18  from plural network devices each with one or more processors for a plural qualified institutions. The plural qualified institutions have agreed to a pre-determined set of regulations to participate in establishing, conducting business and processing transactions based on calculated market term estimates, the cloud communications network  18  comprising: one or more public communication networks  76 , one or more private networks  72 , one or more community networks  74  and one or more hybrid networks  78 . 
     At Step  114 , the cloud application on the cloud server network device calculates in real-time a market term estimate for each time period in the pre-determined set of time periods to create a calculated set of market term estimates using less bandwidth and less processing cycles on the cloud communications network  18  than on a non-cloud communications network. 
     In  FIG. 7B  at Step  115 , members are then allowed to a) add additional bids and offers to the existing submissions or b) modify existing submissions or c) a combination of a) and b) resulting in further trades being consummated. These trades are then reported back to the independent party so that a more accurate and final calculated set of market term estimates are determined. These estimates are securely stored in the cloud and are communicated back to the member institutions in Steps  116  and  118 . The qualified institutions are required to conduct business and make transactions based on the final calculated set of market term estimates. The cloud storage object is sent securely from one or more public communication networks  76 , one or more private networks  72 , one or more community networks  74  and one or more hybrid networks  78  anywhere on the cloud communications network  18 . 
     At Step  116 , the cloud application on the cloud server network device securely stores the calculated set of market term estimates in a cloud storage object  82  on the cloud communications network  18 . The cloud storage object  82 , and/or portions thereof is located anywhere on the one or more public communication networks  76 , one or more private networks  72 , one or more community networks  74  and one or more hybrid networks  78  of the cloud communications network  18 . 
     At Step  118 , the cloud application  30 ′ on the cloud server network device  20  securely sends via the cloud communications network  18  the calculated set of market term estimates  15  in the cloud storage object  82  to the plural network devices  22 ,  24 ,  26  for the plural qualified institutions via the cloud communications network. In  FIG. 7C  at Step  120 , the calculated set of market term estimates  15  in the cloud storage object  82  are securely sent from the cloud application  30 ′ on the cloud server network device  20  via the cloud communications network  18  to plural target network devices  12 ,  14 ,  16  each with one or more processors to provide electronic information as an indication of how the qualified institutions are required to conduct business based on the calculated set of market term estimates  15 . The cloud storage object  82  is sent securely from one or more public communication networks  76 , one or more private networks  72 , one or more community networks  74  and one or more hybrid networks  78  anywhere on the cloud communications network  18 . 
     The calculated set of market term estimates are displayed from on a graphical user interface from another cloud application on the plural target network devices to provide information as an indication of how the qualified institutions are required to conduct business and process transactions based on the calculated set of market term estimates. 
     Accordingly, the present invention provides advanced, financially guaranteed products in an organized and regulated inter-bank funds electronic market preferably for mid-sized banks. The process provides the following advantages: 
     A standardized spot and futures contracts for inter-bank funds 
     A secure electronic marketplace 
     Low transaction costs 
     Transparent prices 
     Quickly cleared transactions 
     Overall regulation by an independent third party and compliance providers, 
     The method and system describe herein provide market estimates for a set of time periods are received from plural qualified institutions that have agreed to a pre-determined set of regulations to participate in establishing, conducting business and processing transactions based on calculated market term estimates. A set of market term estimates (e.g., LIBOR, interest rates, stocks, bonds, options, other goods and services, etc.) and non-market term estimates are calculated in real-time for each time period in the set of time periods. The calculated set of market term estimates is sent to qualified institutions. The qualified institutions are required to conduct business and make transactions based on the calculated set of market term estimates. The calculated set of market term estimates is created and used on both cloud communication networks and non-cloud communications networks. 
     The market term estimates can be derived from different sources, including interest rates, hedge fund indices that provide performance benchmarks based on representative samples from different hedge funds, commodity rates (e.g., gold, silver or other commonly traded commodities), foreign exchange rates and any other market indexes or market-based rates that are based on estimates. 
     Market Estimation of an Indicative Term Structure 
     As previously discussed, AFX calculates and reports a daily AMERIBOR overnight (ON) unsecured interest rate at the end of each business day. The AMERIBOR overnight unsecured interest rate is the volume-weighted average annualized interest rate of loan transactions that qualify for inclusion in the calculation under the AFX Rulebook and that are executed on AFX during that business day in the AMERIBOR overnight unsecured loan market. The daily AMERIBOR overnight unsecured interest rate is denoted as a 360-day annualized percentage rate and is calculated out to five decimal places. 
