Off-site option customer settlement system based on option model identification

Abstract

The invention relates to the technical field of option price analysis, in particular to an off-site option customer settlement system based on option model identification, which comprises a data collection module, a fitting and calculation module, an option pricing module and a credit risk adjustment module, and is characterized in that the data collection module collects historical price data and customer transaction data; the fitting and calculation module is used for fitting two random processes in the random volatility model according to historical price data; estimating parameters of the stochastic volatility model using maximum likelihood estimation; the customer transaction data is analyzed, and the default intensity of the customer is calculated; the option pricing module generates a future path of an underlying asset price and a future path of a volatility through a Monte Carlo simulation method based on two random processes of a random volatility model, and calculates a traditional option price; and the credit risk adjustment module calculates an overall credit risk adjustment item according to the future path of the target asset price and the default intensity of the customer, and adjusts the traditional option price.

Description

Technical Field

[0001] The present invention relates to the technical field of option price analysis, and in particular to an over-the-counter option customer settlement system based on option model identification. Background Art

[0002] The traditional OTC options customer clearing system has several specific problems in option pricing and credit risk management.

[0003] First, in terms of option pricing, many traditional models assume that volatility is fixed and do not consider the dynamic changes of volatility, which leads to a large deviation between the pricing results and the actual market situation. Volatility in the actual market changes frequently, and there are complex interactions between the price of the underlying asset and volatility. The fixed volatility assumption cannot accurately capture these dynamic characteristics, making option pricing lack precision and stability.

[0004] Secondly, in terms of credit risk management, traditional systems often rely on static credit ratings and limited historical data, and are unable to dynamically assess the probability of a customer's default. This makes it difficult for financial institutions to timely and accurately assess a customer's credit risk during transactions, increasing potential default losses. Specifically, traditional systems lack a comprehensive analysis of customer credit rating changes, default event records, and financial statement data, and are unable to quantify the customer's default intensity, making it impossible to effectively adjust the impact of credit risk in option pricing. These problems not only reduce the transparency of transactions, but also increase uncertainty and potential risks in the financial market. Summary of the invention

[0005] The purpose of the present invention is to provide an over-the-counter option customer settlement system based on option model identification to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: an over-the-counter option customer settlement system based on option model identification, comprising a data collection module, a fitting and calculation module, an option pricing module and a credit risk adjustment module, wherein: The data collection module collects historical price data and customer transaction data; The fitting and calculation module fits two random processes in the stochastic volatility model based on historical price data, wherein the random process includes the underlying asset price process and the volatility process; estimates the parameters of the stochastic volatility model using maximum likelihood estimation; and analyzes customer transaction data to calculate the customer's default intensity; The option pricing module generates the future path of the underlying asset price and the future path of volatility based on two random processes of the stochastic volatility model through the Monte Carlo simulation method, calculates the option price under each path, and takes the average value as the traditional option price; The credit risk adjustment module calculates an overall credit risk adjustment item based on the future path of the underlying asset price and the customer's default intensity; and applies the adjustment item to the traditional option price to obtain an adjusted option price.

[0007] As a further improvement of the present technical solution, the historical price data in the data collection module includes the price of the underlying asset; the customer transaction data includes transaction records, credit rating changes, default event records and financial statement data.

[0008] As a further improvement of the technical solution, the fitting and calculation module includes a fitting unit, and the process of the fitting unit fitting the two random processes in the stochastic volatility model specifically includes: Based on the underlying asset price data in the historical price data, the daily logarithmic rate of return is calculated. The logarithmic rate of return refers to the logarithmic change of the asset price between two adjacent trading days. By calculating this change, the daily logarithmic rate of return is obtained as the actual historical price path; based on the daily logarithmic rate of return, the historical volatility is calculated as the actual historical volatility path; the historical volatility is an important indicator to measure the degree of asset price volatility, which is obtained through the standard deviation of the logarithmic rate of return, so as to understand the volatility characteristics of the underlying asset; According to the stochastic volatility model and historical volatility, two random processes in the stochastic volatility model, namely the underlying asset price process and the volatility process, are fitted, and the volatility parameters of the stochastic volatility model are estimated through the maximum likelihood estimation method, wherein: the underlying asset price process is to generate a simulated path of the underlying asset price according to the stochastic volatility model, compare the generated underlying asset price path with the actual historical price path, quantify the comparison difference through the mean square error, and adjust the parameter value of the stochastic volatility model through the maximum likelihood estimation method so that the difference between the simulated path of the underlying asset price and the actual historical price path is minimized; the volatility process is also to generate a simulated path of volatility according to the stochastic volatility model, compare the simulated path of volatility with the actual historical volatility path, quantify the comparison difference through the mean square error, and adjust the parameter value of the stochastic volatility model through the maximum likelihood estimation method so that the difference between the simulated path of the underlying asset price and the actual historical price path is minimized.

