# Research on Interbank Offered Rate Based on Embedded Wireless Communication

Authors: Yaming Lei
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the Wi-Fi (Wireless Fidelity) standard and the increase of wireless access points, wireless data communication based on 802.11 has become increasingly popular. Interest rate risk is a kind of financial risk. In essence, interest rate risk is caused by changes in the price or income of financial products caused by changes in interest rates. As the hot spot of the wireless network, the self-organizing network will inevitably become a development trend to realize voice communication on its market-oriented core interest rate; the ARIMA model is further proposed to improve the prediction accuracy and better fit the daily fluctuation of the Shibor overnight. The monthly average value of Shibor is predicted overnight, and monthly mean prediction of Shibor is overnight. Finally, the overnight Shibor decision-making system is established, and the forecasting model is integrated to guide the participants in the money market.


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## Body

## 1. Introduction

In recent years, with the continuous acceleration of the interest rate marketization process, the use of interest rate tools to carry out macrocontrol has become increasingly frequent, the interest rate environment volatility of commercial banks’ operations has continued to increase, and interest rate risks have become more prominent. Specifically, the traditional cable is no longer used when building a local area network, and infrared, radio waves, etc., are used as transmission media to connect wirelessly, providing all the functions of a wired local area network. China’s interbank lending rate is one of the important currency reference rates in China’s financial market. Judging from the impact of the interbank lending rate market being very large, the interbank lending rate is related to the bank deposit reserve ratio, so its frequency of change is much more obvious than the interest rate changes in other markets. In summary, interbank lending is in a timely and effective manner. Participants in the financial market will also pay close attention to changes in interbank lending rates to determine trends in other interest rates in the money market. The interbank lending market will directly affect the overall operation of the entire financial market. At present, the frequency band used by wireless LAN is mainly S-band.The integration of wireless local area network technology in embedded systems to realize wireless communication and data transmission will become a hot spot in future applications. For example, wireless digital set-top boxes, computers, wireless gateways, and household appliances can form a home wireless local area network and can be connected to the Internet through APs, wireless routers, or wireless bridges; wireless instruments perform data acquisition and wireless transmission; wireless instruments and equipment are arranged on the work site self-organizing network (ad hoc network), carry on the mutual information transmission and the long-distance wireless monitoring, reduces the wiring trouble and the inconvenience, will greatly improve the industrial production efficiency, and are convenient for people’s life. Based on the impact of interbank peer-to-peer interest rates on the overall operation of the entire financial market, many research teams at home and abroad have conducted various researches on this. Ming [1] studied and predicted the interbank interest rate. Lu [2] found that under the influence of changes in the domestic and international economic situation, the development characteristics that conform to China’s national conditions have been formed. At present, the domestic real estate financial market is affected by factors such as the state’s macrocontrol policies, land policies, and interest rate mechanisms. The domestic real estate financial market presents a special development trend under the influence of complex factors. Wang [3] found studying the impact mechanism of monetary policy for stabilizing housing prices and promoting stable economic development. Liang [4] analyzed the main risks of Chinese commercial banks. Hui and others [5] found that in the aspect of compensation spillover, with the promotion of the capital market opening policy, the linear guiding relationship between domestic and foreign interbank interest rates is becoming more and more obvious, and when the market is in an abnormal period, the bank’s interest rate has become the information center; in terms of volatility spillovers, domestic volatility spillovers have always existed. Over time, the development of offshore markets has become more mature, and offshore interest rates have also begun to generate onshore interest rates. Fluctuations spilled out and were reflected in more and more interest rate varieties. Ming and others [6] found that tools will significantly affect the overnight Shibor through liquidity scale channels and currency multiplier channels; overnight Shibor historical changes and CPI year-on-year growth rates will also significantly affect overnight Shibor. Jihong and Zhigao [7] analyzed the dynamics of the overnight, one-week, and one-month interest rate varieties of the Shanghai Interbank Offered Rate Market from 2006 to 2014. The conclusion shows that the short-term interest rate has significant drift term nonlinearity and weakens with the extension of the term; the asymmetric autocorrelation of the diffusion term and the discontinuity of the jump term also exist remarkably. Based on the interest rate swap spread analysis, Wei and Xiaolan [8] explored the arbitrage model of the interbank market repurchase bond and interest rate swap combination, theoretically calculated the arbitrage space, and empirically studied the arbitrage through the dynamic panel model, which are the factors affecting the spread. Xinyue [9] explores the factors affecting the interbank lending rate in Shanghai and draws the following conclusions: the seven-day bond repurchase rate and money supply of the variables selected in this paper are the reasons for the change of Shibor, and the consumer price index is not the cause of Shibor change. Jiang and Zhongqiao [10] used a time series model to empirically study the seven-day interbank offered rate in China’s interbank borrowing market and concluded that the overall interbank lending market interest rate data series is relatively stable, which also explains that its mean recovery phenomenon is more significant. Yanmei [11] established the cointegration equation and error correction model (VEC); used impulse response analysis and variance decomposition to explore the influence, impact degree, and time lag of each variable on China’s market; and obtained the promotion of interest rate marketization in China. In the process of reform, we must pay more attention to the conclusion that government expenditure has an impact on the positive impact of interest rates. Guochang [12] used the VAR model and the variance contribution index to measure the price correlation and risk contagion between the P2P online loan market and the interbank borrowing market and capital market. The study found that the one-stage lag of the P2P online lending market interest rate has a positive impact on the interbank lending rate market, and the second period lag has a negative impact. Jianfa [13] used the VaR model for empirical analysis. The empirical study used the interbank borrowing market interest rate as the observation data. The GARCH, TARCH, and EGACH models based on the normal distribution, t distribution, and GED distribution hypothesis were used to estimate the following conclusions: (1) The TARCH model based on GED distribution has the best fitting effect; (2) the t distribution hypothesis has the possibility of overestimating the interest rate risk. Hua and Xiaojun [14] constructed the ARMA model with higher autoregressive order p and partial autoregressive order q, based on the existing literature on the Chinese Interbank Offered Rate (CHIBOR) study, on the Shanghai Bank. The interbank lending rate (Shibor) is used to estimate and predict, and the ARMA is tested.In the study of interbank interest rates, although they run faster, they do not have comprehensive characteristics. Second, the algorithm used has low accuracy. The computational process requires the identification process to be fast, accurate, and easy to control and to accurately and quickly analyze the features being studied. In this case, artificial intelligence algorithms have emerged, and artificial intelligence algorithms have the advantages of simplicity, speed, high accuracy, and strong operability, which are not available in other algorithms.Aiming at the superiority of artificial intelligence algorithms, they were researched and applied in various fields and achieved good results. Yuan and others [15] used artificial intelligence algorithms in classroom teaching to further deepen their understanding of genetic algorithm concepts and cultivate their information awareness and computational thinking by allowing students to simulate the performance of genetic algorithms and the practice of the machine. Haisheng and Chaoping [16] combine the artificial intelligence algorithm with publishing fusion and propose that the future development of artificial intelligence and publishing needs to be promoted at the four levels of technology, data, algorithms and talents, and the effective supply method of innovative artificial intelligence and publishing integration development. A talent training system that supports the integration of artificial intelligence and publishing was constructed. Feng [17] puts artificial intelligence and philosophy together and finds that when the philosophy of artificial intelligence has its own uniqueness, human philosophical research may benefit from it, and it may also make people lose the function of engaging in philosophical research. Interestingly, there is a “singularity event” in the field of philosophy. Ling and Xinwei [18] applied artificial intelligence to meteorological research to compare and analyze the gap between China and Europe and the United States. At the same time, it enumerated real cases of machine learning and discussed the application of artificial intelligence in meteorology and feasibility. Jing [19] added the artificial intelligence algorithm in the field of intelligent transportation, analyzed the development and characteristics of the intelligent transportation system, expounded the transportation, summarized the intelligent transportation system for the intersection of the two, and put forward the idea of intelligent transportation in the future. Jingli and Bingda [20] applied the artificial intelligence algorithm to the prediction of ancient building life, combined with the global optimization ability of the artificial bee colony (ABC) algorithm and the nonlinear fitting ability of the Elman neural network, and established the service life prediction for ancient buildings. The ABC-Elman model was used for comparative analysis, and the BP neural network model and the Elman model were established. Shiwen [21] applied the artificial intelligence algorithm in the Gobang chess and used the minimax algorithm to optimize the whole process of the whole game tree search, so that the man-machine game can be realized based on the control game search depth and the primary and advanced level settings. Zhenhai and Zhong [22] combine the artificial intelligence algorithm and wireless clock synchronization algorithm. Based on the traditional TPSN protocol, a wireless clock synchronization algorithm combining artificial intelligence-depth learning is proposed. Deep learning is used to continuously repeat the learning characteristics and reduce error. The proposed algorithm achieves higher clock synchronization accuracy than the traditional TPSN protocol, and the clock synchronization performance is greatly improved. Xiaodong [23] uses artificial intelligence in the music field and can compose the algorithm according to the aesthetic principles of music. Shouyi et al. [24] analyzed the types, characteristics, technical routes, and market conditions of existing artificial intelligence chips, expounded on the opportunities and challenges faced by artificial intelligence chips, and looked forward to their future development trends. Yangming [25] creatively classified artificial intelligence. Then, through the overview of the artificial intelligence algorithm, the general development law of artificial intelligence was summarized. Then, the defects of the current artificial intelligence were analyzed. Finally, artificial intelligence was elaborated. There are two major trends in future development: autonomy and biological characteristics (Khalaf et al. [26–28]).The ARIMA model is used as the weight optimization algorithm. The combined model can combine the three basic model prediction advantages (linear and nonlinear), make full use of the sample information of each basic model, and effectively reduce the single model in the interest rate prediction process. There are limitations in the middle. Finally, the predicted values of the combined model are compared with the real values. The empirical algorithm can effectively improve the prediction accuracy and reduce the error. The combined forecasting model can effectively predict the interbank lending rate in the interbank lending market in Shanghai and China, which has certain practicability and scientificity (Adil et al. [29–31]).


