Except pricing method, apparatus and device, and storage medium

Abstract

The invention discloses an option pricing method and device, equipment and a storage medium. Firstly, an initial index of a subject corresponding to a to-be-priced option is acquired; secondly, acquiring an initial distribution function of time required by the subject to complete each stage and initial probability measurement of indexes of the subject after each stage is completed; based on the initial distribution function, obtaining a target distribution function of time required for completing each stage according to the current index of the subject in each stage; based on the initial probability measure, according to the current index of the subject in each stage and the decision, obtaining a target probability measure of the index of the subject after each stage is completed; based on the initial index, the target distribution function of each stage and the target probability measure of each stage, determining the overall maximum income of the plurality of stages; and finally, pricing the option to be priced as a price not less than the overall maximum income. According to the invention, pricing is carried out on the randomly distributed options of which the time interval of the change of the main body index is any given.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an option pricing method, device, equipment and storage medium. Background Art

[0002] Markov process (also known as Markov process) is a type of random process, whose core characteristic is that its future state depends only on the current state and has nothing to do with the past historical state. This characteristic allows the Markov process to be used to describe the dynamics of the subject indicator and thus realize option pricing.

[0003] At present, in business operations, financial transactions and other scenarios, it is often necessary to combine the activities, projects, behaviors and other data of the subject to comprehensively evaluate the subject's indicators. Therefore, the time intervals of changes in the subject's indicators are mostly randomly distributed. However, the traditional Markov process assumes that the time intervals of changes in the subject's indicators are fixed, which is inconsistent with the actual situation, and thus leads to deviations in option pricing. Therefore, how to perform option pricing when the time intervals of changes in the subject's indicators are randomly distributed is an urgent problem to be solved. Summary of the invention

[0004] Based on the above problems, the present application provides an option pricing method, device, equipment and storage medium, with the purpose of pricing options with a random distribution of any given time interval between changes in the subject index.

[0005] The embodiments of the present application disclose the following technical solutions:

[0006] The first aspect of the present application provides an option pricing method, the method comprising:

[0007] Obtaining the initial index of the subject corresponding to the option to be priced, wherein the initial index is the index of the subject when pricing the option, and the subject needs to complete multiple stages, and the index of the subject changes at the end of each stage;

[0008] Obtaining an initial distribution function of the time required for the subject to complete each stage and an initial probability measure of the subject's indicator after completion of each stage;

[0009] Based on the initial distribution function, a target distribution function of the time required to complete each stage is obtained according to the current indicators of the subject in each stage;

[0010] Based on the initial probability measure, a target probability measure of the subject's indicator after completing each stage is obtained according to the subject's current indicator and decision at each stage, wherein the decision is to use the option or not to use the option;

[0011] Determining the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage;

[0012] The option to be priced is priced at a price not less than the overall maximum benefit.

[0013] In an optional implementation, the target distribution function of the time required to complete each stage is obtained based on the initial distribution function and according to the current indicators of the subject in each stage, including:

[0014] The subject's index includes an actual index and a virtual index. When the subject's index belongs to the virtual index, it means that the option has been used. When the subject's index belongs to the actual index, it means that the option has not been used.

[0015] If the subject's current indicator belongs to the actual indicator, the target distribution function is the initial distribution function;

[0016] If the subject's current indicator belongs to the virtual indicator, the target distribution function is an indicator function that is 1 on the interval [1, +∞) and 0 elsewhere.

[0017] In an optional implementation, the step of obtaining a target probability measure of the subject's indicator after completing each stage based on the initial probability measure and according to the subject's current indicator and decision at each stage includes:

[0018] If the subject's current indicator belongs to the actual indicator, when the decision is not to use the option, the target probability measure is the initial probability measure; when the decision is to use the option, the target probability measure is 1;

[0019] If the subject's current indicator belongs to the virtual indicator, the target probability measure is 1.

[0020] In an optional implementation, the determining the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage, and the target probability measure of each stage includes:

[0021] Obtaining a predetermined discount factor and a decision benefit function; wherein the decision benefit function ensures that benefits can be obtained only when the decision is to use the option for the first time;

[0022] determining a maximum benefit for a last stage in the plurality of stages;

[0023] Based on the initial indicator, the target distribution function of each stage, the target probability measure of each stage and the maximum benefit of the last stage, the overall maximum benefit of the multiple stages is determined using the discount coefficient and the decision benefit function.

[0024] In an optional implementation, determining the maximum benefit of the last stage among the multiple stages includes:

[0025] The maximum benefit of the last stage among the multiple stages is calculated based on the indicators, decisions and decision benefit function of the subject in the last stage.

[0026] In an optional implementation, before determining the overall maximum return of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage, the option pricing method further includes:

[0027] The discount factor is predetermined based on the time value of the subject's indicator.