     The AMERIBOR overnight unsecured loan market that is used to establish the AMERIBOR overnight unsecured interest rate currently operates in the following manner. The trading hours are on weekdays except federal holidays from 7:00 a.m. to 5:00 p.m. CT. Borrow and lend orders are entered by AFX participants into the AFX web-based electronic trading system. AFX participants may submit bids/offers to borrow/lend in increments of $1,000,000 in principal through standardized loan terms. Bids/offers are submitted as limit orders and include the side (borrowing/lending), size/amount, and price/interest rate. All bids and offers are anonymously displayed by AFX&#39;s trading system to all AFX participants. Borrow and lend orders are matched and executed based on price/time priority. AFX participants define the other AFX participants to which they would lend and the maximum amount they would lend to each eligible counterparty. A trade will only be executed if the borrower is on the lender&#39;s approved list and to the extent that the loan is within the cumulative limit established by the lender for that counterparty. AFX participants may also use functionality that allows an AFX participant to designate that a lender&#39;s offer will execute against a borrower&#39;s bid only if the bid is priced at or above the lender&#39;s minimum offer price established by the lender with respect to that borrower. Borrow and lend orders are matched in accordance with AFX rules. 
     Loan transaction executions in the AMERIBOR overnight unsecured loan market are displayed in real-time by AFX&#39;s trading system to all AFX participants. AFX also disseminates real time AMERIBOR overnight cash loan transaction data through the AMERIBOR website at www.AMERIBOR.net. This data includes last sale information, including the time, quantity, and interest rate of each transaction in the AMERIBOR overnight unsecured loan market. 
     AFX has established a daily price limit that is a percentage above and below the AMERIBOR overnight unsecured interest rate from the immediately preceding AFX business day. This price limit percentage is currently set at 15%. Based on this price limit, any bids that exceed 115%, and any offers that are less than 85%, of the AMERIBOR overnight unsecured interest rate from the immediately preceding AFX business day are not accepted or displayed by AFX&#39;s trading system. As a result, no transactions on AFX may occur that are less than 85%, or more than 115%, of the AMERIBOR overnight unsecured interest rate from the immediately preceding AFX business day. AFX may adjust the daily price limit percentage from time to time, such as because of interest rate increases or decreases resulting from Federal Open Market Committee policy or in order to allow for orderly markets. 
     The matching of a borrow order and a lend order results in the execution of a loan transaction between the AFX participants that submitted those orders. At the time of execution, the lender and borrower are deemed to have entered into the loan pursuant to the standardized terms specified by AFX and incorporated into AFX rules, at the specific terms of the loan resulting from the trade. Settlement of the loan is done directly between the AFX participants that are the parties to the trade. 
     The execution of a trade initiates the following settlement process. AFX directs the lender to transfer cash to the borrower equal to the principal amount of the loan. Following receipt of that instruction, the lender transfers to the borrower the principal amount of the loan via FedWire (or other customary and appropriate means) by no later than 5:30 p.m. CT on the date of the trade execution. Once the borrower has received the full amount of the principal relating to the loan, the borrower acknowledges to AFX within the AFX trading system by no later than 5:30 p.m. CT on that date that the borrower has received the principal. AFX does not include in the calculation of the AMERIBOR overnight unsecured interest rate any loan for which this acknowledgment is not received prior to the calculation of the AMERIBOR overnight unsecured interest rate for that AFX business day. On the maturity date of the loan, the borrower transfers to the lender the required repayment amount (including any interest) for the loan via FedWire (or other customary and appropriate means) by no later than 5:30 p.m. CT. Upon its receipt in full of the repayment amount, the lender acknowledges to AFX within the AFX trading system by no later than 5:30 p.m. CT on that date that the lender has received the repayment. 
     Each AFX participant agrees to be bound by, and comply with, AFX rules and to be subject to AFX&#39;s jurisdiction through the execution of a User Agreement with AFX and through the jurisdictional provisions of AFX rules which automatically apply to any AFX participant that accesses or enters an order into AFX&#39;s trading system. Under AFX Rules, a loan transaction that is executed on AFX becomes legally binding on each of the parties, and the borrower and lender become contractually obligated to each other to complete the loan and perform their obligations, in accordance with the standard terms included in AFX Rules. A failure to fund, or to pay principal or interest on, a loan executed through AFX, therefore, would constitute a violation of AFX Rules, for which AFX could impose sanctions, and a breach of agreement with the counterparty, which could result in liability for damages and potentially regulatory sanctions. 