[0009] As a further improvement of the technical solution, the fitting and calculation module includes a calculation unit, and the process of the calculation unit calculating the default strength of the customer specifically includes: Extract customer credit rating changes, default event records, and financial statement data from customer transaction data; Based on the changes in credit ratings in customer transaction data, a graph of credit rating changes over time is plotted, and the frequency and direction of rating changes are calculated; based on the default event records in customer transaction data, the number and time intervals of default events are counted, and the default frequency is calculated; based on the financial statement data in the customer transaction data, key financial indicators are extracted, and a graph of indicator changes is plotted to calculate the financial health score; a probability model of default intensity is selected, and the parameters of the probability model are estimated by the maximum likelihood estimation method, so that the probability model can fit the frequency and direction of rating changes, default frequency and financial health score of the customer; the customer's default intensity is calculated by constructing a completed probability model of default intensity, and the customer's default intensity represents the probability of the customer defaulting in a certain period of time in the future.

[0010] As a further improvement of the technical solution, the process of the option pricing module generating the future path of the underlying asset price and the future path of volatility specifically includes: According to the adjusted parameters in the stochastic volatility model, the stochastic process equation of the underlying asset price and volatility is set; a random variable is generated through a random number generator, and the random variable represents the uncertainty of market fluctuations; the random variable is substituted into the stochastic process equation, and the changes in the underlying asset price and volatility at each future time point are gradually simulated to generate multiple future paths as the future path of the underlying asset price and the future path of volatility.

[0011] As a further improvement of the technical solution, the process of calculating the traditional option price by the option pricing module specifically includes: Select an option pricing model; substitute the underlying asset price and volatility on each future path into the formula of the option pricing model to calculate the corresponding option price; repeat this process on each path to obtain option prices under multiple paths; the option price under each path represents the option value under the market conditions of that path; The option prices under all paths are summarized and their average is calculated to obtain the final option price estimate as the traditional option price.

[0012] As a further improvement of the technical solution, the process of the credit risk adjustment module calculating the overall credit risk adjustment item and adjusting the traditional option price specifically includes: For each future path of the underlying asset price, the credit risk adjustment item under that path is calculated in combination with the customer's default intensity. The credit risk adjustment item is used to quantify the possible loss to the option holder due to the customer's default. Using the customer's default intensity, calculate the probability of the customer defaulting at each time point; and obtain the future path of the underlying asset price at the time point when the probability of the customer defaulting is greater than 90% as the default path; summarize and sum the credit risk adjustment items on the default path, and take the average to obtain the overall credit risk adjustment item; The adjusted option price is obtained by adding the overall credit risk adjustment term to the traditional option price.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The OTC option customer settlement system based on option model identification solves the static problem of volatility assumptions in traditional systems by introducing a stochastic volatility model. The system first collects historical price data of the underlying asset, calculates daily logarithmic returns and historical volatility, and then uses the maximum likelihood estimation method to fit two random processes: the underlying asset price process and the volatility process. By generating a simulation path that is more in line with the actual market dynamics, the system can more accurately reflect the market volatility characteristics, thereby improving the accuracy and reliability of option pricing.

[0014] In addition, the system dynamically calculates the customer's default intensity by comprehensively analyzing the customer's transaction records, credit rating changes, default event records and financial statement data. The system uses this data to build a probability model, fits the model parameters through the maximum likelihood estimation method, and estimates the customer's default probability at different time points. These default probabilities are used to calculate the credit risk adjustment items under each underlying asset price path, and finally aggregate and average them to obtain the overall credit risk adjustment items, which are applied to traditional option prices; this step enables financial institutions to effectively consider the customer's credit risk in option pricing and reduce potential default losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall module of the present invention; Figure 2 It is a schematic diagram of the fitting and calculation module unit of the present invention.

