Source: http://www.google.fr/patents/US7162444
Timestamp: 2015-05-22 22:08:29
Document Index: 202778353

Matched Legal Cases: ['art 400', 'art 400', 'art 400', 'art 400', 'art 400', 'art 400']

Brevet US7162444 - Method, system and computer program product for valuating natural gas ... - Google�BrevetsRecherche Images Maps Play YouTube Actualit�s Gmail Drive Plus »Connexion Recherche avanc�e dans les brevets BrevetsA method, system and computer program product for valuating natural gas futures and options contracts using weather-based metrics. The method and computer program product allow gas buyers and traders to make informed decision on purchasing/selling natural gas futures and futures options on a regulated...http://www.google.fr/patents/US7162444?utm_source=gb-gplus-shareBrevet US7162444 - Method, system and computer program product for valuating natural gas contracts using weather-based metrics Recherche avanc�e dans les brevets Num�ro de publicationUS7162444 B1Type de publicationOctroi Num�ro de demandeUS 09/641,394 Date de publication9 janv. 2007 Date de d�p�t18 ao�t 2000 Date de priorit�18 ao�t 2000�tat de paiement des fraisPay�Autre r�f�rence de publicationCA2323872A1 Num�ro de publication09641394, 641394, US 7162444 B1, US 7162444B1, US-B1-7162444, US7162444 B1, US7162444B1 InventeursRoberto C. Machado, Jr., Paul M. Corby, Bruce Frech Cessionnaire d'originePlanalytics, Inc.Exporter la citationBiBTeX, EndNote, RefManCitations de brevets (39), Citations hors brevets (39), R�f�renc� par (17), Classifications (12), �v�nements juridiques (4) Liens externes: USPTO, Cession USPTO, EspacenetMethod, system and computer program product for valuating natural gas contracts using weather-based metrics
US 7162444 B1 R�sum�
A method, system and computer program product for valuating natural gas futures and options contracts using weather-based metrics. The method and computer program product allow gas buyers and traders to make informed decision on purchasing/selling natural gas futures and futures options on a regulated exchange such as the New York Mercantile Exchange (NYMEX) based on historical and forecasted weather. The system includes weather forecast, weather history, and natural gas-related databases, as well as a trading server and several workstation clients, and provides assistance to traders in reaching complex buying/hedging decisions.
1. A computer-based method for valuating natural gas futures and options contracts using weather-based metrics, comprising the steps of:
(1) receiving, via a graphical user interface, an input from a user indicative of a number of monthly gas contracts desired for a period of time;
(2) receiving, from a first database stored in a memory, historical weather information for at least one basket of cities during said period of time;
(3) receiving, from a second database stored in said memory, future weather information for said at least one basket of cities during said period of time;
(4) receiving, from a third database stored in said memory, historical natural gas inventory information for said at least one basket of cities during said period of time;
(5) receiving, from a fourth database stored in said memory, historical gas futures contract price information for said period of time;
(6) applying, at a server, a series of regression analyses to obtain a predicted baseline value for each of the monthly gas contracts within said period of time using said received historical weather information, said future weather information, said historical natural gas inventory information, and said historical gas futures contract price information;
(7) receiving, from a data feed, live exchange data which indicates a current price for each of the monthly gas contracts within said period of time;
(8) applying, at said server, a series of recommendation rules to said predicted baseline value, using said received live exchange data; and
(9) providing, via said graphical user interface, said user with a recommendation for each of the monthly gas contracts within said period of time, wherein said recommendation reflects said input from said user indicative of said number of the monthly gas contracts desired for said period of time.
2. The method of claim 1, wherein said series of regression analyses applied in step (6), comprises the steps of:
(a) performing, at said server, a linear regression of said historical weather information and said historical natural gas inventory information; and
(b) performing, at said server, a multi-variate regression of said historical gas futures contract price information, said historical weather information and said historical natural gas inventory information.
3. The method of claim 1, wherein said series of recommendation rules provided in step (8) includes at least one of the following:
(i) Strong Buy;
(ii) Buy;
(iii) Buy a Call;
(iv) Write a Put;
(v) Sell; and
(vi) Strong Sell.
4. A system for valuating natural gas futures and options contracts using weather-based metrics, comprising:
a weather history database that stores historical weather information for at least one basket of cities;
a weather forecast database that stores future weather information for said at least one basket of cities;
an inventory database that stores historical natural gas inventory information for at least said at least one basket of cities;
a price database that stores historical natural gas futures prices information;
at least one workstation that allows a user to specify inputs that affect a value of the natural gas futures and options contracts; and
at least one trading server, responsive to said at least one workstation and connected to said weather history database, said weather forecast database, said inventory database, and said price database, that applies a pricing model to valuate the natural gas futures and options contracts using said specified inputs from said user;
whereby the system provides assistance to said user in reaching buying/hedging decisions in trading the natural gas futures and options contracts.
5. A computer program product comprising a computer usable medium having computer readable program code means embodied in said medium for causing an application program to execute on a computer that performs valuations of natural gas futures and options contracts using weather-based metrics, said computer readable program code means comprising:
first computer readable program code means for causing the computer to receive an input from a user indicative of a number of monthly gas contracts desired for a period of time;
second computer readable program code means for causing the computer to receive historical weather information for at least one basket of cities during said period of time;
third computer readable program code means for causing the computer to receive future weather information for said at least one basket of cities during said period of time;
fourth computer readable program code means for causing the computer to receive historical natural gas inventory information for said at least one basket of cities during said period of time;
fifth computer readable program code means for causing the computer to receive historical gas futures contract price information for said period of time;
sixth computer readable program code means for causing the computer to apply a series of regression analyses to obtain a predicted baseline value for each of the monthly gas contracts within said period of time using said received historical weather information, said future weather information, said historical natural gas inventory information, and said historical gas futures contract price information;
seventh computer readable program code means for causing the computer to receive live exchange data which indicates a current price for each of the monthly gas contracts within said period of time;
eighth computer readable program code means for causing the computer to apply a series of recommendation rules to said predicted baseline value, using said received live exchange data; and
ninth computer readable program code means for causing the computer to provide said user with a recommendation for each of the monthly gas contracts within said period of time, wherein said recommendation reflects said input from said user indicative of said number of the monthly gas contracts desired for said period of time.
