Data processing method, device and equipment for interest rate derivative product, medium and product

Abstract

The invention provides an interest rate derivative product data processing method and device, equipment, a medium and a product. Relates to the technical field of data processing. The method comprises the following steps: acquiring curve data and sample data days of historical transactions of interest rate derivative products; constructing an interest rate curve according to the curve data and the sample data days of the interest rate derivative products; according to the interest rate curve and a preset product transaction protocol, determining a long-term interchange interest rate of each deadline of the interest rate derivative product; according to the long-term interchange interest rate of each deadline, determining the option fluctuation rate of each deadline; and obtaining a pricing model and a fixed parameter of the interest rate derivative product, and determining a target price of the interest rate derivative product according to the pricing model, the option volatility of each period and the fixed parameter. According to the method provided by the invention, the objectivity and the dynamic adaptability of the option volatility calculation of each period are improved, and the target pricing of interest rate derived products is closer to the market reality.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium and product for processing interest rate derivatives data. Background Technology

[0002] In the field of financial engineering, the fair pricing of interest rate derivatives is central to their trading and management. Pricing typically relies on simulating interest rate paths and calculating the present value of expected cash flows using the no-arbitrage principle. In this process, volatility, as a key parameter measuring interest rate uncertainty, directly determines the reliability of the pricing result through its accurate calculation.

[0003] In existing technologies, there are two main ways to obtain volatility data for interest rate derivatives: First, directly use real-time quotes from market traders and generate volatility surfaces through interpolation and other methods; second, use simplified models (such as the Black model) to fit a few key maturity points and then derive the entire surface.

[0004] However, the aforementioned existing technical solutions have significant limitations: they rely primarily on current market quotes or simplified mathematical models. Therefore, this traditional approach struggles to reflect real-time market changes, resulting in lagging pricing of interest rate derivatives and an inability to accurately capture market dynamics. Summary of the Invention

[0005] This application provides a data processing method, apparatus, device, medium, and product for interest rate derivatives, which analyzes the curve data of historical transactions of interest rate derivatives to determine the volatility of options for each period, improves the objectivity and dynamic adaptability of the calculation of option volatility for each period, and makes the target pricing of interest rate derivatives closer to the actual market.

[0006] In a first aspect, embodiments of this application provide a method for processing interest rate derivative product data, the method comprising:

[0007] Obtain historical transaction curve data and sample data for interest rate derivatives (days);

[0008] Construct an interest rate curve based on the curve data and sample data of interest rate derivatives;

[0009] Based on the interest rate curve and the pre-set product trading agreement, determine the forward swap rates for each maturity of the interest rate derivative product;

[0010] Determine the option volatility for each maturity based on the forward swap rates for each maturity;

[0011] Obtain the pricing model and fixed parameters for interest rate derivatives, and determine the target price of interest rate derivatives based on the pricing model, option volatility for each maturity, and fixed parameters.

[0012] In one possible implementation, obtaining historical transaction curve data and sample data days for interest rate derivatives includes:

[0013] In response to the user's confirmation of interest rate derivative product transaction information, the product currency, product interest rate indicator, and sample data days are confirmed, where the sample data days include the current day and the preset number of days prior to the current day.

[0014] Based on currency and interest rate indicators, obtain the forward curve and the discount curve of the target transaction within a preset number of days before and on the current day as curve data for interest rate derivatives.

[0015] In one possible implementation, the interest rate curve includes a forward curve for the current day and a forward curve for a predetermined number of days prior to the current day, and a discount curve for the current day.

[0016] Accordingly, based on the interest rate curve and the pre-defined product trading agreement, the forward swap rates for each maturity of the interest rate derivative are determined, including:

[0017] Based on the forward curve and discount curve of the day, and combined with the pre-set product transaction agreement, the execution interest rate is calculated to obtain the forward swap rate for a certain term.

[0018] The execution rate is calculated for each term from the forward curve within the preset number of days prior to the current day, resulting in the forward swap rate for each term within the preset number of days prior to the current day.

[0019] In one possible implementation, the option volatility for each maturity is determined based on the forward swap rate for each maturity, including:

[0020] The difference between adjacent terms is obtained by subtracting adjacent terms from the forward swap rates for each term.

[0021] The volatility of options for each period is determined by calculating the normal distribution based on the adjacent differences between multiple adjacent periods.