## 2. Method

### 2.1. BP Neural Network Prediction Model

The artificial neural network does not need to determine the mathematical equation of the mapping relationship between the input and output in advance. It only learns certain rules through its own training and obtains the result closest to the expected output value when the input value is given. As an intelligent information processing system, the core of the artificial neural network to realize its function is the algorithm. The BP neural network is a multilayer feedforward network trained by error back propagation (referred to as error back propagation). Its algorithm is called the BP algorithm. Its basic idea is the gradient descent method, which uses gradient search technology to make the network The error mean square error between the actual output value and the expected output value is the smallest.The topology of the BP neural network (an implicit layer) is shown in Figure1.
(1)
Implicit layer excitation functionf:
(1)fx=11+e−x(2)
Implicit layer outputH:
(2)Hj=f∑i=1nωijxi−aj,j=1,2,⋯,l(3)
Predicted outputO:
(3)Ok=∑j=1lHjωjk−bk,k=1,2,⋯,m(4)
Calculate the prediction errore, Y is the expected output:
(4)ek=Yk−Ok,k=1,2,⋯,m(5)
Weight update:(5)ωij=ωij+ηHj1−Hjxi∑k=1mωjkek,i=1,2,⋯,n,ωjk=ωjk+ηHjek,j=1,2,⋯,lk=1,2,⋯,m(6)
Thresholda,b update:
(6)aj=aj+ηHj1−Hj∑k=1mωjkekj=1,2,⋯,l,bk=bk+ekk=1,2,⋯,mFigure 1
BP network topology diagram.In the above expression,n is the number of input layer nodes and m is the number of output layer nodes.


### 2.2. ARIMA Model

The ARMA model is a hybrid model based on difference and moving average.


#### 2.2.1. AR Example Model

The time seriesyt can be expressed as
(7)yt=ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+εn,where εn is an independent random variable and Eεn=0,Varεn=σε2>0 is satisfied. Autoregressive model ARp:ϕ1,ϕ2,⋯,ϕp is an autoregressive coefficient.


#### 2.2.2.MAq Model

If the time seriesyt is satisfied,
(8)yt=ut−θ1ut−1−θ2ut−2−⋯−θqut−q.MAq,θ1,θ2,⋯,θq is the moving average coefficient.


#### 2.2.3. ARMAp,q Model

The time seriesyt satisfies the linear combination of its previous value and the current and previous values of the random item, namely,
(9)yt=ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+ut−θ1ut−1−θ2ut−2−⋯−θqut−q.And the above equationϕ1,ϕ2,⋯,ϕp is called ARMAp,q. Among them, the autoregressive coefficient θ1,θ2,⋯,θq is the moving average coefficient. Whenϕi=0i=1,⋯,p⟶, the model changes to the MAq model; when θj=0j=Lhq, the model is transformed into the ARp model.


#### 2.2.4. ARIMAp,d,q Model

In theARMAp,q model, yt becomes a stationary time series after passing the d-order difference; then, we can model the smoothed sequence; at this time, ARMAp,q is called the differential autoregressive moving average model, referred to as the ARIMA (p,d,q) model. The model was proposed as early as 1938 and is documented in the book Time Series, which was coauthored by George Box and Gnilymjenkins. It was originally called the Box-Jenkins model. The profit relationship of the bank is shown in Figure 2.Figure 2
The profit relationship of the bank.


### 2.3. Regression Support Vector Machine (SVR) Prediction Model

The application of SVM to interbank lending rate forecasting has the following advantages: It has a solid mathematical theoretical foundation and is specially proposed for the small sample learning problem. In essence, the algorithm is a convex quadratic programming problem, which can ensure that the obtained solution is the global optimum The solution adopts the kernel function method, which effectively solves the complex calculation problem and applies the principle of structural risk minimization and has better generalization ability.The basic idea of the SVM method is to define the optimal linear hyperplane, and the algorithm of finding the optimal linear hyperplane is reduced to solving a convex programming problem. Then, based on the kernel expansion theorem, through nonlinear mapping, the sample space is mapped to a high-dimensional or even infinite-dimensional feature space, so that the linear learning machine method can be applied in the feature space to solve the highly nonlinear classification in the sample space and regression issues. Simply put, it is dimension-raising and linearization.Support vector machine is an approximation method for structural risk minimization based on the two theories of VC dimension and expected risk minimization in statistical learning. Statistical learning theory is a theory that studies the statistical estimation and prediction of small samples, mainly to study the statistical laws and the nature of learning methods in the case of small samples. Under these conditions, the conclusions about the generalizable bounds of statistical learning methods are based on the small-sample inductive reasoning criteria established on the basis of these bounds and are practical methods for implementing new criteria.Support vector machine is developed from the optimal classification surface in the case of linear separability. It can identify any sample without error according to the complexity of the model based on the limited sample information, that is, the learning accuracy and learning ability of a specific training sample. Seek the best compromise between capabilities, in order to obtain the best promotion performance. The support vector machine maps the input vector to a high-dimensional feature space through some preselected nonlinear mapping and performs nonlinear fitting in this feature space. Formally, the SVM output is a linear combination of intermediate nodes, each of which corresponds to a support vector.The global optimal solution is obtained, and the local pole of the traditional neural network is improved. The value problem is circumventing the excessive dependence of traditional neural networks on users repeatedly trying to try out network topology. The business and profit model is shown in Figure3.Figure 3
The business and profit model.When the SVM is used to classify the problem, an optimal classification plane is constructed; the idea is to construct a minimum distance from all the training samples. The optimal classification plane converts. The basic idea of SVR is shown in Figure4.
(1)
Given training set sample pairs,T=xi,yi,i=1,2,⋯,l,xi∈Rd,yi∈R, establish (linear)(10)fx=wΦx+b,where w represents a weight vector Φx representing a nonlinear mapping function and b is an offset.Figure 4
Schematic diagram of the basic idea of SVR.In this article,φw1,w2 is assumed:
(11)ϕw1,w2=fk/K,w1,w2∑k/K,w1,w2.In addition,(12)Fx,a,b=xa−11−x∂a+b+∂b∂a+∂b.In logarithmic processing, we get(13)logη=mt+θ∑k=1ϕw1,w2X.The long-term component equations of the level-effect and volatility-effect models are(14)ϕt=m+∑n=1θ∑n=1δw1,w2Xmn.(2)
Define theC-linear insensitive loss function:
(15)Lfx,y,ε=0,y−fx≤ε,y−fx−ε,y−fx>ε,where y is the corresponding true value and ε is the insensitive coefficient that controls the fitting accuracy.(3)
The regression function is(16)fx=∑i=1lai−ai∗ΦxiΦxj+b∗=∑i=1lai−ai∗Kxi,xj+b∗.The expression of the distributed lag model is as follows:(17)Y=β0+BLX+ε.The specific expression of its MIDAS model is as follows:(18)YT=β0+BLXTM+εTM.Express the yield shock as(19)RI,T−EI,TRI,T=κZGλ.The short-term daily high-frequency time series can be organized as(20)RI,T=μ+ϕGδ.Short-term high-frequency volatility:(21)RV=∑Ri,t2.Then, it can be expressed as(22)κt=m+θ∑k=1Kβw1,w2RVt−k,where K represents the long-term low-frequency variables.The SVM method is a novel statistical learning method. Its modeling process is completely different from traditional statistical methods. It does not involve probability measurement at all but is based on the principle of interval maximization. The explicit expression of nonlinear mapping is avoided due to the introduction of the kernel function, and its computational cost is comparable to that of the linear regression method. Compared with other existing nonlinear regression methods, the SVM regression algorithm is simple, the amount of calculation is small, and it is easy to implement, and it can solve large-scale problems.