6. The computer program product of claim 5, wherein said sixth computer readable program code means comprises:
tenth computer readable program code means for causing the computer to perform a linear regression of said historical weather information and said historical natural gas inventory information; and
eleventh computer readable program code means for causing the computer to perform a multi-variate regression of said historical gas futures contract price information, said historical weather information and said historical natural gas inventory information.
The following applications of common assignee are related to the present application and are each hereby incorporated herein by reference in their entirety:
“System, Method, and Computer Program Product for Valuating Weather-Based Financial Instruments,” Ser. No. 09/168,276, filed Oct. 8, 1998, now U.S. Pat. No. 6,418,417.
“System and Method for the Advanced Prediction of Weather Impact on Managerial Planning applications,” Ser. No. 08/002,847, filed Jan. 15, 1993, now U.S. Pat. No. 5,521,813.
“A User Interface For Graphically Displaying the Impact of Weather on Managerial Planning,” Ser. No. 08/504,952, filed Jul. 20, 1995, now U.S. Pat. No. 5,796,932.
“System and Method for Determining the Impact of Weather and Other Factors on Managerial Planning Applications,” Ser. No. 08/205,494, filed Mar. 4, 1994, now U.S. Pat. No. 5,491,629.
“System and Method for Weather Adapted, Business Performance Forecasting,” Ser. No. 08/588,248, filed Jan. 18, 1996, now U.S. Pat. No. 5,832,456.
The present invention relates generally to financial trading systems and more particularly to the processing, valuating, and trading of financial instruments such as futures and the like.
In today's financial markets, the use of financial instruments known as futures contracts are common place. Futures contracts are standardized, transferable agreements, which may be exchange-traded, to buy or sell a commodity (e.g., a particular crop, livestock, oil, natural gas, etc.). These contracts typically involve an agreed-upon place and time in the future between two parties. That is, a futures contract is supply contract between a buyer and seller, where the buyer is obligated to take delivery, and the seller is obligated to provide delivery of a fixed amount of a commodity at a predetermined price at a specified location. Futures contracts are typically traded exclusively on regulated exchanges and are settled daily based on their current value in the marketplace.
Another form of financial instruments are option contracts. Options contracts are agreements, that may be exchange-traded, among two parties that represent the right to buy or sell a specified amount of an underlying security (e.g., a stock, bond, futures contract, etc.) at a specified price within a specified time. (In relation to the present discussion, an options contract which specifies gas futures is of most interest.)
The parties of options contracts are purchasers who acquire “rights,” and sellers who assume “obligations.” Further, a “call” option contract is one giving the owner the right to buy, whereas a “put” option contract is one giving the owner the right to sell the underlying security. There is typically an up-front, non-refundable premium that the buyer pays the seller to obtain the option rights.
Options and futures contracts are explained in detail in John Hull, Options, Futures, and Other Derivative Securities, Prentice Hall (3rd. ed. 1997), ISBN 0138874980, which is incorporated herein by reference in its entirety.
Taking the example of a specific commodity—natural gas—traders typically buy and sell natural gas futures on a daily basis on the New York Mercantile Exchange (NYMEX) regulated exchange. What is commonly referred to as “natural gas” is a naturally occurring mixture of hydrocarbon and other gases found in porous rock formations. Its principal component is methane whose molecular formula is CH4. It is estimated that natural gas currently provides about 24 percent of all the energy used in the United States.
Typically, gas traders (i.e., those who buy and sell natural gas futures and options) represent the interests of utility companies and other entities who require a large supply of natural gas in order to provide energy to businesses and homes. In order to assure continuous operations, while minimizing expenses, utility companies and other entities buy and sell (i.e., trade) natural gas futures and options.
Because futures and option contracts (i.e., “gas futures”) are essentially financial instruments, they may be traded among investors as are stocks, bonds, and the like. Thus, in order to trade gas futures and options, there must be a mechanism to price them so that traders may exchange them in an open market.
The relationship between the value of a gas future or option and the value of the underlying commodity are not linear and can be very complex. Economists have developed pricing models in order to valuate certain types of futures and options. Further, many strategies exist for utility companies and other entities to predict the demand for energy and thus, the number of contracts needed over a specific period of future time. Each model and strategy has inherent flaws, and thus poses risks.
Risks in relying on any one model or strategy includes errors in the model's underlying assumptions, errors in calculation when using the model, and failure to account for variables (i.e., occurrences) that may affect the price of the underlying commodity (i.e., natural gas). For example, factors such as economics, politics, etc. play a critical role in estimating demand for natural gas.
When considering the latter risk—failure to account for occurrences that may affect price—weather is one occurrence which has been historically been overlooked. That is, weather, and more specifically future weather, has not been included as a formal variable in pricing models.
The few models that have considered weather usually have only considered past (i.e., historical) weather data. Further, strategies based on predicated demand also have only considered historical weather data. That is, most models and strategies assume, for example, that the previous year's weather and its effects on power demand will repeat from year to year. Historical analysis has shown, however, that this assumption is true only a quarter of the time. Thus, regardless of whether futures or options are being evaluated, risk management trading techniques, strategies, or vehicles, traders essentially have been operating in the “blind” without knowledge of future weather conditions.
Therefore, what is needed is a method, system and computer program product for valuating (and thus, processing and trading) natural gas futures and options contracts using weather-based metrics.
The present invention meets the above-identified needs by providing a method, system and computer program product for valuating (and thus, processing and trading) natural gas futures and options contracts using weather-based metrics. The method, system and computer program product captures the extreme sensitivity to future weather, captures volatile price swings in growing paper markets, and provides assistance to traders in reaching complex buying/hedging decisions.
The method and computer program product involve receiving an input from a user indicative of the number of monthly gas contracts desired for a period of time. Next, historical and future weather information and historical natural gas inventory information for a basket of cities, during the entered period of time, are received. Then, historical gas futures contract price information for the period of time is received.