[0022] In one possible implementation, the option volatility for each maturity is determined based on the forward swap rate for each maturity, including:

[0023] The weighting coefficients for each term are assigned based on the forward swap rates for each term. The sum of the weighting coefficients for each term is 1, and the weighting coefficients for each term increase day by day as they approach the current date.

[0024] Determine the average interest rate based on the forward swap rates for each maturity;

[0025] The option volatility for each maturity is determined based on the forward swap rate, weighting factor, and average interest rate for each maturity.

[0026] In one possible implementation, the fixed parameters include the exercise rate, the option term, and the initial market rate;

[0027] Based on the pricing model, option volatility for each maturity, and fixed parameters, determine the target price for interest rate derivatives, including:

[0028] The target price of interest rate derivatives is calculated by inputting the strike rate, option term, initial market rate, and option volatility for each term into the pricing model.

[0029] In one possible implementation, it also includes:

[0030] Based on the interest rate curve and pre-defined product trading rules, multiple different strike prices are determined for the interest rate derivative product;

[0031] Based on multiple different strike prices, option volatility, and the smile effect of volatility surfaces, the volatility of non-at-the-money options at strike prices is determined.

[0032] Secondly, embodiments of this application provide an interest rate derivatives data processing apparatus, comprising:

[0033] The acquisition module is used to acquire historical transaction curve data and sample data for interest rate derivatives over a period of time.

[0034] The data processing module is used to construct interest rate curves based on the curve data and sample data of interest rate derivatives.

[0035] The data processing module is also used to determine the forward swap rates for each maturity of interest rate derivatives based on the interest rate curve and the preset product trading agreement.

[0036] The data processing module is also used to determine the option volatility for each maturity based on the forward swap rates for each maturity.

[0037] The determination module is used to obtain the pricing model and fixed parameters of interest rate derivatives, and determine the target price of interest rate derivatives based on the pricing model, the volatility of options for each maturity, and the fixed parameters.

[0038] In one possible implementation, the acquisition module includes: a response confirmation module and a data acquisition module;

[0039] The response confirmation module is used to respond to the user's confirmation operation on the interest rate derivative product transaction information, confirming the product currency, product interest rate indicator and sample data days, where the sample data days include the current day and the preset number of days before the current day;

[0040] The data acquisition module is used to obtain the forward curve and the discount curve of the target transaction within a preset number of days before the current day, based on the currency and interest rate indicators, as curve data for interest rate derivative products.

[0041] In one possible implementation, in the data processing module, the interest rate curve includes the forward curve for the current day and the forward curve for a preset number of days prior to the current day, and the discount curve for the current day.

[0042] Accordingly, the data processing module includes: a daily swap rate module and swap rate modules for various maturities;

[0043] The same-day swap rate module is used to calculate the execution rate price based on the forward curve and the discount curve of the day, combined with the preset product transaction agreement, to obtain the forward swap rate for a certain term.

[0044] The term swap rate module is used to calculate the execution rate for each term from the forward curve within a preset number of days prior to the current day, and obtain the forward swap rate for each term within the preset number of days prior to the current day.

[0045] In one possible implementation, the data processing module is further configured to perform adjacent subtraction based on the forward swap rates for each maturity to obtain adjacent differences between multiple adjacent maturities.

[0046] The data processing module is also used to perform normal distribution calculations based on the adjacent differences between multiple adjacent periods to determine the option volatility for each period.

[0047] In one possible implementation, the data processing module is further configured to perform a weighting coefficient assignment operation based on the forward swap rates of each term to obtain the weighting coefficients of each term, wherein the sum of the weighting coefficients of each term is 1, and the weighting coefficients corresponding to each term increase day by day according to the timeline that is closer to the current day.

[0048] The data processing module is also used to determine the average interest rate based on the forward swap rates for each maturity.

[0049] The data processing module is also used to determine the option volatility for each maturity based on the forward swap rate, weighting coefficient, and average interest rate for each maturity.

[0050] In one possible implementation, the fixed parameters in the determining module include the exercise rate, the option term, and the initial market rate.

[0051] The determination module is also used to calculate the target price of interest rate derivatives based on the input pricing model of the strike rate, option term, initial market rate, and option volatility for each term.

[0052] In one possible implementation, the apparatus further includes: a module for determining the strike price and a module for determining volatility;

[0053] The strike price determination module is used to determine multiple different strike prices for interest rate derivatives based on the interest rate curve and preset product trading rules;

[0054] The volatility determination module is used to determine the volatility of non-at-the-money option strike prices based on multiple different strike prices, option volatility, and the smile effect of volatility surfaces.