### 2.4. Particle Swarm Optimization Algorithm (PSO)

It is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. It is generally considered a type of swarm intelligence. It can be incorporated into multiagent optimization systems.The PSO algorithm simulates behavioral characteristics to optimize the control of the process. The algorithm first randomly initializes a set of potential solutions representing the problem. Each potential solution is a particle. The particle has no volume and quality, only two essential features of speed and position. In the solution space, each particle dynamically adjusts its speed. The speed further determines how the particles move. The specific implementation process is establishing the fitness value calculated by the fitness function as an evaluation index indicating the merits and demerits of the particles, and the position update of the particles is based on the individual extreme value Pbest and the group extreme value Gbest, wherein the individual extreme value Pbest represents the optimal position of fitness in all positions experienced by the individual particles; the population extreme value Gbest represents the optimal position of fitness found by all particles in the population. The corresponding fitness value is calculated after the particle is replaced once, and the fitness value calculated by the new particle after the position update is compared with the original individual extreme value and the group extreme value until the termination condition is satisfied. The PSO algorithm thus implicitly searches in parallel to find the optimal solution to the problem.Compared with the genetic algorithm, the particle swarm optimization algorithm has the same points as the following: the population is randomly initialized; the fitness value is calculated for each individual in the population, and the fitness value is directly related to the distance of the optimal solution; the population is replicated according to the fitness value.Compared with the genetic algorithm, the information sharing mechanism of PSO is very different. In the genetic algorithm, chromosomes share information with each other, so the movement of the entire population is relatively uniform to the optimal area. In PSO, only gBest gives information to other particles, which is a one-way information flow. The entire search update process is a process of following the current optimal solution. Compared with the genetic algorithm, in most cases, all particles may converge to the optimal solution faster.Figure5 is a flow chart of the PSO algorithm for function extremum optimization.Figure 5
PSO algorithm optimization flow chart.


### 2.5. Interbank Offered Rate

The interbank lending rate generally represents the cost of obtaining wholesale funds for financial institutions and can reflect the supply and demand of funds in a timely manner. It is the most sensitive money market interest rate and plays a guiding role in the interest rate structure of the entire financial market. The interbank offered rate has good conductivity, and its changes will quickly spread and be transmitted to the entire market interest rate system, causing changes in the entire market interest rate. The interbank market interest rate has a restrictive effect on the formation of other money market instrument interest rates and also provides an important cost standard for commercial banks, which is an important standard for commercial banks to determine loan interest rates and deposit interest rates.Saving and investing are the main determinants of interest rates. Therefore, when analyzing the influencing factors of interest rates, it is essential to choose indicators that reflect savings and investment. Savings are reflected by deposits, and investments can be reflected by credit scale. Therefore, the net amount of deposits and loans is an important factor affecting the level of interbank offered rates.The impact of the exchange rate on the interest rate is indirect, which is mainly caused by the nonequilibrium changes in the balance of payments. The balance of international payments is mainly composed of two major parts: one is the current account balance and the other is the capital account balance. If a country’s balance of payments has a long-term surplus, foreign exchange will inevitably exceed demand, and the local currency will appreciate externally. With the exchange rate of the local currency, the country will inevitably convert the balance of payment surplus into foreign exchange reserves. With the increase of foreign exchange reserves, the supply of local currency will increase, the money supply will expand, and the money supply will be loosened, thereby forcing interest rates to go down. Changes in exchange rates require corresponding changes in interest rates, so the adjustment of interest rates should at least consider the objective requirements caused by changes in exchange rates.One of the most important macrofactors affecting the level of interest rates is the gross domestic product of a country. Generally speaking, interest rates and economic development are procyclical. In the stage of economic prosperity, people are full of confidence in the prospects of the economy, the expected rate of return on capital will be high, and the demand for capital by enterprises and individuals is higher than the market supply, so that interest rates will continue to rise with economic growth.


## 2.1. BP Neural Network Prediction Model

The artificial neural network does not need to determine the mathematical equation of the mapping relationship between the input and output in advance. It only learns certain rules through its own training and obtains the result closest to the expected output value when the input value is given. As an intelligent information processing system, the core of the artificial neural network to realize its function is the algorithm. The BP neural network is a multilayer feedforward network trained by error back propagation (referred to as error back propagation). Its algorithm is called the BP algorithm. Its basic idea is the gradient descent method, which uses gradient search technology to make the network The error mean square error between the actual output value and the expected output value is the smallest.The topology of the BP neural network (an implicit layer) is shown in Figure1.
(1)
Implicit layer excitation functionf:
(1)fx=11+e−x(2)
Implicit layer outputH:
(2)Hj=f∑i=1nωijxi−aj,j=1,2,⋯,l(3)
Predicted outputO:
(3)Ok=∑j=1lHjωjk−bk,k=1,2,⋯,m(4)
Calculate the prediction errore, Y is the expected output:
(4)ek=Yk−Ok,k=1,2,⋯,m(5)
Weight update:(5)ωij=ωij+ηHj1−Hjxi∑k=1mωjkek,i=1,2,⋯,n,ωjk=ωjk+ηHjek,j=1,2,⋯,lk=1,2,⋯,m(6)
Thresholda,b update:
(6)aj=aj+ηHj1−Hj∑k=1mωjkekj=1,2,⋯,l,bk=bk+ekk=1,2,⋯,mFigure 1
BP network topology diagram.In the above expression,n is the number of input layer nodes and m is the number of output layer nodes.


## 2.2. ARIMA Model

The ARMA model is a hybrid model based on difference and moving average.


### 2.2.1. AR Example Model

The time seriesyt can be expressed as
(7)yt=ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+εn,where εn is an independent random variable and Eεn=0,Varεn=σε2>0 is satisfied. Autoregressive model ARp:ϕ1,ϕ2,⋯,ϕp is an autoregressive coefficient.


### 2.2.2.MAq Model

If the time seriesyt is satisfied,
(8)yt=ut−θ1ut−1−θ2ut−2−⋯−θqut−q.MAq,θ1,θ2,⋯,θq is the moving average coefficient.


### 2.2.3. ARMAp,q Model

The time seriesyt satisfies the linear combination of its previous value and the current and previous values of the random item, namely,
(9)yt=ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+ut−θ1ut−1−θ2ut−2−⋯−θqut−q.And the above equationϕ1,ϕ2,⋯,ϕp is called ARMAp,q. Among them, the autoregressive coefficient θ1,θ2,⋯,θq is the moving average coefficient. Whenϕi=0i=1,⋯,p⟶, the model changes to the MAq model; when θj=0j=Lhq, the model is transformed into the ARp model.


### 2.2.4. ARIMAp,d,q Model

In theARMAp,q model, yt becomes a stationary time series after passing the d-order difference; then, we can model the smoothed sequence; at this time, ARMAp,q is called the differential autoregressive moving average model, referred to as the ARIMA (p,d,q) model. The model was proposed as early as 1938 and is documented in the book Time Series, which was coauthored by George Box and Gnilymjenkins. It was originally called the Box-Jenkins model. The profit relationship of the bank is shown in Figure 2.Figure 2
The profit relationship of the bank.


## 2.2.1. AR Example Model

The time seriesyt can be expressed as
(7)yt=ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+εn,where εn is an independent random variable and Eεn=0,Varεn=σε2>0 is satisfied. Autoregressive model ARp:ϕ1,ϕ2,⋯,ϕp is an autoregressive coefficient.


## 2.2.2.MAq Model

If the time seriesyt is satisfied,
(8)yt=ut−θ1ut−1−θ2ut−2−⋯−θqut−q.MAq,θ1,θ2,⋯,θq is the moving average coefficient.