The method and computer program product then apply a series of regression analyses to obtain a predicted baseline value for each of the monthly gas contracts within the period. This is accomplished by using historical weather information for the basket of cities and historical natural gas inventory information. Then baseline values are calculated using future weather information.
Then, live exchange data which indicates the current price for each of the monthly gas contracts within the period of time is received. The method and computer program product then apply a series of recommendation rules, using the received live exchange data and baseline values, providing the user with a recommendation for each of the monthly gas contracts within the period of time. The users of the system thereby receive assistance in reaching complex buying/hedging decisions.
The system for valuating a weather-based financial instrument of the present invention includes a weather history database that stores historical weather information for at least one geographic location and a weather forecast database that stores future weather information for the geographic location. The system may also include several natural gas-related databases that store information in order to determine buying/hedging strategies. In order to access the databases and valuate financial instruments, a trading server is included within the system. The trading server provides the central processing of the system by applying a pricing model, and is responsive to a plurality of internal and external workstations that allow users, via a graphical user interface, to access the trading system.
One advantage of the present invention is that gas futures and options can be priced more easily and confidently when accounting for future weather.
Another advantage of the present invention is that it provides a trading system which guides traders by providing buy and sell recommendations for various futures and option contracts.
TO BRIEF DESCRIPTION OF THE FIGURES
FIG. 2 depicts, in one embodiment, a weather history database used by the present invention;
FIG. 3 depicts, in one embodiment, a weather forecast database used by the present invention;
FIGS. 4A–B are flowcharts representing, in one embodiment, the operation of the present invention;
FIG. 5 is an exemplary graphical user interface screen for the trading system of the present invention;
FIGS. 7–8 depict example gas databases used by the present invention.
B. An Example Natural Gas Futures Contract
F. Gas Databases
The present invention is directed to a method, system and computer program product for valuating (and thus, processing and trading) natural gas futures and options contracts using weather-based metrics. In an embodiment of the present invention, a trading organization provides a service that facilitates gas futures and options trading for clients as well as providing an interactive World-Wide Web site accessible via the global Internet.
Such a system would allow the clients (i.e., gas buyers) who represent the interests of utility companies and large entities such as manufacturers, agribusiness, or other industries with large power demands to intelligently trade and use gas futures and options to hedge against weather-related market risks.
The present invention is described in terms of the above example. This is for convenience only and is not intended to limit the application of the present invention. In fact, after reading the following description, it will be apparent to one skilled in the relevant art how to implement the following invention in alternative embodiments. For example, and without limitation, the present invention would also benefit power marketers, fuel traders, power traders, fuel emissions credit traders, investment banks, insurance and re-insurance companies, capital market traders, commodity traders, and over-the-counter (OTC) traders (i.e., anyone whose business relates to power and whose “bottom-line” is affected by weather). These entities would benefit from the present invention not only by having a tool which enables them to hedge against weather-related market risks, but also to speculate for profit. Further, although NYMEX conventions are referenced herein, the present invention could also be used to reference the month-end Inside-FERC monthly settlement price for a gas futures contract.
B. An Example Gas Futures Contract
The present invention is described below in terms of a gas contract. This is for convenience only and is not intended to limit the application of the present invention. Further, the term “gas contract” is used herein to refer to either a natural gas futures contract, an option on a natural gas futures contract, and/or other gas contracts (e.g., physical) as applicable.
As mentioned above, a futures contract is a supply agreement between a buyer and seller, where the buyer and seller are obligated to provide delivery of a fixed amount of a commodity at a predetermined price at a specified location, or exchange the cash differential of the contract at its expiration. A gas futures contract typically traded on the NYMEX includes the following example terms as shown in TABLE 1 below.
TABLE 1 Seller will deliver: 10,000 MMBtu of Natural Gas at Henry Hub Buyer will pay: $x per MMBtu Expiration Date: <month>, <year> In the example, the quantity of natural gas is 10,000 units of one million British thermal units (MMBtu). An MMBtu is the equivalent of one dekatherm, which is approximately equal to a thousand cubic feet (ft3) of natural gas.
The delivery location in the example is specified as “Henry Hub.” This is the port of New Orleans, La., and is the standard gas contract delivery location used by NYMEX in their gas futures prices quoting system.
The expiration date, the date and time after which trading in a contract terminates, and after which obligations become due, is specified by a month and a year. Thus, for example, an expiration date of “April, 2001” would indicate, by NYMEX convention, that the contract expires three business days before the end of March, 2001. Such a contract would be called an “April 2001 contract,” because delivery of the natural gas is to be actually done in April of 2001 (i.e., an “April 2001 contract” expires in March, three business days before Apr. 1, 2001).
Referring to FIG. 1, a natural gas trading system 100, according to an embodiment of the present invention, is shown. It should be understood that the particular trading system 100 in FIG. 1 is shown for illustrative purposes only and does not limit the invention. Other implementations for performing the functions described herein will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein, and the invention is directed to such other implementations. As will be apparent to one skilled in the relevant art(s), all of components “inside” of the trading system 100 are connected and communicate via a communication medium such as a local area network (LAN) 101.
The trading system 100 includes a trading server 102 that serves as the “back-end” (i.e., processing system) of the present invention. Connected to the trading server 102, are gas databases 104 and 116, weather forecast database 106 and a weather history database 108. These databases are explained in more detail below. Also connected to trading system 100, as will be appreciated by those skilled in the relevant art(s), is a live data feed 118 of current gas contract prices available from a regulated exchange where such contracts are traded (e.g., NYMEX).
The trading server 102 is also connected to a Web server 110. As is well-known in the relevant art(s), a Web server is a server process running at a Web site which sends out Web pages in response to Hypertext Transfer Protocol (HTTP) requests from remote browsers. The Web server 110 serves as the “front end” of the present invention. That is, the Web server 110 provides the graphical user interface (GUI) to users of the trading system 100 in the form of Web pages. Such users may access the Web server 110 at the weather trading organization's site via a plurality of internal workstations 110 (shown as workstations 110 a–n).