[0055] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0056] The memory stores computer-executed instructions;

[0057] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0058] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0059] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0060] The interest rate derivative data processing method, apparatus, equipment, medium, and product provided in this application include: first, acquiring historical transaction curve data and sample data days of interest rate derivatives; then, constructing an interest rate curve based on the interest rate derivative curve data and sample data days; next, determining the forward swap rates for each maturity of the interest rate derivative based on the interest rate curve and a preset product trading agreement; then, determining the option volatility for each maturity based on the forward swap rates for each maturity; and finally, acquiring the pricing model and fixed parameters of the interest rate derivative, and determining the target price of the interest rate derivative based on the pricing model, the option volatility for each maturity, and the fixed parameters. This achieves the following technical effects: by deeply analyzing the historical transaction curve data of interest rate derivatives, the option volatility for each maturity is accurately determined, effectively improving the objectivity of the option volatility calculation for each maturity and avoiding interference from human factors and short-term market fluctuations. Simultaneously, by adjusting the calculation results of the option volatility for each maturity in real time according to changes in the historical transaction curve data, the calculated option volatility for each maturity is more closely aligned with actual market dynamics, enhancing its dynamic adaptability to market changes. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0063] Figure 1 A flowchart illustrating a data processing method for interest rate derivatives provided in this application embodiment;

[0064] Figure 2 A schematic diagram of the structure of an interest rate derivatives data processing device provided in an embodiment of this application;

[0065] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0066] Figure label:

[0067] 210 - Acquisition Module; 220 - Data Processing Module; 230 - Determination Module;

[0068] 310 - Processor; 320 - Memory; 330 - Communication components; 340 - Bus. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0070] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply difference. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0071] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the interest rate derivative data processing method provided in the embodiments of this application is merely an example; an interest rate derivative data processing method may also include more or less content.

[0072] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0073] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0074] Interest rate derivatives pricing plays a crucial role in the financial sector, with its core objective being to accurately determine the fair value of various interest rate-related financial derivatives. Due to the inherent uncertainty of interest rates' future paths, interest rate derivatives pricing requires reasonable modeling of possible future interest rate movements and strict adherence to the no-arbitrage principle to calculate the discounted value of expected returns, thereby providing market participants with a valuable pricing basis.

[0075] Within the current technological framework, there are certain limitations in determining volatility data during the pricing process of interest rate derivatives. Common practices include directly using trader quotes to generate volatility surfaces, or determining volatility data based on time-based quotes and simplified models (such as the Black model).

[0076] However, the aforementioned existing technical solutions have significant drawbacks: First, traditional volatility surfaces heavily rely on instantaneous market quotes. When market liquidity is insufficient or sharp fluctuations occur, market quotes are easily influenced by factors such as trading activity, and are not entirely based on the intrinsic value of interest rate derivatives themselves. This can lead to missing or distorted quote data, resulting in unstable volatility surface construction. Second, simplified models are typically based on static parameter assumptions, oversimplifying complex dynamic changes in interest rates and failing to fully capture the subtle characteristics and trends of the interest rate market. This causes pricing results based on such volatility surfaces to often lag behind market changes, lacking objectivity and dynamic adaptability, thus affecting the accuracy and reliability of interest rate derivative pricing.

[0077] Based on this, embodiments of this application provide a method, apparatus, device, medium, and product for processing data of interest rate derivatives, which can be used in the field of data processing technology and aims to solve the above-mentioned technical problems of the prior art. By deeply analyzing the historical trading curve data of interest rate derivatives, the volatility of options for each maturity is accurately determined, effectively improving the objectivity of the calculation of option volatility for each maturity and avoiding interference from human factors and short-term market fluctuations. At the same time, by adjusting the calculation results of option volatility for each maturity in real time according to changes in historical trading curve data, the calculated option volatility for each maturity is made to better reflect the actual market dynamics, enhancing its dynamic adaptability to market changes. Based on such accurate and dynamically adjusted option volatility for each maturity, target pricing for interest rate derivatives can be further performed, making the pricing results closer to the actual market, improving the scientificity and reliability of the pricing mechanism, and providing market participants with a more reliable and timely pricing reference.