[0016] In the figure: 100, data collection module; 200, fitting and calculation module; 201, fitting unit; 202, calculation unit; 300, option pricing module; 400, credit risk adjustment module. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Next, see Figure 1-Figure 2 The present invention provides a technical solution: an over-the-counter option customer settlement system based on option model identification, including a data collection module 100, a fitting and calculation module 200, an option pricing module 300 and a credit risk adjustment module 400.

[0019] The data collection module 100 obtains historical price data and customer transaction data, wherein the historical price data includes the price of the underlying asset, which can provide a basis for fitting the stochastic volatility model and ensure that the estimation of the model parameters is more accurate, thereby improving the scientificity and reliability of option pricing; customer transaction data includes transaction records, credit rating changes, default event records and financial statement data, which comprehensively reflect the customer's credit status and transaction behavior, help to more accurately calculate the customer's default intensity, provide a basis for credit risk adjustment, reduce potential losses caused by customer default, and enhance the transparency and security of financial transactions.

[0020] The fitting unit 201 in the fitting and calculation module 200 fits two random processes in the stochastic volatility model according to the historical price data, wherein the random process includes the underlying asset price process and the volatility process; and estimates the parameters of the stochastic volatility model using maximum likelihood estimation, specifically including: Based on the underlying asset price data in the historical price data, the daily logarithmic rate of return is calculated. The logarithmic rate of return refers to the logarithmic change of the asset price between two adjacent trading days. By calculating this change, the daily logarithmic rate of return is obtained as the actual historical price path; based on the daily logarithmic rate of return, the historical volatility is calculated as the actual historical volatility path; the historical volatility is an important indicator to measure the degree of asset price volatility, which is obtained through the standard deviation of the logarithmic rate of return, so as to understand the volatility characteristics of the underlying asset; According to the stochastic volatility model and historical volatility, two random processes in the stochastic volatility model, namely the underlying asset price process and the volatility process, are fitted, and the volatility parameters of the stochastic volatility model are estimated through the maximum likelihood estimation method, wherein: the underlying asset price process is to generate a simulated path of the underlying asset price according to the stochastic volatility model, compare the generated underlying asset price path with the actual historical price path, quantify the comparison difference through the mean square error, and adjust the parameter value of the stochastic volatility model through the maximum likelihood estimation method so that the difference between the simulated path of the underlying asset price and the actual historical price path is minimized; the volatility process is also to generate a simulated path of volatility according to the stochastic volatility model, compare the simulated path of volatility with the actual historical volatility path, quantify the comparison difference through the mean square error, and adjust the parameter value of the stochastic volatility model through the maximum likelihood estimation method so that the difference between the simulated path of the underlying asset price and the actual historical price path is minimized.

[0021] The calculation unit 202 in the fitting and calculation module 200 analyzes the customer transaction data and calculates the customer's default intensity, specifically including: Extract customer credit rating changes, default event records, and financial statement data from customer transaction data; Based on the changes in credit ratings in customer transaction data, draw a graph of credit rating changes over time, and calculate the frequency and direction of rating changes; based on the default event records in customer transaction data, count the number and time intervals of default events, and calculate the default frequency; based on the financial statement data in the customer transaction data, extract key financial indicators, such as debt ratio, profitability indicators, debt repayment ability indicators, etc., and draw an indicator change graph to calculate the financial health score; select a probability model of default intensity (such as a proportional risk model, a generalized linear model, etc.), and estimate the parameters of the probability model through the maximum likelihood estimation method, so that the probability model can fit the frequency and direction of the customer's rating changes, the default frequency and the financial health score; by constructing the completed probability model of default intensity, calculate the customer's default intensity, that is, the probability that the customer will default in a certain period of time in the future.

[0022] The fitting and calculation module 200 fits the two random processes in the stochastic volatility model (the underlying asset price process and the volatility process) and uses the maximum likelihood estimation method to adjust the model parameters, so that the generated simulation path is more in line with the actual market dynamics; this not only improves the accuracy of option pricing, but also can more accurately capture market volatility characteristics and reduce pricing errors caused by model assumption deviations, thereby providing financial institutions with a more reliable pricing basis; it can also dynamically calculate the customer's default intensity, comprehensively assess the customer's credit risk, and reduce the potential losses caused by default.