[0028] The second aspect of the present application provides an option pricing device, which includes:

[0029] A first acquisition module is used to acquire the initial index of the subject corresponding to the option to be priced, wherein the initial index is the index of the subject when pricing the option, and the subject needs to complete multiple stages, and the index of the subject changes at the end of each stage;

[0030] A second acquisition module is used to obtain an initial distribution function of the time required for the subject to complete each stage and an initial probability measure of the subject's indicator after each stage is completed;

[0031] A target distribution function determination module is used to obtain a target distribution function of the time required to complete each stage based on the initial distribution function and the current indicators of the subject in each stage;

[0032] A target probability measurement determination module is used to obtain a target probability measurement of the subject's indicators after completing each stage based on the subject's current indicators and decisions at each stage, wherein the decision is to use the option or not to use the option;

[0033] An overall maximum benefit determination module, used to determine the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage;

[0034] An option pricing module is used to price the option to be priced at a price that is not less than the overall maximum benefit.

[0035] In an optional implementation, the overall maximum benefit determination module includes:

[0036] A third acquisition module is used to acquire a predetermined discount coefficient and a decision benefit function; wherein the decision benefit function ensures that benefits can be obtained only when the decision is to use the option for the first time;

[0037] A first profit determination module, used to determine the maximum profit of the last stage among the multiple stages;

[0038] The second profit determination module is used to determine the overall maximum profit of the multiple stages based on the initial indicators, the target distribution function of each stage, the target probability measure of each stage and the maximum profit of the last stage, using the discount coefficient and the decision profit function.

[0039] The third aspect of the present application provides an option pricing device, the device comprising:

[0040] a memory having a computer program stored thereon;

[0041] A processor is used to execute the computer program in the memory to implement the steps of the option pricing method introduced in any implementation manner of the first aspect.

[0042] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the option pricing method introduced in any implementation of the first aspect.

[0043] Compared with the prior art, this application has the following beneficial effects:

[0044] First, the initial indicators of the subject corresponding to the option to be priced are obtained; secondly, the initial distribution function of the time required for the subject to complete each stage and the initial probability measure of the subject's indicators after each stage are completed are obtained; again, based on the initial distribution function, the target distribution function of the time required to complete each stage is obtained according to the current indicators of the subject at each stage; then, based on the initial probability measure, the target probability measure of the subject's indicators after completing each stage is obtained according to the current indicators and decisions of the subject at each stage; then, based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage are determined to determine the overall maximum benefit of the multiple stages; finally, the option to be priced is priced at a price not less than the overall maximum benefit. It can be seen that in the technical solution of the present application, the target distribution function of the time required to complete each stage is dynamically adjusted based on the current subject's indicators, and the target probability measure of the subject's indicators at each stage is dynamically adjusted based on the current subject's indicators and decisions, which more truly reflects the random distribution of the time intervals of changes in the subject's indicators. At the same time, the technical solution of the present application not only considers the benefits of a single stage, but also evaluates the potential value of options by considering the overall maximum benefits of all stages, thereby ensuring the accuracy and rationality of pricing options with any given random distribution of the time interval between changes in the subject indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 A flow chart of an option pricing method provided in an embodiment of the present application;

[0047] Figure 2 A schematic diagram of the structure of an option pricing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] As described above, the current Markov process assumes that the time interval between changes in the underlying indicator is fixed, which is inconsistent with the actual situation. Therefore, deviations will occur when using the Markov process to price options whose time interval between changes in the underlying indicator is any given random distribution.

[0049] After research, the inventor proposed an option pricing method, device, equipment and storage medium.

[0050] First, the initial indicators of the subject corresponding to the option to be priced are obtained; secondly, the initial distribution function of the time required for the subject to complete each stage and the initial probability measure of the subject's indicators after the completion of each stage are obtained; again, based on the initial distribution function, the target distribution function of the time required to complete each stage is obtained according to the current indicators of the subject at each stage; then, based on the initial probability measure, the target probability measure of the subject's indicators after completing each stage is obtained according to the current indicators and decisions of the subject at each stage; then, based on the initial indicators, the target distribution function of each stage and the target probability measure of each stage, the overall maximum benefit of the multiple stages is determined; finally, the option to be priced is priced at a price not less than the overall maximum benefit.

[0051] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] An option is a contract that gives the holder the right to buy or sell an entity at a fixed exercise price on or before a specific date. The entity can be a company, stock, government bond or commodity futures. The option price refers to the price used to obtain the right granted by the option contract, also known as the premium.

[0053] See also Figure 1 , which is a flow chart of an option pricing method provided in an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0054] S101. Obtain the initial indicators of the subject corresponding to the option to be priced.