[0078] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0079] According to the first aspect of this application, this embodiment discloses a method for processing interest rate derivative product data.

[0080] Figure 1 This is a flowchart illustrating a data processing method for interest rate derivatives provided in an embodiment of this application. Figure 1 As shown, the method includes:

[0081] S101. Obtain historical transaction curve data and sample data for interest rate derivatives.

[0082] In this embodiment, the execution entity of the interest rate derivative product data processing method can be a data processing server or other devices with data processing capabilities, such as smartphones, laptops, personal computers, tablets, etc. The data processing server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server, etc., without specific limitations. For ease of description, this embodiment uniformly describes the execution entity of an interest rate derivative product data processing method as a server.

[0083] Specifically, curve data can refer to interest rate curves formed by historical transactions in the market, such as yield curves, interbank lending rate curves, and swap rate curves.

[0084] Sample data days refer to the time span of historical data used for modeling (such as the past 30 days, 90 days, 100 days, 1 year, etc.).

[0085] By acquiring historical transaction curve data and sample data days of interest rate derivatives, a real market data foundation can be provided for subsequent construction of interest rate curves.

[0086] In one possible implementation, obtaining historical transaction curve data and sample data days for interest rate derivatives includes: in response to a user's confirmation operation on interest rate derivative transaction information, confirming the product currency, product interest rate indicator, and sample data days, wherein the sample data days include the current day and a preset number of days prior to the current day. Based on the currency and interest rate indicator, obtaining the forward curve for the target transaction within the preset number of days prior to the current day and the discount curve for the current day as the curve data for the interest rate derivative.

[0087] Specifically, users can select or input the currency of the interest rate derivative product, the benchmark interest rate such as the product interest rate indicator, and a specified historical data time range, such as "the past 100 trading days" or "the past year," through a user interface (such as the front end of a trading system). The product interest rate indicator can be a floating rate.

[0088] Users can trigger the subsequent data acquisition process by clicking buttons such as "Confirm," "Submit," or "Start Pricing."

[0089] By receiving user confirmation, the server can parse parameters such as product currency, product interest rate indicators, and sample data days. This enables customized data acquisition on demand, avoids loading irrelevant data, and improves efficiency and accuracy.

[0090] The sample data days is a time interval in the form of [TN, T], where T is the current trading day (today), and N is the number of days set by the user (e.g., 30, 90, 100, 180). The data range of the sample data days covers all trading days from TN to T.

[0091] For example, if the current date is September 8, 2025, and the user sets the sample data days to 30 days, the actual data date range obtained by the server will be from August 9, 2025 to September 8, 2025.

[0092] Furthermore, the target transaction can refer to a specific interest rate derivative contract that the user is currently operating or pricing. The forward curve is constructed based on interest rate indicators and is a forward curve representing a predetermined number of business days in advance. The discount curve is the discount curve for the current day. The forward curve includes historical curves for each day (N+1 curves in total), which can be used to analyze interest rate changes and calculate volatility. The discount curve, representing the current day, can be used for cash flow discounting during final pricing.

[0093] By only acquiring data on the currency, interest rate, and time range specified by the user, resource waste is avoided. This allows for pricing of interest rate derivatives applicable to different regions and markets.

[0094] S102. Construct an interest rate curve based on the curve data and sample data of interest rate derivatives.

[0095] Specifically, the server can generate multiple interest rate curves reflecting changes in market interest rates, corresponding to the user-input sample data days, based on the obtained curve data and sample data days, using mathematical methods such as interpolation and fitting. The interest rate curves can include N+1 curves reflecting market responses to forward rates for various maturities in the past, and one discount curve for the current day used to discount cash flows at their present value. These interest rate curves are the fundamental input data for pricing interest rate derivatives.

[0096] S103. Determine the forward swap rates for each maturity of the interest rate derivative product based on the interest rate curve and the pre-set product transaction agreement.

[0097] In this embodiment of the application, the forward swap rate refers to the fixed interest rate level of interest rate swaps conducted within a preset number of days in the past, such that the initial value of the swap contract is zero.

[0098] Pre-set product transaction agreements refer to the contractual terms of interest rate derivatives, such as: swap term (e.g., 5-year term), reset frequency (e.g., reset every 3 months), notional principal, etc.

[0099] The server can calculate forward swap rates for different start and end times based on the constructed interest rate curve and the no-arbitrage pricing principle.