[0023] The option pricing module 300 generates the future path of the underlying asset price and the future path of volatility based on the two random processes of the stochastic volatility model through the Monte Carlo simulation method, calculates the option price under each future path, and takes the average value as the traditional option price, which specifically includes: The Monte Carlo simulation method is used to generate the future path of the underlying asset price and the future path of volatility. According to the adjusted parameters in the stochastic volatility model, the stochastic process equation of the underlying asset price and volatility is set; a random variable is generated through a random number generator, and the random variable represents the uncertainty of market fluctuations; the random variable is substituted into the stochastic process equation, and the changes of the underlying asset price and volatility at each future time point are gradually simulated to generate multiple future paths as the future path of the underlying asset price and the future path of volatility; each future path is an evolution of the underlying asset price and volatility under specific market conditions; by generating multiple paths (for example, thousands or tens of thousands), the Monte Carlo simulation can capture multiple possibilities of market fluctuations, thereby providing a rich sample basis for subsequent option pricing. The advantage of this method is that it can handle complex stochastic processes and provide more accurate and comprehensive risk assessments; Calculating the option price under each future path is one of the key steps of the Monte Carlo simulation method. For each future path of the underlying asset price and the future path of volatility generated by Monte Carlo simulation, select an option pricing model, such as the classic Black-Scholes model or the Heston model; according to the underlying asset price and volatility on each future path, substitute the formula of the option pricing model to calculate the corresponding option price; this process will be repeated on each path to obtain option prices under multiple paths; the option price under each path represents the option value under the market conditions of that path; Since the number of paths generated by Monte Carlo simulation is very large, the option prices under these paths cover the possibilities under various market scenarios; therefore, the option prices under all paths are summarized, and their average is calculated to obtain the final option price estimate as the traditional option price; this average reflects the option price expectations under various possible changes in the underlying asset price and volatility under the stochastic volatility model, providing a more accurate and comprehensive pricing basis for trading and risk management; this method can effectively cope with the uncertainty of market fluctuations and improve the scientificity and reliability of financial decision-making.

[0024] The credit risk adjustment module 400 calculates the overall credit risk adjustment item based on the future path of the underlying asset price and the customer's default strength; applies the adjustment item to the traditional option price to obtain the adjusted option price, specifically including: For each future path of the underlying asset price, the credit risk adjustment item under that path is calculated in combination with the customer's default intensity. The credit risk adjustment item is used to quantify the possible loss to the option holder due to the customer's default. Using the customer's default intensity, calculate the probability of the customer defaulting at each time point; and obtain the future path of the underlying asset price at the time point when the probability of the customer defaulting is greater than 90% as the default path; since default may occur at different time points in the future, it is necessary to discount the potential loss to the current time point, which is achieved through the discount formula in the public database; summarize and sum the credit risk adjustment items on the processed default path, and take the average to obtain the overall credit risk adjustment item; The adjusted option price is obtained by adding the overall credit risk adjustment term to the traditional option price.

[0025] The credit risk adjustment module 400 allows the adjusted option price to not only reflect the impact of market fluctuations, but also take into account the potential impact of customer credit risk, thereby improving the comprehensiveness and robustness of pricing. This helps financial institutions to more accurately assess transaction risks, reduce potential default losses, and enhance transaction transparency and market stability.

[0026] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An OTC option customer settlement system based on option model identification, characterized in that: The method comprises a data collection module (100), a fitting and calculation module (200), an option pricing module (300) and a credit risk adjustment module (400), wherein: The data collection module (100) collects historical price data and customer transaction data; The fitting and calculation module (200) fits two random processes in the stochastic volatility model according to the historical price data, wherein the random process includes the underlying asset price process and the volatility process; estimates the parameters of the stochastic volatility model using maximum likelihood estimation; and analyzes the customer transaction data to calculate the customer's default intensity; The option pricing module (300) generates the future path of the underlying asset price and the future path of volatility based on two random processes of the stochastic volatility model through a Monte Carlo simulation method, calculates the option price under each path, and takes the average value as the traditional option price; The credit risk adjustment module (400) calculates an overall credit risk adjustment item based on the future path of the underlying asset price and the customer's default intensity; and applies the adjustment item to the traditional option price to obtain an adjusted option price.

2. The OTC option customer settlement system based on option model identification according to claim 1, characterized in that: The historical price data in the data collection module (100) includes the price of the underlying asset; the customer transaction data includes transaction records, credit rating changes, default event records and financial statement data.