## 2.2.3. ARMAp,q Model

The time seriesyt satisfies the linear combination of its previous value and the current and previous values of the random item, namely,
(9)yt=ϕ1yt−1+ϕ2yt−2+⋯+ϕpyt−p+ut−θ1ut−1−θ2ut−2−⋯−θqut−q.And the above equationϕ1,ϕ2,⋯,ϕp is called ARMAp,q. Among them, the autoregressive coefficient θ1,θ2,⋯,θq is the moving average coefficient. Whenϕi=0i=1,⋯,p⟶, the model changes to the MAq model; when θj=0j=Lhq, the model is transformed into the ARp model.


## 2.2.4. ARIMAp,d,q Model

In theARMAp,q model, yt becomes a stationary time series after passing the d-order difference; then, we can model the smoothed sequence; at this time, ARMAp,q is called the differential autoregressive moving average model, referred to as the ARIMA (p,d,q) model. The model was proposed as early as 1938 and is documented in the book Time Series, which was coauthored by George Box and Gnilymjenkins. It was originally called the Box-Jenkins model. The profit relationship of the bank is shown in Figure 2.Figure 2
The profit relationship of the bank.


## 2.3. Regression Support Vector Machine (SVR) Prediction Model

The application of SVM to interbank lending rate forecasting has the following advantages: It has a solid mathematical theoretical foundation and is specially proposed for the small sample learning problem. In essence, the algorithm is a convex quadratic programming problem, which can ensure that the obtained solution is the global optimum The solution adopts the kernel function method, which effectively solves the complex calculation problem and applies the principle of structural risk minimization and has better generalization ability.The basic idea of the SVM method is to define the optimal linear hyperplane, and the algorithm of finding the optimal linear hyperplane is reduced to solving a convex programming problem. Then, based on the kernel expansion theorem, through nonlinear mapping, the sample space is mapped to a high-dimensional or even infinite-dimensional feature space, so that the linear learning machine method can be applied in the feature space to solve the highly nonlinear classification in the sample space and regression issues. Simply put, it is dimension-raising and linearization.Support vector machine is an approximation method for structural risk minimization based on the two theories of VC dimension and expected risk minimization in statistical learning. Statistical learning theory is a theory that studies the statistical estimation and prediction of small samples, mainly to study the statistical laws and the nature of learning methods in the case of small samples. Under these conditions, the conclusions about the generalizable bounds of statistical learning methods are based on the small-sample inductive reasoning criteria established on the basis of these bounds and are practical methods for implementing new criteria.Support vector machine is developed from the optimal classification surface in the case of linear separability. It can identify any sample without error according to the complexity of the model based on the limited sample information, that is, the learning accuracy and learning ability of a specific training sample. Seek the best compromise between capabilities, in order to obtain the best promotion performance. The support vector machine maps the input vector to a high-dimensional feature space through some preselected nonlinear mapping and performs nonlinear fitting in this feature space. Formally, the SVM output is a linear combination of intermediate nodes, each of which corresponds to a support vector.The global optimal solution is obtained, and the local pole of the traditional neural network is improved. The value problem is circumventing the excessive dependence of traditional neural networks on users repeatedly trying to try out network topology. The business and profit model is shown in Figure3.Figure 3
The business and profit model.When the SVM is used to classify the problem, an optimal classification plane is constructed; the idea is to construct a minimum distance from all the training samples. The optimal classification plane converts. The basic idea of SVR is shown in Figure4.
(1)
Given training set sample pairs,T=xi,yi,i=1,2,⋯,l,xi∈Rd,yi∈R, establish (linear)(10)fx=wΦx+b,where w represents a weight vector Φx representing a nonlinear mapping function and b is an offset.Figure 4
Schematic diagram of the basic idea of SVR.In this article,φw1,w2 is assumed:
(11)ϕw1,w2=fk/K,w1,w2∑k/K,w1,w2.In addition,(12)Fx,a,b=xa−11−x∂a+b+∂b∂a+∂b.In logarithmic processing, we get(13)logη=mt+θ∑k=1ϕw1,w2X.The long-term component equations of the level-effect and volatility-effect models are(14)ϕt=m+∑n=1θ∑n=1δw1,w2Xmn.(2)
Define theC-linear insensitive loss function:
(15)Lfx,y,ε=0,y−fx≤ε,y−fx−ε,y−fx>ε,where y is the corresponding true value and ε is the insensitive coefficient that controls the fitting accuracy.(3)
The regression function is(16)fx=∑i=1lai−ai∗ΦxiΦxj+b∗=∑i=1lai−ai∗Kxi,xj+b∗.The expression of the distributed lag model is as follows:(17)Y=β0+BLX+ε.The specific expression of its MIDAS model is as follows:(18)YT=β0+BLXTM+εTM.Express the yield shock as(19)RI,T−EI,TRI,T=κZGλ.The short-term daily high-frequency time series can be organized as(20)RI,T=μ+ϕGδ.Short-term high-frequency volatility:(21)RV=∑Ri,t2.Then, it can be expressed as(22)κt=m+θ∑k=1Kβw1,w2RVt−k,where K represents the long-term low-frequency variables.The SVM method is a novel statistical learning method. Its modeling process is completely different from traditional statistical methods. It does not involve probability measurement at all but is based on the principle of interval maximization. The explicit expression of nonlinear mapping is avoided due to the introduction of the kernel function, and its computational cost is comparable to that of the linear regression method. Compared with other existing nonlinear regression methods, the SVM regression algorithm is simple, the amount of calculation is small, and it is easy to implement, and it can solve large-scale problems.


## 2.4. Particle Swarm Optimization Algorithm (PSO)

It is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. It is generally considered a type of swarm intelligence. It can be incorporated into multiagent optimization systems.The PSO algorithm simulates behavioral characteristics to optimize the control of the process. The algorithm first randomly initializes a set of potential solutions representing the problem. Each potential solution is a particle. The particle has no volume and quality, only two essential features of speed and position. In the solution space, each particle dynamically adjusts its speed. The speed further determines how the particles move. The specific implementation process is establishing the fitness value calculated by the fitness function as an evaluation index indicating the merits and demerits of the particles, and the position update of the particles is based on the individual extreme value Pbest and the group extreme value Gbest, wherein the individual extreme value Pbest represents the optimal position of fitness in all positions experienced by the individual particles; the population extreme value Gbest represents the optimal position of fitness found by all particles in the population. The corresponding fitness value is calculated after the particle is replaced once, and the fitness value calculated by the new particle after the position update is compared with the original individual extreme value and the group extreme value until the termination condition is satisfied. The PSO algorithm thus implicitly searches in parallel to find the optimal solution to the problem.Compared with the genetic algorithm, the particle swarm optimization algorithm has the same points as the following: the population is randomly initialized; the fitness value is calculated for each individual in the population, and the fitness value is directly related to the distance of the optimal solution; the population is replicated according to the fitness value.Compared with the genetic algorithm, the information sharing mechanism of PSO is very different. In the genetic algorithm, chromosomes share information with each other, so the movement of the entire population is relatively uniform to the optimal area. In PSO, only gBest gives information to other particles, which is a one-way information flow. The entire search update process is a process of following the current optimal solution. Compared with the genetic algorithm, in most cases, all particles may converge to the optimal solution faster.Figure5 is a flow chart of the PSO algorithm for function extremum optimization.Figure 5
PSO algorithm optimization flow chart.


## 2.5. Interbank Offered Rate

The interbank lending rate generally represents the cost of obtaining wholesale funds for financial institutions and can reflect the supply and demand of funds in a timely manner. It is the most sensitive money market interest rate and plays a guiding role in the interest rate structure of the entire financial market. The interbank offered rate has good conductivity, and its changes will quickly spread and be transmitted to the entire market interest rate system, causing changes in the entire market interest rate. The interbank market interest rate has a restrictive effect on the formation of other money market instrument interest rates and also provides an important cost standard for commercial banks, which is an important standard for commercial banks to determine loan interest rates and deposit interest rates.Saving and investing are the main determinants of interest rates. Therefore, when analyzing the influencing factors of interest rates, it is essential to choose indicators that reflect savings and investment. Savings are reflected by deposits, and investments can be reflected by credit scale. Therefore, the net amount of deposits and loans is an important factor affecting the level of interbank offered rates.The impact of the exchange rate on the interest rate is indirect, which is mainly caused by the nonequilibrium changes in the balance of payments. The balance of international payments is mainly composed of two major parts: one is the current account balance and the other is the capital account balance. If a country’s balance of payments has a long-term surplus, foreign exchange will inevitably exceed demand, and the local currency will appreciate externally. With the exchange rate of the local currency, the country will inevitably convert the balance of payment surplus into foreign exchange reserves. With the increase of foreign exchange reserves, the supply of local currency will increase, the money supply will expand, and the money supply will be loosened, thereby forcing interest rates to go down. Changes in exchange rates require corresponding changes in interest rates, so the adjustment of interest rates should at least consider the objective requirements caused by changes in exchange rates.One of the most important macrofactors affecting the level of interest rates is the gross domestic product of a country. Generally speaking, interest rates and economic development are procyclical. In the stage of economic prosperity, people are full of confidence in the prospects of the economy, the expected rate of return on capital will be high, and the demand for capital by enterprises and individuals is higher than the market supply, so that interest rates will continue to rise with economic growth.