[0100] In one possible implementation, the interest rate curve includes a forward curve for the current day and a forward curve for a preset number of days prior to the current day, and a discount curve for the current day.

[0101] Accordingly, based on the interest rate curve and the pre-defined product trading agreement, the forward swap rates for each maturity of the interest rate derivative are determined, including:

[0102] Based on the forward curve and discount curve of the day, and combined with the preset product transaction agreement, the execution rate price is calculated to obtain the forward swap rate for a certain term; the execution rate is calculated for each term from the forward curve within a preset number of days prior to the day to obtain the forward swap rates for each term within a preset number of days prior to the day.

[0103] Specifically, a pre-set product transaction agreement refers to the standardized terms of an interest rate derivative contract, including the swap structure (interest payment frequency, interest base, notional principal, etc.).

[0104] The server can obtain forward rates for various maturities using forward curves, and discount past cash flows to the present using discount curves to obtain the discount factor. Then, based on the formula for calculating forward swap rates, it can obtain the fixed interest rate that makes the net present value of the swap contract zero, i.e., the forward swap rate for each maturity.

[0105] The formula for calculating the forward swap rate is:

[0106]

[0107] Where K is the forward swap rate, n is the preset number of days, i=1 represents the current day, and F i ∆T is the forward rate. i Let df(T) be the length of the time period. i ) is the discount factor.

[0108] The server can use the forward curves for each maturity and the discount curve for the current day to calculate the forward swap rate for each maturity.

[0109] S104. Determine the option volatility for each maturity based on the forward swap rates for each maturity.

[0110] In this embodiment of the application, option volatility reflects the uncertainty of past interest rate changes and is a core parameter for option pricing.

[0111] Specifically, after determining the forward swap rates for each maturity, the server can further determine the option volatility for each maturity based on the forward swap rates for each maturity.

[0112] In one possible implementation, the option volatility for each maturity is determined based on the forward swap rate for each maturity, including:

[0113] The forward swap rates for each maturity are subtracted from each other to obtain the adjacent differences between multiple adjacent maturities. The option volatility for each maturity is then determined by calculating the normal distribution of the adjacent differences between multiple adjacent maturities.

[0114] Specifically, according to the normal model, forward swap rates for all maturities follow a normal distribution. The stochastic differential equation governing forward swap rates is:

[0115]

[0116] Here, ds represents a small change in the forward swap rate. σ is the normally distributed volatility, which can be used to measure the degree of volatility in the forward swap rate. dw is the stochastic factor driving the random change in the forward swap rate; it is a small change in Brownian motion, used to describe the random fluctuation of the forward swap rate, representing a small increment of random fluctuation. It is normally distributed with a mean of 0, and its variance is proportional to the time interval.

[0117] In the discrete case, ds can be approximated as discrete ΔS, i.e., ΔS = S. t+Δt -S t S t ΔS is the forward swap rate at time t. According to the normal model, ΔS follows a normal distribution with variance:

[0118]

[0119] Where ΔS represents the change in forward swap rates, i.e., the difference between forward swap rates at adjacent time points, and V[∆S] is the variance of ΔS. σ represents the volatility of forward swap rates, i.e., the volatility of options at various maturities. Δt is the time interval, where Δt represents the time difference between adjacent data points, i.e., 1 day. t Let ΔS be the t-th sample, representing the adjacent differences in forward swap rates for adjacent maturities. μ is the mean of ΔS; that is, when calculating the variance of ΔS, it is necessary to consider the variance of each ΔS sample. t Subtract the mean μ from the mean to obtain the deviation from the mean.

[0120] Therefore, given a known time interval Δt (1 day) and the fact that the change in ΔS follows a normal distribution, the server can calculate the option volatility σ for each period by calculating the sample variance. Specifically, the sample variance of ΔS can be calculated first, then divided by Δt and the square root taken to obtain the option volatility σ.

[0121] In one possible implementation, in addition to the equal-weighted volatility calculation method, the server can also choose to increase the weight of the recent curve and use a weighted calculation method. Specifically, the option volatility for each maturity is determined based on the forward swap rates for each maturity, including:

[0122] Weighting coefficients are assigned to forward swap rates for each maturity, resulting in weighted coefficients for each maturity. The sum of the weighted coefficients for all maturities is 1, and the weighted coefficients for each maturity increase daily as they approach the current date. The mean interest rate is determined based on the forward swap rates for each maturity. Finally, the option volatility for each maturity is determined based on the forward swap rates, weighted coefficients, and mean interest rate.