3. The OTC option customer settlement system based on option model identification according to claim 2, characterized in that: The fitting and calculation module (200) comprises a fitting unit (201), and the process of fitting two random processes in the stochastic volatility model by the fitting unit (201) specifically comprises: Based on the underlying asset price data in the historical price data, the daily logarithmic rate of return is calculated. The logarithmic rate of return refers to the logarithmic change of the asset price between two adjacent trading days. By calculating this change, the daily logarithmic rate of return is obtained as the actual historical price path; based on the daily logarithmic rate of return, the historical volatility is calculated as the actual historical volatility path; the historical volatility is an important indicator to measure the degree of asset price volatility, which is obtained through the standard deviation of the logarithmic rate of return, so as to understand the volatility characteristics of the underlying asset; According to the stochastic volatility model and historical volatility, two random processes in the stochastic volatility model, namely the underlying asset price process and the volatility process, are fitted, and the volatility parameters of the stochastic volatility model are estimated through the maximum likelihood estimation method, wherein: the underlying asset price process is to generate a simulated path of the underlying asset price according to the stochastic volatility model, compare the generated underlying asset price path with the actual historical price path, quantify the comparison difference through the mean square error, and adjust the parameter value of the stochastic volatility model through the maximum likelihood estimation method so that the difference between the simulated path of the underlying asset price and the actual historical price path is minimized; the volatility process is also to generate a simulated path of volatility according to the stochastic volatility model, compare the simulated path of volatility with the actual historical volatility path, quantify the comparison difference through the mean square error, and adjust the parameter value of the stochastic volatility model through the maximum likelihood estimation method so that the difference between the simulated path of the underlying asset price and the actual historical price path is minimized.

4. The OTC option customer settlement system based on option model identification according to claim 3, characterized in that: The fitting and calculation module (200) comprises a calculation unit (202), and the process of the calculation unit (202) calculating the default strength of the customer specifically comprises: Extract customer credit rating changes, default event records, and financial statement data from customer transaction data; Based on the changes in credit ratings in customer transaction data, a graph of credit rating changes over time is plotted, and the frequency and direction of rating changes are calculated; based on the default event records in customer transaction data, the number and time intervals of default events are counted, and the default frequency is calculated; based on the financial statement data in the customer transaction data, key financial indicators are extracted, and a graph of indicator changes is plotted to calculate the financial health score; a probability model of default intensity is selected, and the parameters of the probability model are estimated by the maximum likelihood estimation method, so that the probability model can fit the frequency and direction of rating changes, default frequency and financial health score of the customer; the customer's default intensity is calculated by constructing a completed probability model of default intensity, and the customer's default intensity represents the probability of the customer defaulting in a certain period of time in the future.

5. The OTC option customer settlement system based on option model identification according to claim 4, characterized in that: The process of the option pricing module (300) generating the future path of the underlying asset price and the future path of volatility specifically includes: According to the adjusted parameters in the stochastic volatility model, the stochastic process equation of the underlying asset price and volatility is set; a random variable is generated through a random number generator, and the random variable represents the uncertainty of market fluctuations; the random variable is substituted into the stochastic process equation, and the changes in the underlying asset price and volatility at each future time point are gradually simulated to generate multiple future paths as the future path of the underlying asset price and the future path of volatility.

6. The OTC option customer settlement system based on option model identification according to claim 5, characterized in that: The process of calculating the traditional option price by the option pricing module (300) specifically includes: Select an option pricing model; substitute the underlying asset price and volatility on each future path into the formula of the option pricing model to calculate the corresponding option price; repeat this process on each path to obtain option prices under multiple paths; the option price under each path represents the option value under the market conditions of that path; The option prices under all paths are summarized and their average is calculated to obtain the final option price estimate as the traditional option price.

7. The over-the-counter option customer settlement system based on option model identification according to claim 6, characterized in that: The process of the credit risk adjustment module (400) calculating the overall credit risk adjustment item and adjusting the traditional option price specifically includes: For each future path of the underlying asset price, the credit risk adjustment item under that path is calculated in combination with the customer's default intensity. The credit risk adjustment item is used to quantify the possible loss to the option holder due to the customer's default. Using the customer's default intensity, calculate the probability of the customer defaulting at each time point; and obtain the future path of the underlying asset price at the time point when the probability of the customer defaulting is greater than 90% as the default path; summarize and sum the credit risk adjustment items on the default path, and take the average to obtain the overall credit risk adjustment item; The adjusted option price is obtained by adding the overall credit risk adjustment term to the traditional option price.

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