A firewall 112 (shown as “FW” 112) serves as the connection and separation between the LAN 101, which includes the plurality of network elements (i.e., elements 102–110 and 116–120) “inside” of the LAN 101, and the global Internet 103 “outside” of the LAN 101. Generally speaking, a firewall—which is well-known in the relevant art(s)—is a dedicated gateway machine with special security precaution software. It is typically used, for example, to service Internet 103 connections and dial-in lines, and protects a cluster of more loosely administered machines hidden behind it from an external invasion.
The global Internet 103, outside of the LAN 101, includes a plurality of external workstations 114 (shown as workstations 114 a–n). The external workstations 114 allow client-users (traders) of the trading organization to remotely access and use the trading system 100.
The trading system 100 includes an administrative workstation 120 that may be used by the trading organization to update, maintain, monitor, and log statistics related to the server 102 and the trading system 100 in general. While one trading server computer 102 is shown in FIG. 1, it will be apparent to one skilled in the relevant art(s) that trading system 100 may be run in a distributed fashion over a plurality of the above-mentioned network elements connected via LAN 101. Similarly, while several databases (i.e., 104 and 116, 106, and 108) are shown in FIG. 1, it will be apparent to one skilled in the relevant art(s) that trading system 100 may utilize databases physically located on one or more computers which may or may not be the same as sever 102. More detailed descriptions of the trading system 100 components, as well as their functionality, are provided below.
An example weather history database 108 is shown in FIG. 2. The weather history database 108 is described in detail in a commonly-owned U.S. Pat. No. 5,832,456 incorporated herein by reference in its entirety. For completeness, however, the weather history database 108 is briefly described herein. The weather history database 108 includes, for each year in the view, one or more records for each metropolitan area (MA). (The term MA closely resembles the well known name Metropolitan Statistical Area (MSA). However MA encompasses a larger surrounding geographical area/region than the strict MSA definition.) (However, since MA and MSA are similar, they are used interchangeably herein.) The weather history database 108 contains but is not limited to data on metropolitan areas. These records contain information specifying the weather that occurred in the subject MA in the time span represented in the view. Specifically, for each MA, there is a record for each of several weather data types.
Each weather pattern includes one or more weather parameters. For example, the temperature weather pattern includes the temperature parameter and the seasonal parameter. For any given period, each parameter can be either seasonal, below seasonal, or above seasonal. For any given period, the values of these weather patterns are represented by the entries (see records 202–205 in FIG. 2) in the weather history database 108 having the category data type. This file is used as the “look up” to allow the system to determine which patterns it will use.
An example weather forecast database 106 is shown in FIG. 3. The weather forecast database 106 is described in detail in the commonly-owned U.S. Pat. No. 5,832,456 incorporated herein by reference in its entirety. For completeness, however, the weather forecast database 106 is briefly described herein. The weather forecast database 106 includes, for each future year in the view, one or more records for each MA. These records (e.g., records 302–304) contain information specifying the weather that is predicted to occur in the subject MA in the future time span represented in the view. Specifically, for each MA, there is a record for each of several weather data types.
Similar to weather history database 108, weather forecast database 106 contains three classes of weather data types—seasonal, actual and category. These categories are the same as those described above with respect to the weather history database 108. Accordingly, the description above of the weather history database 108 also applies to the weather forecast database 106.
As evident by the description above, the weather history database 108 is a past database because it contains historical information. In contrast, the weather forecast database 106 is a future database because it contains information pertaining to predicted weather in the future, or future weather.
Further, in an embodiment of the present invention, both databases 106 and 108 would contain MA weather data for at least two specific “baskets of cities.” For example, during the heating season (October–April), a basket of cities containing weather data for New York, Kansas City, Chicago and Pittsburgh would be of most interest to the operation of trading system 100 as will be explained in more detail below. Further, during the cooling season (May–September), for example, a basket of cities containing weather data for New York, Dallas, Houston, New Orleans, and Miami would be of most interest to the operation of trading system 100 as will be explained in more detail below.
In one embodiment of the present invention, the individual cities which are included in the heating and cooling season basket of cities would be those chosen by, but not limited to, the United States Department of Energy in their energy demand analyses.
The gas databases 104 and 116 contain the data that is used by the trading server 102 that are relevant in determining the complex buying/hedging decisions for which users will employ trading system 100.
In a preferred embodiment, gas database 104 would include historical natural gas futures price information. That is, database 104 would include the daily high, low and closing prices for each month's gas contracts for a historical time period (e.g., the previous five years). That is, for a particular historical date (e.g., Dec. 1, 1994), database 104 would contain that date's high, low and closing price for each month's contract going forward twelve months (i.e., January contract through December contract for 1995). An example gas database 104 is shown in FIG. 8.
In a preferred embodiment, database 116 would include historical American Gas Association (AGA) inventory information. The AGA is a natural gas industry trade association, currently based in Alexandria, Va. The AGA conducts technical research and helps create standards for equipment and products involved in every facet of the natural gas industry. It also compiles statistics which are considered industry standards. One such statistic is the weekly inventory of natural gas, measured in cubic feet, currently found in each of three regions of the United States: (1) the Producing Region (i.e., the gulf coast); (2) the Consuming East Region (i.e., east of the Rocky Mountains); and (3) the Consuming West Region (i.e., west of the Rocky Mountains). Thus, database 116 would include the 52 weekly measurements for each of three regions for a historical time period (e.g., the previous five years). Database 116 would also include the most currently available AGA inventory information (i.e., contain inventory data up to the present week). An example gas database 116 is shown in FIG. 7.
In an embodiment of the present invention, during the operation of trading system 100, the AGA inventory data in database 116 for the three regions are correlated with the weather data in databases 106 and 108 for the basket of cities discussed above.
As will be appreciated by one skilled in the relevant art(s), the gas databases 104 and 116 may include additional financial information on an application specific basis.
Referring to FIG. 4A, a flowchart 400 representing the operation of trading system 100, according to an embodiment of the present invention, is shown. Flowchart 400 begins at step 402 with control passing immediately to step 404.