[0123] Specifically, the weighting coefficients for each period are represented as follows:

[0124]

[0125] Among them, w t Here, A represents the weighting coefficients for each period, and n is the normalization coefficient, used to ensure that the sum of the weighting coefficients for each period is 1. λ is the preset number of days. λ is the decay factor, which can take values ​​between 0 and 1, for example, 0.95. The value of λ determines the rate at which the weighting coefficients decay over time; the closer λ is to 0, the more uniform the distribution of the weighting coefficients.

[0126] The weighting coefficients are equivalent to a probability distribution, allowing option volatility to react promptly to recent market upheavals. However, when this upheaval occurs on day n, and the market is about to leave the sample set, because w... n It's very small and won't have a significant impact on option volatility.

[0127] At this point, the forward swap rate S t The average interest rate is:

[0128]

[0129] in, This represents the average interest rate for forward swaps.

[0130] Furthermore, the formula for calculating the volatility of options at different maturities can be:

[0131]

[0132] Where σ represents the option volatility for each maturity period.

[0133] In one possible implementation, the server can also determine multiple different strike prices for interest rate derivatives based on the interest rate curve and preset product trading rules. The volatility under the non-at-the-money option strike price is then determined based on the multiple different strike prices, option volatility, and the smile effect of the volatility surface.

[0134] Specifically, using the methods described above, the server can calculate the volatility of at-the-money (ATM) options at different maturities. If it is necessary to extend the calculation to volatility at different strike prices, the server can also combine the smile effect of the volatility surface to calculate the volatility at non-ATM strike prices.

[0135] Specifically, the formula for calculating volatility at non-ATM strike prices is as follows:

[0136]

[0137] Where SkewVol is the volatility at the non-ATM strike price, and AtmVol is the volatility at the at-the-money (ATM) option price. α is the slope angle of the volatility curve, and β is the opening size of the volatility curve. Both α and β are adjustment parameters that can be used to quantify the sensitivity of volatility to changes in strike price. The difference between the strike price and the at-the-money option is defined as:

[0138] =Strike−ATM

[0139] Strike represents the execution price.

[0140] S105. Obtain the pricing model and fixed parameters of the interest rate derivative, and determine the target price of the interest rate derivative based on the pricing model, the volatility of options for each maturity, and the fixed parameters.

[0141] In this embodiment of the application, the server can further calculate the target price (i.e. option price) of the interest rate derivative based on the attribute model of the theoretical price of European options, such as the Black-Scholes (BS) model.

[0142] In one possible implementation, the fixed parameters include the strike rate, option term, and initial market interest rate. Based on the pricing model, option volatility for each term, and the fixed parameters, the target price of the interest rate derivative is determined, including:

[0143] The target price of interest rate derivatives is calculated by inputting the strike rate, option term, initial market interest rate, and option volatility for each term into a pricing model (such as the Black-Scholes model).

[0144] Since the pricing model here can adopt a model that is commonly found in existing technologies (such as the BS model), the pricing model will not be described in detail here.

[0145] By processing historical transaction curve data of multiple currencies, the volatility of options for each term is determined, which improves the objectivity and dynamic adaptability of the volatility calculation for options for each term, making the target pricing of the interest rate derivatives more closely reflect the actual market.

[0146] This embodiment provides a data processing method for interest rate derivatives, comprising: first, acquiring historical trading curve data and sample data days of interest rate derivatives; then, constructing an interest rate curve based on the curve data and sample data days; next, determining the forward swap rates for each maturity of the interest rate derivatives based on the interest rate curve and a preset product trading agreement; then, determining the option volatility for each maturity based on the forward swap rates for each maturity; finally, acquiring the pricing model and fixed parameters of the interest rate derivatives, and determining the target price of the interest rate derivatives based on the pricing model, the option volatility for each maturity, and the fixed parameters. This achieves the following technical effects: by deeply analyzing the historical trading curve data of interest rate derivatives, the option volatility for each maturity is accurately determined, effectively improving the objectivity of the option volatility calculation for each maturity and avoiding interference from human factors and short-term market fluctuations. Simultaneously, by adjusting the calculated results of the option volatility for each maturity in real time according to changes in the historical trading curve data, the calculated option volatility for each maturity is more closely aligned with actual market dynamics, enhancing its dynamic adaptability to market changes. By acquiring historical trading curve data and sample data days for interest rate derivatives, a real market data foundation is provided for subsequent construction of interest rate curves. Upon receiving user confirmation, the server can parse parameters such as the product currency, product interest rate indicator, and sample data days, enabling customized data acquisition on demand, avoiding the loading of irrelevant data, and improving efficiency and accuracy. By acquiring only the user-specified product currency, product interest rate indicator, and time range, resource waste is avoided, making it suitable for pricing interest rate derivatives in different regions and markets.