[0055] In the embodiment of the present application, an unpriced option refers to an option contract for which the specific amount of the premium has not yet been determined.

[0056] In this step, you first need to identify the subject of the option to be priced, for example, the subject of the option to be priced is a company or a stock. Then, you need to obtain the initial index of the subject, which is the index of the subject when pricing the option.

[0057] In the embodiment of the present application, the subject needs to complete multiple stages, and the subject's indicators will change at the end of each stage.

[0058] In an example implementation, the option contract is to acquire a company at a fixed execution price L within five years. The company's current indicator is x0, and there are multiple (such as 100) projects to be completed. The completion of each project is a determining factor affecting the company's indicators, that is, the company's indicators will change after each project is completed. The company will complete these projects in a fixed order within five years. After completing one project, it will start another project, and the time required for each project is independent and different random distribution. At this time, to price the option contract, the initial indicator of the subject is x0.

[0059] S102, obtaining an initial distribution function of the time required for the subject to complete each stage and an initial probability measure of the subject's indicators after completing each stage.

[0060] In this step, it is necessary to obtain the initial distribution functions of the time required for the subject to complete each stage. These initial distribution functions describe the randomness of the time required to complete each stage under different initial indicators. At the same time, it is also necessary to obtain the initial probability measures of the changes in the subject's indicators after completing each stage. These probability measures describe the different probability distributions of the subject's indicators after completing each stage under different indicators.

[0061] In an example implementation, assuming that the initial indicator of a company is x0, and the first project is completed after time t1, the initial distribution function of the time t1 required to complete the first stage is F ini 1(t|x0). As the completion of the project changes, the indicator changes to x1, and the initial probability measure of the distribution of indicator x1 is Q ini 1(B|x0) represents the probability measure of x1 in set B.

[0062] Next, the company will continue to carry out the second project and complete the second project after time t2. The initial distribution function of the time t2 required to complete the second stage is F ini 2(t|x1). As the completion of the project changes, the indicator changes to x2, and the initial probability measure of the distribution of indicator x2 is Q ini 2(B|x1) represents the probability measure of x2 in set B.

[0063] By analogy, when the company carries out the nth project, the company's indicator is x n-1 , after time t n If the nth project is completed later, the time required to complete the nth stage is t n The initial distribution function is F ini n (t|x n-1 ). As the project progresses, the indicator changes to x n , index x n The initial probability measure of the distribution is Q ini n (B|x n-1 ), indicating x n Probability measure on set B.

[0064] S103. Based on the initial distribution function, a target distribution function of the time required to complete each stage is obtained according to the current indicators of the subject in each stage.

[0065] In the option pricing process, as the project progresses and the subject's indicators change, the distribution function of the time required to complete each stage will also change accordingly. Therefore, in this step, it is necessary to dynamically calculate the target distribution function of the time required to complete each stage based on the current subject's indicators and the initial distribution function.

[0066] S104. Based on the initial probability measure, the target probability measure of the subject's indicators after completing each stage is obtained according to the current indicators and decisions of the subject at each stage.

[0067] In the embodiment of the present application, the subject makes a decision at the end of each stage to decide whether to use the option or not.

[0068] As the stages progress and different decisions are made, the subject's indicators will change after completing each stage, and this change is random. Therefore, in this step, it is necessary to dynamically calculate the subject's target probability measure after completing each stage based on the subject's current indicators, initial probability measure, and decision.

[0069] S105, determining the overall maximum benefit of multiple stages based on the initial indicators, the target distribution function of each stage and the target probability measure of each stage.

[0070] In the embodiment of the present application, the overall maximum benefit is the maximum expected benefit of the decision made by the subject at each stage starting from the initial indicator.

[0071] Combine the initial indicators, the target distribution function of each stage and the target probability measure of each stage to calculate the overall benefits under various possible scenarios (such as using options or not using options at each stage) and determine the overall maximum benefit.

[0072] S106. Price the option to be priced at a price that is not less than the overall maximum profit.

[0073] Finally, based on the calculated overall maximum return, the option to be priced is priced at a price not less than the maximum return. This price not only reflects the potential value of the option, but also ensures that the interests of the option issuer are not harmed.

[0074] In the embodiment of the present application, the target distribution function of the time required to complete each stage of the dynamic adjustment of the current subject indicator and the target probability measurement of the subject indicator of each stage of the dynamic adjustment based on the current subject's indicators and decisions more truly reflects the random distribution of the time interval of the subject indicator change. At the same time, not only the benefits of a single stage are considered, but also the potential value of the option is evaluated by considering the overall maximum benefits of all stages, thereby ensuring the accuracy and rationality of pricing options with any given random distribution of the time interval of the subject indicator change.