In step 404, the user (e.g., gas buyer) enters the number of contracts they require for each of the twelve months going forward. The user would input this information based on the estimated consumption demand of the entity whose interest they represent.
In step 406, both the historical weather database 108 and AGA database 116 are read so that the trading server 102 has the correct information for processing. More specifically, the trading server 102 would query the AGA database 116 for the historical AGA natural gas inventory information for the relevant time period (e.g., starting at the present date and going backwards for a one-year period).
Also, in step 406, the trading server 102 would query the weather history database 108 (or obtain the information from some other source, such as a commercial service or governmental agency) for historical temperature information for each of the cities located in the cooling and heating season basket of cities, as applicable. Such historical weather would date back, in one embodiment, at least five years.
In an embodiment of the present invention, during the operation of trading system 100 (i.e., step 406 and step 410 described below), the weather data (e.g., daily average temperature) for the individual cities which are included in the heating and cooling season basket of cities can be equally considered, or weighted according to population, perception (e.g., weighing weather data for New York more heavily than the other cities in the basket because of NYMEX's location), etc.
After the completion of step 406, flowchart 400 may proceed to both steps 408 and 412.
In step 408, a first regression analysis is performed. In an embodiment of the present invention, linear regression is used. As will be appreciated by those skilled in the relevant art(s), linear regression, an example of multi-variate modeling techniques, is useful when using several variables to predict the values of a single continuous dependent variable. In general, regression generates exact coefficients for each predictor, and shows what proportion of the variability of the dependent variable is uniquely explained by each individual predictor and a measure of volatility (standard deviation). This makes it possible to build a predictive model.
In alternative embodiment, other non-linear regression analysis (e.g., curvilinear regression, loglinear analysis, etc.) may be employed. These analysis techniques are well-known in the relevant art(s), and are described in detail, for example, in David G. Kleinbaum et al., Applied Regression Analysis and Other Multivariable Methods, Duxbury Press (3rd. ed. 1998), ISBN 0534209106, which is hereby incorporated by reference in its entirety.
The regression of step 408 uses the historical weather and AGA inventory data read in step 406 in order to obtain an estimate for AGA inventory change. Mathematically, step 408 can be represented as follows. First, EQUATION (1) is the standard equation of a line (i.e., the linear equation):
y=m 1 x+b; (1)
where m1 is the slope and b is the y-axis intercept of the line. Using the AGA inventory data as the y-axis, and the historical weather (i.e., temperature) data as the x-axis, the linear regression of step 408 produces a straight line from the data points and determines m1.
In step 412, the historical natural gas futures prices database 104 is read. This allows, in step 414, a three-dimensional (multi-variate) regression analysis to be performed. The regression of step 414 uses the historical gas contract prices data as the y-axis, historical weather data as the x-axis, and historical AGA inventory data as the z-axis, in order to obtain an estimate of each month's contract. Mathematically, this can be represented by EQUATION (2):
y=m 1 x 1 +m 2 x 2 +b; (2)
where y is the price of the contract, m1 is the AGA inventory, x1 is the historical AGA inventory data, x2 is the historical weather data, b is the y-axis intercept. Step 414 produces a straight line from the data points. In addition, step 414 generates a measure of price volatility (i.e., standard deviation) used later in the process in applying the recommendation rules (see TABLE 2).
In step 410, the weather forecast database 106 is read. This allows, in step 416, the result of both steps 408 and 414 to be used to obtain a predicted closing value (i.e., a “baseline”). EQUATION (2) is used to compute the baseline value (i.e., solving fory—the baseline), where x2 is substituted by future weather and the y from EQUATION (1) becomes x1.
In step 418, the live exchange data 118 is read. In step 420, a series of recommendation rules (i.e., conditions) are applied to arrive at an action recommendation in step 422. Flowchart 400 then ends as indicated by step 424.
In an embodiment of the present invention, the series of recommendation rules applied in step 420 to the baseline values are summarized in TABLE 2 below. The rules appear in the “if (condition) recommendation” pseudo-code notation.
B = baseline value obtained in step 416
F = current future's price obtained in step 418
δ = standard deviation obtained from the regression analysis
of step 414
Using EQUATION (3):
then the recommendation rules are applied as follows:
if(n > n1)
Strong Sell;
else if(n > n2)
else if(n > n3)
Write a Put;
else if(n > n4)
Buy a Call;
else if(n > n5)
Strong Buy;
where n1 = 1.0, n2 = 0.5, n3 = 0, n4 = −0.5, and n5 = −1.0.
In TABLE 2, the values n1 to n5 are examples used in a preferred embodiment of the present invention. In essence, EQUATION (3) converts prices into standard deviations. Thus, the further away n is from the baseline, the stronger the recommendation signal. The values of n1 to n5, can be subjectively varied based on the observations of the trading organization and the specific implementation of the predictive model used in the price analysis. As will be apparent to one skilled in the relevant art(s), various analysis of historical natural gas prices as correlated with weather can be used to determine the values of n1 to n5 that yield the best recommendations.
In an embodiment of the present invention, as apparent from TABLE 2, one of six action recommendations are given to the users of the trading system 100. These recommendations are summarized in TABLE 3 below.
This is a strong signal to buy a futures contract.
Condition: the current futures price is well
below the predicted closing price.
Buy a Futures contract.
Condition: the current futures price is below the
predicted closing price.
Buy a Call option on the Futures contract for
that month. When the option comes due, the
user will either exercise the option if the strike
price is below the contract closing price.
Otherwise the user will buy at the contract
Condition: the current futures price is below or
close to the predicted closing price.
Write (i.e., sell) a Put option on the Futures
contract for that month. When the option comes
due, the buyer of the option will sell the gas to
the user if the market price is below the strike
price. Otherwise the buyer will let the option
expire and the user will buy at the contract
Condition: the current futures price is above or
Sell a futures contract or buy natural gas at
the index (i.e., closing) settlement price for
that month. This can be done by just waiting
and buying gas at the bid contract closing price,
or the user can contact a supplier and notify
them that the user will be buying natural gas at
the contract index price for that month.