[0147] In this embodiment of the invention, electronic devices or main control devices can be divided into functional modules according to the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the module division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0148] Figure 2 This is a schematic diagram of the structure of an interest rate derivatives data processing device provided in an embodiment of this application. Figure 2 As shown, the interest rate derivative data processing device includes: an acquisition module 210, a data processing module 220, and a determination module 230.

[0149] The acquisition module 210 is used to acquire historical transaction curve data and sample data days of interest rate derivatives.

[0150] The data processing module 220 is used to construct an interest rate curve based on the curve data and sample data of interest rate derivatives.

[0151] The data processing module 220 is also used to determine the forward swap rates for each term of interest rate derivative products based on the interest rate curve and the preset product transaction agreement.

[0152] The data processing module 220 is also used to determine the option volatility for each maturity based on the forward swap rates for each maturity.

[0153] The determination module 230 is used to obtain the pricing model and fixed parameters of the interest rate derivative, and to determine the target price of the interest rate derivative based on the pricing model, the volatility of options for each maturity, and the fixed parameters.

[0154] In one possible implementation, the acquisition module 210 includes a response confirmation module and an acquisition data module.

[0155] The response confirmation module is used to respond to the user's confirmation operation on the interest rate derivative product transaction information, confirming the product currency, product interest rate indicator, and sample data days, where the sample data days include the current day and the preset number of days prior to the current day.

[0156] The data acquisition module is used to obtain the forward curve and the discount curve of the target transaction within a preset number of days before the current day, based on the currency and interest rate indicators, as curve data for interest rate derivative products.

[0157] In one possible implementation, in the data processing module 220, the interest rate curve includes the forward curve for the current day and the forward curve for a preset number of days prior to the current day, as well as the discount curve for the current day.

[0158] Accordingly, the data processing module 220 includes: a daily swap rate module and swap rate modules for each maturity.

[0159] The same-day swap rate module is used to calculate the execution rate price based on the forward curve and the discount curve of the day, combined with the preset product transaction agreement, to obtain the forward swap rate for a given term.

[0160] The term swap rate module is used to calculate the execution rate for each term from the forward curve within a preset number of days prior to the current day, and obtain the forward swap rate for each term within the preset number of days prior to the current day.

[0161] In one possible implementation, the data processing module 220 is further configured to perform adjacent subtraction processing based on the forward swap rates of each term to obtain adjacent differences of multiple adjacent terms.

[0162] The data processing module 220 is also used to perform normal distribution calculations based on the adjacent differences of multiple adjacent periods to determine the option volatility for each period.

[0163] In one possible implementation, the data processing module 220 is further configured to perform a weighting coefficient assignment operation based on the forward swap rates of each term to obtain the weighting coefficients of each term, wherein the sum of the weighting coefficients of each term is 1, and the weighting coefficients corresponding to each term increase day by day according to the timeline closest to the current day.

[0164] The data processing module 220 is also used to determine the average interest rate based on the forward swap rates for each maturity.

[0165] The data processing module 220 is also used to determine the option volatility for each maturity based on the forward swap rate, weighting coefficient and average interest rate for each maturity.

[0166] In one possible implementation, in the determining module 230, the fixed parameters include the exercise rate, the option term, and the initial market rate.

[0167] The determination module 230 is also used to calculate the target price of interest rate derivatives based on the input pricing model of the exercise rate, option term, initial market rate, and option volatility for each term.

[0168] In one possible implementation, the device further includes a module for determining the strike price and a module for determining volatility.

[0169] The strike price determination module is used to determine multiple different strike prices for interest rate derivatives based on the interest rate curve and preset product trading rules.

[0170] The volatility determination module is used to determine the volatility of non-at-the-money option strike prices based on multiple different strike prices, option volatility, and the smile effect of volatility surfaces.

[0171] This embodiment provides an interest rate derivatives data processing device that can execute the methods provided in the above-described method embodiments. Its implementation principle and technical effects are similar, and will not be described in detail here.