     In other embodiments, the present invention provides computer-implemented systems and methods for electronic market estimation of an indicative term structure for an interest rate benchmark with market-based measures on a cloud communications network. The indicative term structure may be based in part on the AMERIBOR overnight unsecured interest rate (an ON benchmark rate). The method includes the steps of: 
     calculating, using a processor, the relevant inputs for independent variables to be used in the non-linear model estimation, whereby the relevant inputs include the overnight (ON) benchmark rate denoted by x 0 , the Consumer Price Index (CPI) denoted by x 1 , and the interest rate spread defined as the difference between the 90-day commercial paper and the three month T-Bill denoted by x 2 ; 
     storing, in a memory module, the relevant inputs previously calculated and updating them in real-time as the data is published on the Federal Reserve Economic Data (FRED); 
     retrieving the calculated inputs from the memory module in real-time on the cloud application for subsequent calculation of an indicative term structure estimate; 
     estimating, by means of a processor, a dynamic way to generate an indicative term structure, using the following non-linear pricing equation: 
         y=β   0 +β 1   x   0 +β 2   x   0   2 +β 3   x   1 +β 4   x   2 +ϵ  (1)
 
     calculating, using a processor, the dependent variable, y, the 30-day benchmark rate, or another term period through an iterative process involving the previously calculated market-based inputs; and 
     estimating, by means of a processor, the 90-day benchmark rate using the following linear pricing equation where x 0  is the 30-day indicative rate calculated in equation (1): 
         y=β   0 +β 1   x   0 +ϵ  (2)
 
     sending securely the calculated set of market term estimates via the cloud communications network to a plurality of target network devices to provide electronic information as an indication of how the qualified institutions have agreed to participate in establishing, conducting business, and processing transactions based on the calculated term estimates. 
     In a preferred embodiment, the variable x 1  is a monthly US Consumer Price Index (CPI) for all urban consumers (all items in US city average) and the variable x 2  is an interest rate spread defined as the difference between a daily US 90-day commercial paper (AA Financial) and a three-month T-Bill. 
     A specific illustration of calculating a 30-day benchmark rate, or indicative rate, using equation (1) and real-world data will not be discussed. As discussed above, the indicative rate is calculated as a function of the ON rate, expected inflation rate in the US, and the interest rate spread. 
     Referring now to  FIG. 8 , the indicative rate was calculated as a function of the ON rate, expected inflation, and the interest rate spread. CPI was used to account for expected inflation. In order to test and verify the model, 10 years of historical data ending Dec. 31, 2019 were used. A LASSO (least absolute shrinkage and selection operator was used as the regression analysis method to estimate the following non-linear regression model: 
         y=β   0 +β 1   x   0 +β 2   x   0   2 +β 3   x   1 +β 4   x   2 +ϵ  (1)
 
     where the dependent variable, y, is the 30-day LIBOR and the independent variables are the ON LIBOR denoted by x 0 , the US Consumer Price Index (CPI) for all urban consumers (all items in US city average) denoted by x 1 , and the interest rate spread defined as the difference between a daily US 90-day commercial paper (AA Financial) and a three-month T-Bill denoted by x 2 . The non-linear regression results provide a dynamic way to generate the indicative rate (30-day LIBOR rate). The results of the regression analysis are presented in Table 9. 
                     TABLE 9                  Regression Results, 30-day LIBOR                         Variables   {circumflex over (β)}   Standard Error               x 0  − ON LIBOR    1.0690***   0.0072       (ON LIBOR) 2     −0.0252***   0.0029       x 1  − CPI    0.0013***   0.0002       x 2  − Interest Rate Spread    0.0795***   0.0108       Constant   −0.2511***   0.0351               ***p &lt; 0.01,       **p &lt; 0.05,       *p &lt; 0.10            
The Parsimonious Konstant (PK) is the coefficient on the ON LIBOR, 1.0690. The interpretation of the PK is that for each one percentage point increase in the interest rate, the 30-day LIBOR is expected to increase by the PK of approximately 1.0690 percentage points. This model explains 99.6% of the variation in 30-day LIBOR yields. Because the model explains so much of the variation in the data, this model can accurately be used to calculate the indicative value.