Condition: the current futures price is above
the predicted closing price.
This is a strong signal to sell a futures contract
or buy natural gas at the index settlement price
above the predicted closing price.
Referring to FIG. 4B, flowchart 400, which represents the operation of trading system 100 according to one embodiment of the present invention, is shown in a control flow format. That is, FIG. 4B, as will be appreciated by one skilled in the relevant art(s), illustrates how the system 100 components interact during the operation of flowchart 400 according to one embodiment of the present invention.
A GUI screen 500 with the representative numbers is shown in FIG. 5. The GUI screen 500 includes a column 501 which labeled “days left” which indicated to the user the number of days until that particular month's contract expires. Screen 500 also includes a column 502 which indicates the month and year of the contract for which a particular row in the GUI screen contains information. A column 503 contains the current gas contract prices available from a regulated exchange where such contracts are traded (e.g., NYMEX) and obtained from live data feed 118. A column 504 contains the change in price for each contract from a past price (e.g., yesterday's closing price).
GUI screen 500 also includes a column 505 indicates the implied volatility of the contract's price. This is calculated, for example, using the Black-Scholes option pricing model. As is well-known in the relevant art(s), the Black-Scholes option pricing model, based on stochastic calculus, is the most influential and extensively used options pricing model and is described in detail in a variety of publicly available documents, such as Neil A. Chriss, The Black-Scholes and Beyond Interactive Toolkit: A Step-by-Step Guide to In-depth Option Pricing Models, McGraw-Hill (1997), ISBN: 078631026X, which is incorporated herein by reference in its entirety.
GUI screen 500 also includes a display 506 which indicates to the user the current (i.e., today's) date. A column 507 indicates, for each contract, one of the six recommendations trading system 100 outputs as explained above and detailed in TABLE 3. A column 508, when viewed in conjunction with the recommendation of column 507, indicates the number of each month's contract the user should act upon in accordance with the recommendation. This is calculated by trading system 100 by using the desired number of contracts the user inputted in step 404 described above with reference to FIG. 4A.
GUI screen 500 also includes a column 509 which displays, if the recommendation in column 207 relates to an option contract (i.e., a call or a put), the premium for the contract. As will be appreciated by those skilled in the relevant art(s), a premium is the up-front, non-refundable amount that a buyer pays a seller to obtain an option as determined competitively by buyers and sellers in open outcry trading on an exchange (e.g., NYMEX) trading floor.
GUI screen 500 also includes a column 510, which reflects the number of each contract the user desires as inputted in step 404 described above with reference to FIG. 4A. A column 511 reflects how many actual contracts the user has obtained to date. This number, if the user of trading system 100 uses it properly, should be equal to or less than the desired number of each month's contract appearing in column 510. Columns 512, 514, 516 and 518 are the number of futures, index, call, and put contracts, respectively, the user has obtained to date. The sum of the number of contracts appearing in columns 512, 514, 516 and 518 should equal the number of actual contracts appearing in column 511.
It should be understood that the control flow shown in FIGS. 4A–B and thus, GUI screen 500 shown in FIG. 5, are presented for example purposes only. The present invention is sufficiently flexible and configurable such that users (on the plurality of workstations 110 and/or 114) may navigate through the system 100 in ways other than that shown in the figures.
The present invention (i.e., natural gas trading system 100 or any part thereof) may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems. In fact, in one embodiment, the invention is directed toward one or more computer systems capable of carrying out the functionality described herein. An example of a computer system 600 is shown in FIG. 6. The computer system 600 includes one or more processors, such as processor 604. The processor 604 is connected to a communication infrastructure 606 (e.g., a communications bus, cross-over bar, or network). Various software embodiments are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person skilled in the relevant art(s) how to implement the invention using other computer systems and/or computer architectures.
Citations de brevets Brevet cit� Date de d�p�t Date de publication D�posant TitreUS380841019 juin 197230 avr. 1974R SchlesingerMethod for providing representation for needed work force in a storeUS401536611 avr. 19755 avr. 1977Advanced Decision Handling, Inc.Highly automated agricultural production systemUS404062923 juin 19759 ao�t 1977John KellyCommodities board game apparatusUS421875519 juin 197819 ao�t 1980Root Steven AWeather forecasting apparatusUS476653915 juil. 198723 ao�t 1988Fox Henry LMethod of determining the premium for and writing a policy insuring against specified weather conditionsUS47841504 nov. 198615 nov. 1988Research CorporationSurgical retractor and blood flow monitorUS506350623 oct. 19895 nov. 1991International Business Machines Corp.Cost optimization system for supplying partsUS512886128 nov. 19897 juil. 1992Hitachi, Ltd.Inventory control method and systemUS51288624 sept. 19907 juil. 1992Management Information Support, Inc.Customer operable system for a retail store or fast-food restaurant having plural ordering stationsUS513092520 f�vr. 199114 juil. 