[0172] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. Figure 3 As shown, the electronic device provided in this embodiment includes at least one processor 310 and a memory 320. The electronic device also includes a communication component 330. The processor 310, memory 320, and communication component 330 are connected via a bus 340.

[0173] In the specific implementation process, at least one processor 310 executes computer execution instructions stored in memory 320, causing at least one processor 310 to execute an interest rate derivative data processing method as executed on the electronic device side.

[0174] The specific implementation process of processor 310 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0175] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0176] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0177] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0178] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0179] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0180] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0181] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0182] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0185] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0187] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing data on interest rate derivatives, characterized in that, include: Obtain historical transaction curve data and sample data for interest rate derivatives (days); Construct an interest rate curve based on the curve data of the interest rate derivatives and the sample data days; Based on the interest rate curve and the preset product trading agreement, determine the forward swap rates for each maturity of the interest rate derivative product; Determine the option volatility for each maturity based on the forward swap rates for each maturity; Obtain the pricing model and fixed parameters of the interest rate derivative, and determine the target price of the interest rate derivative based on the pricing model, the option volatility of each term, and the fixed parameters.

2. The method according to claim 1, characterized in that, The acquisition of historical transaction curve data and sample data days for interest rate derivatives includes: In response to the user's confirmation operation on the transaction information of interest rate derivatives, the product currency, product interest rate indicator and sample data days are confirmed, wherein the sample data days include the current day and the preset number of days before the current day; Based on the currency and the interest rate indicator, obtain the forward curve and the discount curve of the target transaction within a preset number of days before and on the current day as the curve data for interest rate derivative products.

3. The method according to claim 2, characterized in that, The interest rate curve includes the forward curve for the current day and the forward curve for the number of days prior to the current day, and the discount curve for the current day. Accordingly, determining the forward swap rates for each maturity of the interest rate derivative product based on the interest rate curve and the preset product trading agreement includes: Based on the forward curve and discount curve of the day, and combined with the pre-set product transaction agreement, the execution interest rate is calculated to obtain the forward swap rate for a certain term. The execution rate is calculated for each term from the forward curve within the preset number of days prior to the current day, resulting in the forward swap rate for each term within the preset number of days prior to the current day.

4. The method according to claim 1, characterized in that, The determination of option volatility for each maturity based on forward swap rates for each maturity includes: The difference between adjacent terms is obtained by subtracting adjacent terms from the forward swap rates for each term. The option volatility for each period is determined by calculating the normal distribution based on the adjacent differences between the multiple adjacent periods.

5. The method according to claim 1, characterized in that, The determination of option volatility for each maturity based on forward swap rates for each maturity includes: The weighting coefficients for each term are assigned based on the forward swap rates for each term, and the sum of the weighting coefficients for each term is 1. The weighting coefficients for each term increase day by day according to the timeline closest to the current day. Determine the average interest rate based on the forward swap rates for each maturity; The option volatility for each maturity is determined based on the forward swap rate, weighting factor, and the average rate for each maturity.

6. The method according to claim 1, characterized in that, The fixed parameters include the exercise rate, option term, and initial market rate. The step of determining the target price of interest rate derivatives based on the pricing model, option volatility for each maturity, and fixed parameters includes: The target price of interest rate derivatives is calculated by inputting the strike rate, option term, initial market rate, and option volatility for each term into the pricing model.

7. The method according to claim 5, characterized in that, Also includes: Based on the interest rate curve and the preset product trading rules, determine multiple different strike prices for the interest rate derivative product; The volatility of non-at-the-money option strike prices is determined based on multiple different strike prices, the option volatility, and the smile effect of the volatility surface.

8. A data processing device for interest rate derivatives, characterized in that, include: The acquisition module is used to acquire historical transaction curve data and sample data for interest rate derivatives over a period of time. The data processing module is used to construct an interest rate curve based on the curve data of the interest rate derivative and the sample data days. The data processing module is also used to determine the forward swap rates for each term of the interest rate derivative product based on the interest rate curve and the preset product trading agreement. The data processing module is also used to determine the option volatility for each maturity based on the forward swap rates for each maturity. The determination module is used to obtain the pricing model and fixed parameters of the interest rate derivative, and determine the target price of the interest rate derivative based on the pricing model, the option volatility of each term, and the fixed parameters.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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