 
     The results of the non-linear regression, the 30-day LIBOR, and the predicted values discussed above are shown on the chart depicted in  FIG. 8 . This chart shows that equation (1) illustrates that the 30-day LIBOR rates are explained by a combination of the LIBOR ON rate, expected inflation, and the interest rate spread. Although this approach may appear simpler than prior art approaches to model yield curves, this model explains over 99% (˜99.6%) of variation in the LIBOR data without requiring complicated calculations or analysis. This shows both the simplicity and predictive power of the model of equation (1). 
     In order to calculate the 90-day indicative rate, the 90-day LIBOR was regressed on the ON LIBOR and the 30-day indicative rate from equation (1). The following regression was used to estimate the 90-day model: 
         y=β   0 +β 1   x   0 +ϵ  (2)
 
     Where the dependent variable, y, is the 90-day indicative rate and the independent variable, x 0 , is the 30-day indicative rate. The results of the regression analysis are presented in table 10 below. 
                     TABLE 10                  Regression Results, 90-day LIBOR                         Variables   {circumflex over (β)}   Standard Error               x 0  − 30-Day Indicative Rate   1.0327***   0.0032       Constant   0.1192***   0.0033               ***p &lt; 0.01,       **p &lt; 0.05,       *p &lt; 0.10            
This model explains 97.8% of the variation in 90-day LIBOR yields. Similar to the 30-day model, because the model explains so much of the variation in the data, this model can accurately be used to calculate the 90-day indicative value.
 
     Using a similar approach, other indicative rates, such as an AMERIBOR30 rate or an AMERIBOR90 rate, can also be calculated using the equivalent variables, namely with y being the AMERIBOR30 rate and the independent variables being the ON AMERIBOR denoted by x 0 , CPI denoted by x 1 , and the interest rate spread denoted by x 2 . 
     Turning next to  FIG. 9 , depicted is a graph showing the market rate (y-axis) for a plurality of dates in the range from Jul. 2, 2018-Jul. 22, 2020. This chart shows the correlation between the ON AMERIBOR, the calculated 30-day LIBOR, and the calculated AMERIBOR30 rate. The calculated 30-day LIBOR rate and the AMERIBOR30 rate (R 2 =0.996) were calculated using equation (1) and the methodology discussed with reference to  FIG. 7 . The large divergence between the two values occurred exactly during the time when LIBOR was primarily based on estimates and not actual transactions (due to a March 2020 COVID-19 pandemic which caused a market meltdown and accurate transactions were not reported). 
     The  FIG. 10  graph is similar to  FIG. 9 , but shows the correlation between the ON AMERIBOR, the calculated AMERIBOR90, and the 90-day LIBOR. Again, this graph shows that linear regression performed using equation (1) leads to an accurate prediction of the AMERIBOR90. 
     Forward Looking Term Rates Calculated from Real World Transaction Data 
     In some embodiment, disclosed are systems and methods for calculating a forward-looking term rate composed entirely from real-world funding transactions data. The following description is related to the calculation of a 30-day rate. However, it should be obvious that any time-frame can be used (e.g., 30 day, 60 day, 90 day, etc.). 
     First, Commercial Paper (CP) and Commercial Deposit (CD) issuances collected by the Depository Trust &amp; Clearing Corporation (DTCC) are combined with AMERIBOR® unsecured overnight and 30-day lending data in order to generate a reference rate that is truly representative of funding costs. The CP and CD data is first trimmed for relevance. Preferably, the CP and CD data is trimmed so that it is limited to data issued by a financial company within the past 5 AFX trading days, within a days-to-expiration range of 2-40 days, and over $1 mm in principal. For example, the following transactions are samples of trimmed data:
         20 mm AMERIBOR® loan @ 1.15% for 1 day   20 mm CD issuance @ 1.25% for 10 days   10 mm CP issuance @ 1.40% for 20 days   5 mm CP issuance @ 1.45% for 30 days   5 mm CD issuance @ 1.50% for 40 days
 
After trimming the data for relevance, the most recent 5 AFX trading days&#39; overnight and 30-day lending transaction volumes and interest rates are combined with the trimmed DTCC data to make up the full data pool. A dollar basis point value is calculated for each transaction:
       

       20 mm*(1/360)*(0.01)=$555.56(2.60%) 
       20 mm*(10/360)*(0.01)=$5,555.56(25.97%) 
       10 mm*(20/360)*(0.01)=$5,555.56(25.97%) 
       5 mm*(30/360)*(0.01)=$4,166.67(19.48%) 
       5 mm*(40/360)*(0.01)=$5,555.56(25.97%) 
     Each transaction&#39;s interest rate is then weighted by volume and basis point value as shown below: 
       Rate=1.15%*(0.0260)+1.25%*(0.2597)+1.40%*(0.2597)+1.45%*(0.1948)+1.50%*(0.2597)=1.39% 
     While in actuality the sample pool contains thousands of transactions averaging billions of dollars in daily volume, the process works the same way. 