1992Rutgers, The State UniversityApparatus and method for economical continuous, and predictable greenhouse crop productionUS514052314 d�c. 199018 ao�t 1992Ktaadn, Inc.Neural network for predicting lightningUS516844521 f�vr. 19891 d�c. 1992Hitachi, Ltd.Automatic ordering system and method for allowing a shop to tailor ordering needsUS518960614 mai 199123 f�vr. 1993The United States Of America As Represented By The Secretary Of The Air ForceTotally integrated construction cost estimating, analysis, and reporting systemUS520866515 f�vr. 19914 mai 1993Telaction CorporationPresentation player for an interactive digital communication systemUS523749624 mars 199217 ao�t 1993Hitachi, Ltd.Inventory control method and systemUS52509419 ao�t 19915 oct. 1993Mcgregor Peter LCustomer activity monitorUS525316512 d�c. 199012 oct. 1993Eduardo LeisecaComputerized reservations and scheduling systemUS528386513 oct. 19921 f�vr. 1994Clear With Computers, Inc.Computer-assisted parts sales systemUS529506421 sept. 198815 mars 1994Videocart, Inc.Intelligent shopping cart system having cart position determining and service queue position securing capabilityUS52950695 juin 199115 mars 1994International Business Machines CorporationComputer method for ranked hyphenation of multilingual textUS53093553 sept. 19933 mai 1994Lockwood Lawrence BAutomated sales systemUS53770957 juil. 199227 d�c. 1994Hitachi, Ltd.Merchandise analysis system with sales data table and various functions for predicting the sale by itemUS54916294 mars 199413 f�vr. 1996Strategic Weather ServicesSystem and method for determining the impact of weather and other factors on managerial planning applicationsUS552181315 janv. 199328 mai 1996Strategic Weather ServicesSystem and method for the advanced prediction of weather impact on managerial planning applicationsUS579693220 juil. 199518 ao�t 1998Strategic Weather ServicesUser interface for graphically displaying the impact of weather on managerial planningUS583245618 janv. 19963 nov. 1998Strategic Weather ServicesSystem and method for weather adapted, business performance forecastingUS6021402 *5 juin 19971 f�vr. 2000International Business Machines CorporaitonRisk management system for electric utilitiesUS6418417 *8 oct. 19989 juil. 2002Strategic Weather ServicesSystem, method, and computer program product for valuating weather-based financial instrumentsUS658444731 juil. 199824 juin 2003Planalytics, Inc.Method and computer program product for weather adapted, consumer event planningJPH0477896A Titre non disponibleJPH0676161A Titre non disponibleJPH01236396A Titre non disponibleJPH01259488A Titre non disponibleJPH02268396A Titre non disponibleJPH02299059A Titre non disponibleJPH04135271A Titre non disponibleJPH04353970A Titre non disponibleJPH05189406A Titre non disponibleJPH06149833A Titre non disponible* Cit� par l'examinateurCitations hors brevetsR�f�rence1"Origins of Option Pricing Techniques," "The Black and Scholes Model," "The Black and Scholes Model," and "Graphs of the Black Scholes Model," as printed from http://bradley.bradley.edu/.about.arr/bsm, Apr. 9, 1997, (8 pages).2Banham, R., "Reinsurers Seek Relief in Computer Predictions", Aug. 1993, pp. 14-16, 18-19, XP002082269, p. 14, col. 1, line 1, col. 2, line 29.3Best, D.L. and Pryor, S.P., Air Weather Service Model Output Statistics System Project Report, United States Air Force, Entire Report submitted (Oct. 1983).4Brennan, P.J., "Portfolio Managers Weather Global Risk Management Challenge," Wall Street Computer Review, Dealer's Digest, pp. 20-22, 24, 54 and 56 (Oct. 1989).5Cave, T., "Weather Services is A Boon To System Dispatchers," Transmission & Distribution, Intertec, pp. 165-166 and 168-169 (Aug. 1991).6Copy of International Search Report from PCT Appl. No. PCT/US99/23452, 5 pages, mailed Jun. 4, 2000.7Demand Modeling & Forecasting System Product Description, Printed from DIALOG File No. 256, 1 page (Aug. 1984-Product Release Date).8Down to Earth Sales Analysis 3.1 Product Description, Printed from DIALOG File No. 256, 1 page (Apr. 1989-Product Release Date).9Ehrenberg, A.S.C. et al., "The After-Effects of Price-Related Consumer Promotion", Journal of Advertising Research, Advertising Research Foundation, vol. 34, No. 4, pp. 11-21 (Jul./Aug. 1994).10Engle, R.F. et al., "Modelling Peak Electricity Demand", Journal of Forecasting, John Wiley & Sons, vol. 11, No. 3, pp. 241-251 (Apr. 1992).11English-language Abstract of Japanese Patent Publication No. 01-238396, from http://www1.ipdl.jpo.go.jp, 1 Page (Sep. 21, 1989-Date of publication of application).12English-language Abstract of Japanese Patent Publication No. 01-259488, from http://www1.ipdl.jpo.go.jp, 1 Page (Oct. 17, 1989-Date of publication of application).13English-language Abstract of Japanese Patent Publication No. 02-268396, from http://www1.ipdl.jpo.go.jp, 1 Page (Nov. 2, 1990-Date of publication of application).14English-language Abstract of Japanese Patent Publication No. 02-299059, from http://www1.ipdl.jpo.go.jp, 1 Page (Dec. 11, 1990-Date of publication of application).15English-language Abstract of Japanese Patent Publication No. 04-077896, from http://www1.ipdl.jpo.go.jp, 1 Page (Mar. 11, 1992-Date of publication of application).16English-language Abstract of Japanese Patent Publication No. 04-135271, from http://www1.ipdl.jpo.go.jp, 1 Page (May 8, 1992-Date of publication of application).17English-language Abstract of Japanese Patent Publication No. 04-353970, from http://www1.ipdl.jpo.go.jp, 1 Page (Dec. 8, 1992-Date of publication of application).18English-language Abstract of Japanese Patent Publication No. 05-189406, from http://www1.ipdl.jpo.go.jp, 1 Page (Jul. 30, 1993-Date of publication of application).19English-language Abstract of Japanese Patent Publication No. 06-076161, from http://www1.ipdl.jpo.go.jp, 1 Page (Mar. 18, 1994-Date of publication of application).20English-language Abstract of Japanese Patent Publication No. 06-149833, from http://www1.ipdl.jpo.go.jp, 1 Page (May 31, 1994-Date of publication of application).21Gotschall, Mary G., "Bullish on weather," Electric Perspectives, Washington, vol. 23, No. 5, p. 30, 8 pgs (Sep./Oct. 1998).22Hunter, R., "Forecast for Weather Derivatives: Hot Derivatives Strategy," May 1999, pp. 1-6, XP002133864, as printed from http://derivatives.com/magazine/arrive/1998/0598feal.asp> p. 1, line 1-p. 6, line 9.23Hurrell, M., "The Weather Business," Intercity, pp. 29, 31 and 32 (Feb. 1991).24IMREX Demand Forecasting System Product Description, Printed from DIALOG File No. 256. 