## 3. Experiment

### 3.1. Data Source

The reason why Shibor was chosen as the research object is because the trading volume of the overnight Shibor is the largest in the Shanghai Interbank Lending Market, and it accounts for a considerable share. It is more practical to study the overnight Shibor. From 2010 to 2019, the overnight Shibor generated 2248 daily data, and the last 2,100 (January 4, 2007, to May 29, 2018) overnight Shibor data were used.


### 3.2. Experimental Platform

For the development of the model involved in this experiment, as well as the adjustment, training, and testing of the experimental process, it is operated in the environment of Table1.Table 1
Experimental environment.
Lab environmentSystem configurationOperating systemLinux 14.04CPUIntel Core i5-7300HQRAM8 GGraphics card4 GProgramming languagePython3.6Learning frameworkTensorflow1.2.1


### 3.3. Variable Selection

In the processing of data, the quotations for the month are calculated arithmetically, and the average value obtained represents the interest rate of the overnight Shibor for this month. Thus, from the 2010 to 2018 period of 9 years, the 108-month data of the overnight Shibor was obtained. Predict the decline in performance therefore, before establishing an overnight Shibor.


### 3.4. Evaluation Indicators

For predictive performance error evaluation, the forecast model of interbank interest rates in Shanghai, in addition to the comparative analysis of the data, a class of intuitive and quantifiable prediction accuracy evaluation criteria is needed.Mean Absolute Error (MAE) expression:(23)MAE=1n∑i=1nyt−y^t.Mean square error (MSE) expression:(24)MSE=1n∑i=1nyt−y∧t2.Mean Absolute Percentage Error (MAPE) expression:(25)MAPE=1n∑i=1nyt−y^tyt×100%.


## 3.1. Data Source

The reason why Shibor was chosen as the research object is because the trading volume of the overnight Shibor is the largest in the Shanghai Interbank Lending Market, and it accounts for a considerable share. It is more practical to study the overnight Shibor. From 2010 to 2019, the overnight Shibor generated 2248 daily data, and the last 2,100 (January 4, 2007, to May 29, 2018) overnight Shibor data were used.


## 3.2. Experimental Platform

For the development of the model involved in this experiment, as well as the adjustment, training, and testing of the experimental process, it is operated in the environment of Table1.Table 1
Experimental environment.
Lab environmentSystem configurationOperating systemLinux 14.04CPUIntel Core i5-7300HQRAM8 GGraphics card4 GProgramming languagePython3.6Learning frameworkTensorflow1.2.1


## 3.3. Variable Selection

In the processing of data, the quotations for the month are calculated arithmetically, and the average value obtained represents the interest rate of the overnight Shibor for this month. Thus, from the 2010 to 2018 period of 9 years, the 108-month data of the overnight Shibor was obtained. Predict the decline in performance therefore, before establishing an overnight Shibor.


## 3.4. Evaluation Indicators

For predictive performance error evaluation, the forecast model of interbank interest rates in Shanghai, in addition to the comparative analysis of the data, a class of intuitive and quantifiable prediction accuracy evaluation criteria is needed.Mean Absolute Error (MAE) expression:(23)MAE=1n∑i=1nyt−y^t.Mean square error (MSE) expression:(24)MSE=1n∑i=1nyt−y∧t2.Mean Absolute Percentage Error (MAPE) expression:(25)MAPE=1n∑i=1nyt−y^tyt×100%.


## 4. Results

### 4.1. ARIMA Empirical Analysis

This paper selects the empirical analysis of the ARMA forecasting model from the interbank lending market of the Shanghai Interbank Lending Market and the Interbank Lending Market from July 1, 2017, to June 30, 2018, and gradually implements the abovementioned ARMA model implementation steps to obtain forecasts. Table2 shows the multistep prediction accuracy of the ARIMA model.Table 2
ARIMA model multistep prediction accuracy.
Area1-step prediction2-step prediction3-step predictionMAERMSEMAPEMAERMSEMAPEMAERMSEMAPEChina0.06030.08992.36850.10160.14094.04530.14530.18605.8233Shanghai0.05760.8562.32290.09980.14054.02680.14360.18435.8395It can be seen from Table2 that the ARIMA model is used to predict the interbank lending rate between China and Shanghai. The 1-step prediction error (MAPE) is stable at around 2.3%, and the 2-step prediction error (MAPE) is stable at around 4.0%. 3-step prediction error (MAPE) is stable at around 5.8%. It can be seen from the data that the stability of the model is strong. They do not exceed 1% in the interbank lending forecast rate between China and Shanghai. It can be seen that the 1-step, 2-step, and 3-step predictions have different degrees of hysteresis, and the one-step prediction has the best change trend in the Shanghai bank lending rate very well (local maxima and minima), but the lag effect has not changed. The ARIMA model still has a certain lag for the interbank lending rate of the Chinese banks, and the one-step forecasting effect is the best, the closest to the true value. However, the forecasting effect of the starting point and the last point is not as good as that of the Shanghai Interbank Offered Rate, but the middle part is well fitted to the changing trend of the interbank offered rate. In summary, the ARIMA model performs well in loan interest rate forecasting, but the lag of the forecast needs to be strengthened. As the number of forecasting steps increases, the forecasting error becomes larger and larger.


### 4.2. BP Neural Network Result Analysis

Figure6 shows the prediction effect of the BP neural network on overnight Shibor.Figure 6
BP neural network prediction results.It can be seen from Figure6 that the fitting performance is general when the fluctuation is large.


### 4.3. Error Comparison Analysis

After analyzing the data, the sample is divided into two subsamples in 2015 to study the short-term interest rate behavior. The preliminary analysis results of the overall sample and subsample are shown in Table3. Table 3 lists the descriptive statistical results of interest rate samples and daily changes in interest rates and obtains the mean, standard deviation, skewness, kurtosis, and autocorrelation coefficient statistics of the two sets of data. It can be seen that the autocorrelation coefficient of the short-term interest rate is close to 1, and the first autocorrelation coefficients of the two interest rate subintervals are also close to 1. However, the two subcycles have significantly different values, standard deviations, and skewness. Prior to 2015, short-term interest rates had higher averages, standard deviations, and lower negative skewers. Table 4 gives a descriptive statistical result of the daily interest rate difference. It can be seen that the first autocorrelation coefficient of the sample and the 2006-2008 subsample is negative, and the first autocorrelation coefficient of the 2009-20 juxta sample is positive. The daily interest rate difference has a positive skewness, but the two subcycles have significantly different mean, standard deviation, skewness, and first autocorrelation coefficient.Table 3
Descriptive statistics of daily changes.
Spot rateMeanStdSkewnessKurtosisFirst autocorr2010-20180.010630410.01640591.3486983.1509120.9952010-20140.031987460.0143673-0.6002432.2627710.9812015-20180.001034070.00041622.02570316.958540.891Table 4
Descriptive statistics of daily interest rate differences.
Daily changeMeanStdSkewnessKurtosisFirst autocorr2010-2018−1.664353e−050.00141461.77097959.859160.0462010-2014−5.274586e−050.00254661.03573616.694650.0482015-20184.916667e−080.0001923.13453289.50437-0.204