       FIG. 11  depicts a comparison of a 30-day rate calculated utilizing this described method. As shown, the resulting term rate has a correlation of 0.9937 with the 30-day LIBOR. Here, the calculated date range was from Jan. 2, 2018 to Nov. 20, 2020. However, similar correlation above 0.99 has been shown for various other date ranges. 
     The calculated 30-day rate can be used to aid lenders and borrowers with the transition from the soon-to-be-concluded LIBOR term rates. It is based solely upon real world transactions data without falling back on a regression or any sort of indicative analysis. By combining CP and CD transactions with AFX overnight and 30-day lending, a robust data pool averaging nearly $14 billion per day is created, which helps to safeguard the reference rate from any potential manipulation. If on any particular day underlying transaction volumes fail to meet a threshold level for sufficiency, the rate falls back by expanding the dataset to include an additional trailing AFX business day&#39;s CP, CD, and AMERIBOR data. If volume levels are still insufficient, this process is repeated until volume thresholds are met. 
     In a similar manner, a 90-day forward looking term rate can also be calculated composed entirely from real-world funding transactions data. CP and CD issuances collected by the DTCC are combined with AMERIBOR® unsecured overnight and 30-day lending data in order to generate a reference rate that is truly representative of funding costs. The CP and CD data is first trimmed for relevance (issued by a financial company within the past 5 AFX trading days, within a days-to-expiration range of 2-120 days, over $1 mm in principal). After trimming the data for relevance, the most recent 5 AFX trading days&#39; lending transaction volumes and interest rates are combined with the trimmed DTCC data to make up the full data pool. Each transaction&#39;s interest rate then is weighted by dollar basis point value according to the same process as the 30-day term rate previously described. 
     The resulting term-rate has a strong correlation with 3-month LIBOR and is based on over $18 billion in daily transaction volumes, making it highly resistant to manipulation.  FIG. 12  depicts a comparison of a 90-day rate calculated utilizing this described method to the 3-month LIBOR (5-day rolling average). As shown, the resulting 90-day term rate has a correlation of 0.9958 with the 30-day LIBOR for the period from Jan. 2, 2018 to Nov. 20, 2020. However, similar correlation above 0.99 has been shown for various other date ranges. 
     The resulting 90-day term rate historically shown a high correlation with three-month LIBOR and should aid lenders and borrowers with the transition from the soon-to-be-concluded LIBOR term rates. It is based solely upon real world transactions data without falling back on a regression or any sort of indicative analysis. By combining CP and CD transactions with AFX overnight lending, we get a robust data pool averaging over $18 billion per day, which helps to safeguard the reference rate from any potential manipulation. If on any particular day underlying transaction volumes fail to meet a threshold level for sufficiency, the rate falls back by expanding the dataset to include an additional trailing AFX business day&#39;s CP, CD, and AMERIBOR® data. If volume levels are still insufficient, this process is repeated until volume thresholds are met. 
     It should be understood that the architecture, programs, processes, methods and systems described herein are not related or limited to any particular type of computer or network system (hardware or software), unless indicated otherwise. Various types of general purpose or specialized computer systems may be used with or perform operations in accordance with the teachings described herein. As noted herein, the components of the system improve the functioning of the computers in an environment of attempting to accurately and expeditiously calculate and disseminate market interest rate estimates in an improved and unexpected manner compared to the prior art. 
     In view of the wide variety of embodiments to which the principles of the present invention can be applied, it should be understood that the illustrated embodiments are exemplary only, and should not be taken as limiting the scope of the present invention. Also, while various elements of the preferred embodiments have been described as being implemented in software, in other embodiments hardware or firmware implementations may alternatively be used, and vice-versa. Thus, the claims should be read to cover all embodiments that come within the scope and spirit of the invention including equivalents thereto.