1 page (1984-Product Release Date).25Jensen, C, and Anderson, L. Harvard Graphics: The Complete Reference, Osborne McGraw-Hill, pp. 5, 16, 17, 126-129 and 737-747 (1990).26Lucchetti, A., "Cold Winter On the Way? Some bet on it," Wall Street Journal, Nov. 6, 1997.27Malliaris, M., "Beating the Best: A Neutral Network Challenges the Black-Scholes Formula," Proceedings of the Conference on Artificial Intelligence for Applications, US, Los Alamitos, IEEE Comp. Soc. Press, 1993, pp. 445-449, XP000379639, ISBN: 0-8186-3840-0, p. 445, col., 1, line 16, p. 446, col. 1, line 17.28Microsoft Access User's Guide, Microsoft Corporation, pp. 22-27, 36-39, 326-335, 370-372 and 395-447 (1992).29Microsoft Excel User's Guide, Microsoft Corporation, pp. 280-281, 596-601 and 706-709 (1993).30Mitchell, R. et al., "Where No Computer Has Gone Before: Massively Parallel Processing Promises Unparalleled Performance," Business Week, McGraw-Hill, pp. 80-84 and 88 (Nov. 25, 1991).31 *Natural Gas Trends, Jul. 28, 2003, Williams, Michael.32Schwartz, S., "Modeling tools aid in finacial risk management," Insurance & Technology, vol. 21, No. 4, pp. 20-21 (Apr. 1996).33Stix, G., "A Calculus of Risk," Scientific American, pp. 92-97 (May 1998).34Studwell, A., "Weather Derivatives," 11<SUP>th </SUP>Conference on Applied Climatology, Jan. 10-15, 1999, pp. 36-40, XP00089822, p. 36, col. 1, line 1-p. 40, col. 1, line 33.35The Weather Initiative, (Brochure), The Met Office, 23 pages (1990).36Turvey, C. G., "Weather Derivatives and Specific Event Risk,", Aug., 1999, pp. 1-11, XP002133865, as printed from http://agecon.lib.umn.edu/aaea99/sp99/sp99/tu02.pdf>, p. 2, line 1, p. 8, line 2.37 *Turvey, Calum G.; Weather Derivatives and Specific Event Risk, Aug. 1999, pp. 1-11.38Turvey, Calum, "Weather Derivatives for Specific Event Risks in Agriculture," Review of Agricultural Economics, American Agricultural Economics Associaton, vol. 23, No. 2, pp. 333-351 (Spring/Summer 2001).39Upbin, B., "Betting against God," Forbes, vol. 162, No. 1, p. 108(1) (Jul. 6, 1998).* Cit� par l'examinateur R�f�renc� par Brevet citant Date de d�p�t Date de publication D�posant TitreUS7337122 *14 janv. 200326 f�vr. 2008Mirant Americas, Inc.Method for producing a superior insurance model for commodity event riskUS7676420 *19 juin 20039 mars 2010Accenture Global Services GmbhElectronic settlement of petroleum and gas distributionsUS79455008 avr. 200817 mai 2011Pricelock, Inc.System and method for providing an insurance premium for price protectionUS79455018 avr. 200817 mai 2011Pricelock, Inc.System and method for constraining depletion amount in a defined time frameUS801969412 f�vr. 200813 sept. 2011Pricelock, Inc.System and method for estimating forward retail commodity price within a geographic boundaryUS806521831 mars 201122 nov. 2011Pricelock, Inc.System and method for providing an insurance premium for price protectionUS8069107 *23 avr. 200829 nov. 2011Cheniere Energy, Inc.Transaction gatewayUS808651731 mars 201127 d�c. 2011Pricelock, Inc.System and method for constraining depletion amount in a defined time frameUS815602212 f�vr. 200710 avr. 2012Pricelock, Inc.Method and system for providing price protection for commodity purchasing through price protection contractsUS816095212 f�vr. 200917 avr. 2012Pricelock, Inc.Method and system for providing price protection related to the purchase of a commodityUS826046825 juin 20094 sept. 2012Versify Solutions, Inc.Aggregator, monitor, and manager of distributed demand responseUS827571919 janv. 201025 sept. 2012Accenture Global Services LimitedElectronic settlement of petroleum and gas distributionsUS853879512 f�vr. 200817 sept. 2013Pricelock, Inc.System and method of determining a retail commodity price within a geographic boundaryUS8606686 *6 mars 200910 d�c. 2013Versify Solutions, Inc.System and method for gathering and performing complex analyses on power data from multiple remote sourcesUS876194827 avr. 200924 juin 2014Versify Solutions, Inc.System and method for managing and monitoring renewable energy power generationUS89657196 mars 200924 f�vr. 2015Versify Solutions, Inc.Universal performance monitor for power generatorsUS20050010481 *8 juil. 200313 janv. 2005Lutnick Howard W.Systems and methods for improving the liquidity and distribution network for illiquid items* Cit� par l'examinateurClassifications Classification aux �tats-Unis705/35, 705/37, 705/317 Classification internationaleG06Q40/00, G06Q30/00, G06F19/00 Classification coop�rativeG06Q40/00, G06Q30/018, G06Q40/04 Classification europ�enneG06Q40/04, G06Q30/018, G06Q40/00�v�nements juridiques DateCode�v�nementDescription25 mars 2014FPAYFee paymentYear of fee payment: 813 juil. 2010SULPSurcharge for late payment13 juil. 2010FPAYFee paymentYear of fee payment: 415 d�c. 2000ASAssignmentOwner name: PLANALYTICS, INC., PENNSYLVANIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MACHADO, ROBERTO C., JR.;CORBY, PAUL M.;FRECH, BRUCE;REEL/FRAME:011374/0510Effective date: 20001128Faire pivoterImage d'origineAccueil Google - Plan du site - T�l�chargements par lot sur l'USPTO - R�gles de confidentialit� - Conditions d'utilisation - � propos de Google�Brevets - Envoyer des commentairesDonn�es fournies par IFI CLAIMS Patent Services