### 4.4. Support Vector Machine (SVR) and PSO Optimized SVR Model Prediction Analysis

This paper selects the monthly data of these four input independent variables from January 2009 to November 2018. The exchange rate and pledged repo rate are both monthly growth rates. Then, combined with the monthly arithmetic average data of Shibor from February 2010 to December 2018, a total of 107 sets of data were obtained. The monthly data of the four interest rate influencing factors always match the overnight Shibor corresponding, so that the treatment can make the prediction model have practical application value. When establishing the SVR prediction model, the experiment selected the first 86 groups of 107 groups of data to train and then used the remaining 21 sets of real values to test the prediction performance, that is, predict the overnight Shibor monthly average from April 2018 to December 2018. The SVR model predicts the results as shown below.It can be seen from Figure7 that due to the large volatility of the overnight Shibor monthly mean data, the SVR predicts a generally predictive overnight Shibor, and the smoothness is poor, and the fluctuation of the predicted value is significantly larger than the fluctuation of the true value; especially when the Shibor changed greatly in April, May, and June of 2018, the prediction model could not follow the change well, and the fitting effect was poor. The model MAPE has a value of 15.1298, and the predicted results do not reflect the overnight Shibor trend.Figure 7
SVR model prediction results.Following the construction process of the SVR prediction model above, the first 86 groups of the 107 sets of data are also trained, and then, the remaining 21 sets of true values test the predicted performance, that is, the overnight Shibor month predicted from March 2017 to December 2018. For the mean, the PSO-SVR model predicts the results as shown in Figure8.Figure 8
PSO-SVR model prediction results.It can be seen from Figure8 that the PSO-SVR prediction model overcomes the problem of excessive volatility of the SVR prediction model to a certain extent and can basically be used for the monthly trend prediction of Shibor overnight. Similarly, for the large drop in Shibor overnight in April and May of 2018, the PSO-SVR model has a poor prediction effect. The reason may be that the large change in the quasibook capital of April has led to changes in the overnight Shibor, and related economic indicators cannot capture the impact of this emergency. The MAPE value of the model is 13.1351. When the two points of April and May 2018 are eliminated, the MAPE value of the model is 8.4499, and the prediction error is greatly reduced.First of all, before establishing the model, we must first perform descriptive statistical tests on the data and judge by observing the coefficient of variation, kurtosis, skewness, etc. The statistical description of the variables is shown in Table5.Table 5
The statistical description of the variables.
VariableMeanStandard deviationCoefficient of variationMaximum valueLDR69.051442.6418970.0382598476.77709CPI100.22740.5768860.00575577102.6ER6.8912330.6881010.099851658.2768RR2.5539436.6530432.60500841432Interbank offered rates more comprehensively; this paper attempts to establish a two-factor and multifactor GARCH-MIDAS model and firstly performs a correlation test on the selected six low-frequency explanatory variables. Six low-frequency explanatory variable test results are shown in Table6.Table 6
Six low-frequency explanatory variable test results.
VariableM2LDRCPIERM21.000000———LDR0.5247921.000000——CPI-0.0999350.0020891.000000—ER-0.6795600.0834290.0850611.000000This paper selects macroeconomic variables that are not statistically correlated with constructing a two-factor horizontal value GARCH-MIDAS model. The estimated results of the GARCH-MIDAS model are shown in Table7. A total of five groups of two-factor mixed-frequency models are constructed, which are consumer price index and loan-to-deposit ratio, loan-to-deposit ratio and exchange rate of USD/RMB, loan-to-deposit ratio and rediscount rate, loan-to-deposit ratio and statutory deposit reserve ratio, dual factors of consumer price index and statutory deposit reserve ratio, and horizontal effect mixing model.Table 7
The estimated results of the GARCH-MIDAS model.
VariableCPI+LDRLDR+ERLDR+RRLDR+RRα0.0016 (34.02%)-0.0002 (88.57%)0.0020 (3191%)0.0020 (31.91%)β0.2680 (0.00%)0.0602 (0.00%)0.2493 (0.00%)0.2493 (0.00%)μ0.7282 (0.00%)0.9397 (0.00%)0.7395 (0.00%)0.7395 (0.00%)Similar to the correlation test of the two-factor level value, this paper also conducts a pairwise correlation test for the two-factor volatility variables. The correlation test results of the volatility effect are shown in Table8. The correlation coefficient between the two variables is less than 0.3; that is, the two variables can be regarded as uncorrelated.Table 8
The correlation test results of the volatility effect.
VariablevM2vLDRvCPIvERvM21.000000———vLDR1.6247821.111111——vCPI-1.1888361.1121881.111111—vER-1.6786611.1834281.1861611.111111In order to further study the factors that affect the fluctuation of the interbank offered rate, this paper constructs a multifactor GARCH-MIDAS model, conducts a multicollinearity test on the low-frequency monthly explanatory variables, and uses the variance inflation factor (VIF) method to test; VIF is the multicollinearity test for the low-frequency monthly explanatory variable level values as shown in Table9.Table 9
The multicollinearity test for the low-frequency monthly explanatory variable level values.
CPIVIF1/VIFER23.720.042152SDRR14.290.069994M25.590.178844LDR2.750364191RR2.160.461929The CPI is shown in Figure9. It can be seen that the overall trends of the long-term components and conditional variances of the variable mixing model are basically similar, indicating that the long-term components described by the model can basically reflect the overall trend of the interbank offered rate fluctuations.Figure 9
The CPI.The deposit-loan ratio of financial institutions is shown in Figure10, where RV is the realized volatility calculated using known data, and RVF is the realized volatility calculated based on the GARCH-MIDAS model.Figure 10
The deposit-loan ratio of financial institutions.Measuring the estimation result model, this paper calculates the sample based on the estimation result of the model. The estimated results of the root mean square error RMSEin and RMSEout are shown in Table10. It can be seen that the value of the root mean square error of all models is smaller than the value of the root mean square error of the sample.Table 10
The estimated results of the root mean square error RMSEin and RMSEout.
VariableRMSEinRMSEoutM20.13409920.0660112LDR0.13052260.0666586CPI0.13061160.0636661ER0.12918380.0602260RR0.16653310.0661405According to the parameter results estimated by the model, this paper calculates the estimated valueH of the posterior test failure rate of VaR under the 99% confidence level of interbank lending rate fluctuations. The posterior test rate represents the breakdown of the logarithmic rate of return of IBOR, the ratio of the value at risk (VaR) to the total sample size. In this paper, the value of the posterior test ratio and the results are shown in Table 11.Table 11
The value of the posterior test ratio is calculated based on the in-sample and out-of-sample.
VariableHinHoutM20.02400.0294LDR0.00970.0120CPI0.01010.0042ER0.01050.0014RR0.01740.0159Figure11 shows the trading volume of the interbank lending market over time.Figure 11
Trading volume of the interbank lending market over the years.In order to investigate the influence ofε on the performance of the model, after determining the penalty factor C and the radial basis kernel parameter σ, experiments were carried out under different allowable errors ε, respectively. The training and generalization results under different accuracy requirements are shown in Table 12.Table 12
The training and generalization results under different accuracy requirements.
ToleranceTraining samplesNumber of support vectorsTraining error (%)176696.220.176536.820.001761670.000176119The effect of changes inC on the model is shown in Table 13. It can be seen that when C is small, the prediction effect is very poor. With the increase of C, the prediction accuracy increases rapidly. When C exceeds a certain value, the accuracy no longer changes with the change of C; that is, in a large range, the generalization ability is improved, insensitive to changes in C.Table 13
The effect of changes inC on the model.
CPrediction error (%)0.125.610171001210007100005.7500004.5The parameterσ of the kernel function reflects the characteristics of the training data and has a great influence on the generalization ability of the system. The selection goal of kernel function parameters is to enable the trained SVM to accurately predict unknown data; that is, it pays more attention to the generalization ability of the model. The effect of kernel function on model performance is shown in Table 14.Table 14
The effect of kernel function on model performance.
Kernel parametersNumber of support vectorsAverage relative error (%)0.000017631.10.00016523.80.0015913.50.0065564.3The prediction results of the SVM model are shown in Table15. The running time required for the SVM modeling process is 66.92 s, where MAPE=0.266628,R=0.664021,andMSE=0.20279.Table 15
The prediction results of the SVM model.
Month (2018)Actual valuePredictive value43.163.134853.353.342363.43.349173.83.791783.373.624693.453.1787103.473.1931According to the proposed variable selection method, relative contribution analysis is performed on all input variables, and input variables whose relative contribution value exceeds the average value of relative contribution are selected. The relative contribution values of the impact factors are shown in Figure12.Figure 12
The relative contribution values of the impact factors.Selectβ=1.7, at this time SV=60. By constructing a classification surface with a relatively small number of support vectors, a prediction model with higher generalization performance will be obtained. The MSE corresponding to different β is shown in Table 16.Table 16
The MSE corresponding to differentβ.
βSVMSE1.2660.1525231.4640.1548771.7630.1590541.8600.1729142510.187359The relative error is controlled within 4% in the first nine months, and it has high prediction accuracy in the long-range forecast, showing good forecasting ability (the sudden increase in the error in the last month is due to the national interest rate or sudden change in policy). The predicted values of the influencing factors in the study are shown in Figure13.Figure 13
The predicted values of the influencing factors.Assuming that there is no autocorrelation between the original Shibor sequence and the Shibor yield sequence, the Eviews7.2 quantitative analysis software is used to test, and the test results are shown in Table17.Table 17
Eviews7.2 test results.
Lag orderACPAC10.9360.93620.869-0.06 130.8150.07040.7690.03050.725-0.00460.6920.074The predictive ability of the GARCH family of models and the SV model was evaluated using both symmetric metrics (RMSE, MAE, and MAPE) and asymmetric metrics (LL). The predicted results are shown in Table18.Table 18
The predicted results.
ModelRMSEMAETGARCH0.39448 10.140743EGARCH0.3958610.140759PARCH0.3949950.140768Leverage SV0.2165290.0987451


## 4.1. ARIMA Empirical Analysis

This paper selects the empirical analysis of the ARMA forecasting model from the interbank lending market of the Shanghai Interbank Lending Market and the Interbank Lending Market from July 1, 2017, to June 30, 2018, and gradually implements the abovementioned ARMA model implementation steps to obtain forecasts. Table2 shows the multistep prediction accuracy of the ARIMA model.Table 2
ARIMA model multistep prediction accuracy.
Area1-step prediction2-step prediction3-step predictionMAERMSEMAPEMAERMSEMAPEMAERMSEMAPEChina0.06030.08992.36850.10160.14094.04530.14530.18605.8233Shanghai0.05760.8562.32290.09980.14054.02680.14360.18435.8395It can be seen from Table2 that the ARIMA model is used to predict the interbank lending rate between China and Shanghai. The 1-step prediction error (MAPE) is stable at around 2.3%, and the 2-step prediction error (MAPE) is stable at around 4.0%. 3-step prediction error (MAPE) is stable at around 5.8%. It can be seen from the data that the stability of the model is strong. They do not exceed 1% in the interbank lending forecast rate between China and Shanghai. It can be seen that the 1-step, 2-step, and 3-step predictions have different degrees of hysteresis, and the one-step prediction has the best change trend in the Shanghai bank lending rate very well (local maxima and minima), but the lag effect has not changed. The ARIMA model still has a certain lag for the interbank lending rate of the Chinese banks, and the one-step forecasting effect is the best, the closest to the true value. However, the forecasting effect of the starting point and the last point is not as good as that of the Shanghai Interbank Offered Rate, but the middle part is well fitted to the changing trend of the interbank offered rate. In summary, the ARIMA model performs well in loan interest rate forecasting, but the lag of the forecast needs to be strengthened. As the number of forecasting steps increases, the forecasting error becomes larger and larger.


## 4.2. BP Neural Network Result Analysis

Figure6 shows the prediction effect of the BP neural network on overnight Shibor.Figure 6
BP neural network prediction results.It can be seen from Figure6 that the fitting performance is general when the fluctuation is large.


## 4.3. Error Comparison Analysis

After analyzing the data, the sample is divided into two subsamples in 2015 to study the short-term interest rate behavior. The preliminary analysis results of the overall sample and subsample are shown in Table3. Table 3 lists the descriptive statistical results of interest rate samples and daily changes in interest rates and obtains the mean, standard deviation, skewness, kurtosis, and autocorrelation coefficient statistics of the two sets of data. It can be seen that the autocorrelation coefficient of the short-term interest rate is close to 1, and the first autocorrelation coefficients of the two interest rate subintervals are also close to 1. However, the two subcycles have significantly different values, standard deviations, and skewness. Prior to 2015, short-term interest rates had higher averages, standard deviations, and lower negative skewers. Table 4 gives a descriptive statistical result of the daily interest rate difference. It can be seen that the first autocorrelation coefficient of the sample and the 2006-2008 subsample is negative, and the first autocorrelation coefficient of the 2009-20 juxta sample is positive. The daily interest rate difference has a positive skewness, but the two subcycles have significantly different mean, standard deviation, skewness, and first autocorrelation coefficient.Table 3
Descriptive statistics of daily changes.
Spot rateMeanStdSkewnessKurtosisFirst autocorr2010-20180.010630410.01640591.3486983.1509120.9952010-20140.031987460.0143673-0.6002432.2627710.9812015-20180.001034070.00041622.02570316.958540.891Table 4
Descriptive statistics of daily interest rate differences.
Daily changeMeanStdSkewnessKurtosisFirst autocorr2010-2018−1.664353e−050.00141461.77097959.859160.0462010-2014−5.274586e−050.00254661.03573616.694650.0482015-20184.916667e−080.0001923.13453289.50437-0.204


## 4.4. Support Vector Machine (SVR) and PSO Optimized SVR Model Prediction Analysis

This paper selects the monthly data of these four input independent variables from January 2009 to November 2018. The exchange rate and pledged repo rate are both monthly growth rates. Then, combined with the monthly arithmetic average data of Shibor from February 2010 to December 2018, a total of 107 sets of data were obtained. The monthly data of the four interest rate influencing factors always match the overnight Shibor corresponding, so that the treatment can make the prediction model have practical application value. When establishing the SVR prediction model, the experiment selected the first 86 groups of 107 groups of data to train and then used the remaining 21 sets of real values to test the prediction performance, that is, predict the overnight Shibor monthly average from April 2018 to December 2018. The SVR model predicts the results as shown below.It can be seen from Figure7 that due to the large volatility of the overnight Shibor monthly mean data, the SVR predicts a generally predictive overnight Shibor, and the smoothness is poor, and the fluctuation of the predicted value is significantly larger than the fluctuation of the true value; especially when the Shibor changed greatly in April, May, and June of 2018, the prediction model could not follow the change well, and the fitting effect was poor. The model MAPE has a value of 15.1298, and the predicted results do not reflect the overnight Shibor trend.Figure 7
SVR model prediction results.Following the construction process of the SVR prediction model above, the first 86 groups of the 107 sets of data are also trained, and then, the remaining 21 sets of true values test the predicted performance, that is, the overnight Shibor month predicted from March 2017 to December 2018. For the mean, the PSO-SVR model predicts the results as shown in Figure8.Figure 8
PSO-SVR model prediction results.It can be seen from Figure8 that the PSO-SVR prediction model overcomes the problem of excessive volatility of the SVR prediction model to a certain extent and can basically be used for the monthly trend prediction of Shibor overnight. Similarly, for the large drop in Shibor overnight in April and May of 2018, the PSO-SVR model has a poor prediction effect. The reason may be that the large change in the quasibook capital of April has led to changes in the overnight Shibor, and related economic indicators cannot capture the impact of this emergency. The MAPE value of the model is 13.1351. When the two points of April and May 2018 are eliminated, the MAPE value of the model is 8.4499, and the prediction error is greatly reduced.First of all, before establishing the model, we must first perform descriptive statistical tests on the data and judge by observing the coefficient of variation, kurtosis, skewness, etc. The statistical description of the variables is shown in Table5.Table 5
The statistical description of the variables.
VariableMeanStandard deviationCoefficient of variationMaximum valueLDR69.051442.6418970.0382598476.77709CPI100.22740.5768860.00575577102.6ER6.8912330.6881010.099851658.2768RR2.5539436.6530432.60500841432Interbank offered rates more comprehensively; this paper attempts to establish a two-factor and multifactor GARCH-MIDAS model and firstly performs a correlation test on the selected six low-frequency explanatory variables. Six low-frequency explanatory variable test results are shown in Table6.Table 6
Six low-frequency explanatory variable test results.
VariableM2LDRCPIERM21.000000———LDR0.5247921.000000——CPI-0.0999350.0020891.000000—ER-0.6795600.0834290.0850611.000000This paper selects macroeconomic variables that are not statistically correlated with constructing a two-factor horizontal value GARCH-MIDAS model. The estimated results of the GARCH-MIDAS model are shown in Table7. A total of five groups of two-factor mixed-frequency models are constructed, which are consumer price index and loan-to-deposit ratio, loan-to-deposit ratio and exchange rate of USD/RMB, loan-to-deposit ratio and rediscount rate, loan-to-deposit ratio and statutory deposit reserve ratio, dual factors of consumer price index and statutory deposit reserve ratio, and horizontal effect mixing model.Table 7
The estimated results of the GARCH-MIDAS model.
VariableCPI+LDRLDR+ERLDR+RRLDR+RRα0.0016 (34.02%)-0.0002 (88.57%)0.0020 (3191%)0.0020 (31.91%)β0.2680 (0.00%)0.0602 (0.00%)0.2493 (0.00%)0.2493 (0.00%)μ0.7282 (0.00%)0.9397 (0.00%)0.7395 (0.00%)0.7395 (0.00%)Similar to the correlation test of the two-factor level value, this paper also conducts a pairwise correlation test for the two-factor volatility variables. The correlation test results of the volatility effect are shown in Table8. The correlation coefficient between the two variables is less than 0.3; that is, the two variables can be regarded as uncorrelated.Table 8
The correlation test results of the volatility effect.
VariablevM2vLDRvCPIvERvM21.000000———vLDR1.6247821.111111——vCPI-1.1888361.1121881.111111—vER-1.6786611.1834281.1861611.111111In order to further study the factors that affect the fluctuation of the interbank offered rate, this paper constructs a multifactor GARCH-MIDAS model, conducts a multicollinearity test on the low-frequency monthly explanatory variables, and uses the variance inflation factor (VIF) method to test; VIF is the multicollinearity test for the low-frequency monthly explanatory variable level values as shown in Table9.Table 9
The multicollinearity test for the low-frequency monthly explanatory variable level values.
CPIVIF1/VIFER23.720.042152SDRR14.290.069994M25.590.178844LDR2.750364191RR2.160.461929The CPI is shown in Figure9. It can be seen that the overall trends of the long-term components and conditional variances of the variable mixing model are basically similar, indicating that the long-term components described by the model can basically reflect the overall trend of the interbank offered rate fluctuations.Figure 9
The CPI.The deposit-loan ratio of financial institutions is shown in Figure10, where RV is the realized volatility calculated using known data, and RVF is the realized volatility calculated based on the GARCH-MIDAS model.Figure 10
The deposit-loan ratio of financial institutions.Measuring the estimation result model, this paper calculates the sample based on the estimation result of the model. The estimated results of the root mean square error RMSEin and RMSEout are shown in Table10. It can be seen that the value of the root mean square error of all models is smaller than the value of the root mean square error of the sample.Table 10
The estimated results of the root mean square error RMSEin and RMSEout.
VariableRMSEinRMSEoutM20.13409920.0660112LDR0.13052260.0666586CPI0.13061160.0636661ER0.12918380.0602260RR0.16653310.0661405According to the parameter results estimated by the model, this paper calculates the estimated valueH of the posterior test failure rate of VaR under the 99% confidence level of interbank lending rate fluctuations. The posterior test rate represents the breakdown of the logarithmic rate of return of IBOR, the ratio of the value at risk (VaR) to the total sample size. In this paper, the value of the posterior test ratio and the results are shown in Table 11.Table 11
The value of the posterior test ratio is calculated based on the in-sample and out-of-sample.
VariableHinHoutM20.02400.0294LDR0.00970.0120CPI0.01010.0042ER0.01050.0014RR0.01740.0159Figure11 shows the trading volume of the interbank lending market over time.Figure 11
Trading volume of the interbank lending market over the years.In order to investigate the influence ofε on the performance of the model, after determining the penalty factor C and the radial basis kernel parameter σ, experiments were carried out under different allowable errors ε, respectively. The training and generalization results under different accuracy requirements are shown in Table 12.Table 12
The training and generalization results under different accuracy requirements.
ToleranceTraining samplesNumber of support vectorsTraining error (%)176696.220.176536.820.001761670.000176119The effect of changes inC on the model is shown in Table 13. It can be seen that when C is small, the prediction effect is very poor. With the increase of C, the prediction accuracy increases rapidly. When C exceeds a certain value, the accuracy no longer changes with the change of C; that is, in a large range, the generalization ability is improved, insensitive to changes in C.Table 13
The effect of changes inC on the model.
CPrediction error (%)0.125.610171001210007100005.7500004.5The parameterσ of the kernel function reflects the characteristics of the training data and has a great influence on the generalization ability of the system. The selection goal of kernel function parameters is to enable the trained SVM to accurately predict unknown data; that is, it pays more attention to the generalization ability of the model. The effect of kernel function on model performance is shown in Table 14.Table 14
The effect of kernel function on model performance.
Kernel parametersNumber of support vectorsAverage relative error (%)0.000017631.10.00016523.80.0015913.50.0065564.3The prediction results of the SVM model are shown in Table15. The running time required for the SVM modeling process is 66.92 s, where MAPE=0.266628,R=0.664021,andMSE=0.20279.Table 15
The prediction results of the SVM model.
Month (2018)Actual valuePredictive value43.163.134853.353.342363.43.349173.83.791783.373.624693.453.1787103.473.1931According to the proposed variable selection method, relative contribution analysis is performed on all input variables, and input variables whose relative contribution value exceeds the average value of relative contribution are selected. The relative contribution values of the impact factors are shown in Figure12.Figure 12
The relative contribution values of the impact factors.Selectβ=1.7, at this time SV=60. By constructing a classification surface with a relatively small number of support vectors, a prediction model with higher generalization performance will be obtained. The MSE corresponding to different β is shown in Table 16.Table 16
The MSE corresponding to differentβ.
βSVMSE1.2660.1525231.4640.1548771.7630.1590541.8600.1729142510.187359The relative error is controlled within 4% in the first nine months, and it has high prediction accuracy in the long-range forecast, showing good forecasting ability (the sudden increase in the error in the last month is due to the national interest rate or sudden change in policy). The predicted values of the influencing factors in the study are shown in Figure13.Figure 13
The predicted values of the influencing factors.Assuming that there is no autocorrelation between the original Shibor sequence and the Shibor yield sequence, the Eviews7.2 quantitative analysis software is used to test, and the test results are shown in Table17.Table 17
Eviews7.2 test results.
Lag orderACPAC10.9360.93620.869-0.06 130.8150.07040.7690.03050.725-0.00460.6920.074The predictive ability of the GARCH family of models and the SV model was evaluated using both symmetric metrics (RMSE, MAE, and MAPE) and asymmetric metrics (LL). The predicted results are shown in Table18.Table 18
The predicted results.
ModelRMSEMAETGARCH0.39448 10.140743EGARCH0.3958610.140759PARCH0.3949950.140768Leverage SV0.2165290.0987451


## 5. Discussions

For such as the interbank interest rate in Shanghai, the overall prediction effect of the BP neural network prediction model is general. In the fitting process of the test set, there is a large fluctuation inflection point, as a whole.The prediction effect of the ARIMA fluctuation release rate has been quite good. Further optimization of the wavelet neural network parameters using the ARIMA model has a significant improvement effect on the overnight Shibor. This shows good results, and the predicted value can be used to assist decision-making.In the artificial intelligence-based overnight Shibor prediction model, we use the BP neural network and ARIMA model to predict the daily data of the overnight Shibor. The focus is on that it does not involve fluctuations in the time dimension of day; such assumptions are basically satisfied.In Shibor’s trend research, the Shibor has been officially launched in 2010, and the monthly data is very limited. The regression support vector machine (SVR) algorithm for small sample learning is selected. By analyzing the correlation test analysis, the SVR forecasting model is established to predict the monthly data of the overnight Shibor. The empirical results show that. The particle swarm optimization SVR prediction algorithm realizes the nonlinear function extremum optimization of individuals in solvable space through particle dynamic adjustment. It shows superiority to the SVR prediction model, with the ability to predict.


## 6. Conclusion

The embedded wireless communication network is in the ad hoc network; each user terminal not only can move but also has two functions of router and host. On the one hand, as a host, the terminal needs to run various user-oriented application programs and complete the data packet forwarding and routing maintenance work according to the routing strategy and routing table. This paper studies Shibor’s overnight varieties from algorithms. In the selection of the algorithm, the applied prediction algorithm and then the ARIMA model are applied to the overnight Shibor prediction, which improves the prediction accuracy. The research focus is on the independent variable of the last month Shibor and can be improved.The main findings of this paper are as follows:(1)
For the data such as the interbank interest rate in Shanghai, the overall prediction occurs; the inflection point will be greatly affected. The predicted effect is afterwards(2)
The prediction effect of the ARIMA fluctuation release rate has been quite good. The ARIMA model is further used, which has a significant improvement effect on them overnight. The prediction of Shibor’s daily data shows good results, and the predicted value can be used to assist decision-making(3)
In Shibor’s trend research, for the Shibor from 2007, the monthly data is very limited, and the regression support vector machine (SVR) algorithm for small sample learning is selected(4)
Particle swarm optimization SVR prediction algorithm, through the dynamic adjustment of particles to achieve the optimization of the nonlinear function of individuals in the solvable space, performance outperformed the SVR modelWhen making long-term forecasts on the trend of interbank lending rates, the article will cause a certain degree of deviation in the forecast results when there is a sudden change in the economy, such as major changes in national economic policies and other uncertain factors, which can easily lead to misleading. This is also an issue that needs to be focused on and considered in future research.



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