[0075] In the example of this application, it should be noted that the subject's indicators include actual indicators and virtual indicators. When the subject's indicator is a virtual indicator, it means that the option has been used. Since the option can only be used once, it can be considered that after the option is used, the subject's indicator becomes a virtual indicator and remains unchanged. The virtual indicator can be marked as △. When the subject's indicator is a virtual indicator, no decision can be made (i.e., whether to use the option); when the subject's indicator is an actual indicator, it means that the option is not used, and the range of the actual indicator is the value range of the subject's indicator. For example, when the subject is a company, the range of the actual indicator can be [0,10000000000].

[0076] Next, we will explain in more detail how to price options.

[0077] In an optional implementation, step S103 includes:

[0078] If the current indicator of the subject belongs to the actual indicator, the target distribution function is the initial distribution function, that is, F n (t|x,a)=F ini n (t|x). Where F n (t|x,a) is the target distribution function of the time required to complete the nth stage when the current indicator is x and belongs to the actual indicator, F ini n (t|x) is the initial distribution function of the time required to complete the nth stage when the current indicator is x and it is an actual indicator. a is the decision. When the decision is to use the option, a is 1, and when the decision is not to use the option, a is 0.

[0079] If the current indicator of the subject is a virtual indicator, the option has been used at this time, and the remaining stages are meaningless. It can be defined as a fixed time of each stage as 1 unit of time. Then the target distribution function is an indicator function that is 1 on the interval [1, +∞) and 0 elsewhere, that is, F n (t|△,a)=1 [1,+∞] (t).

[0080] In an optional implementation, step S104 includes:

[0081] In the embodiment of the present application, first, the target probability measure Q n (B|x,a) is the probability that the subject's indicator is within range B after selecting decision a and completing stage n when the subject's current indicator is x.

[0082] If the subject's current indicator is an actual indicator and the decision is not to use the option, the target probability measure is the initial probability measure, that is, Q n (B|x,0)=Q ini n (B|x); When the decision is to use the option, the target probability measure is 1, that is, Q n (B|x,1)=1 △∈B ; This means that when the decision is 0 and the option is not used, the main indicator will change normally. When the decision is 1 and the option is used, the main indicator will enter the virtual indicator and remain unchanged, indicating that the option has been used up.

[0083] If the subject's current indicator is a virtual indicator, no matter whether the decision is to use the option or not, the target probability measure is 1, that is, Q n (B|△,a)=1 △∈BSince there is time value, that is, current funds are more valuable than future funds, in an optional implementation, before step S105, the option pricing method further includes:

[0084] Based on the time value of the entity's indicators, a discount factor is determined in advance. The discount factor is used to reflect this difference in time value and discount future cash flows to current values.

[0085] In the embodiment of the present application, the discount coefficient is β.

[0086] In an optional implementation, step S105 includes:

[0087] Obtain a predetermined discount factor and decision benefit function; wherein the decision benefit function ensures that benefits can be obtained only when the decision is to use the option for the first time. Specifically, the decision benefit function is c(x,a), and when the decision function is equal to 0, it means no benefits.

[0088] When the decision is 1 to use the option, and the subject indicator x belongs to the actual indicator, c(x,1) = xb, where b is the fixed execution price agreed in the option.

[0089] When the decision is 0 and the option is not used, and the subject indicator x belongs to the actual indicator, c(x,0)=0.

[0090] When the main indicator is a virtual indicator, c(△,a)=0 regardless of the decision.

[0091] Determine the maximum benefit of the last stage in multiple stages:

[0092] First, the constructor J n (x) represents the maximum expected return that the subject can obtain in the nth stage starting from indicator x. This is a recursive function, namely:

[0093]

[0094] Where max is the maximum value, which can be achieved by selecting the value of decision a, A(x) is the decision range (including 0 not using the option and 1 using the option), +inf is positive infinity, x represents the main indicator, x n is the current indicator corresponding to the subject in the nth stage, a represents the decision, a n is the decision corresponding to the nth stage, c is the decision benefit function, β is the discount coefficient, t is the time, F n is the target distribution function of stage n, Q n is the target probability measure of stage n, X is the range of the subject indicator, and represents the range of the integral.

[0095] When there are 100 stages in total, the maximum benefit of the last stage is calculated based on the main quality assurance, decision and decision benefit function of the last stage as Jn(x)=max a∈A(x) c(x,a).

[0096] The maximum gain from the last stage J 100 (x) starts, and J is calculated forward once n (x) until J0(x0), the final result J0(x0) is the overall maximum benefit.

[0097] This process is to determine the overall maximum benefit of multiple stages based on the initial indicators, the target distribution function of each stage, the target probability measure of each stage and the maximum benefit of the last stage, using the discount coefficient and decision benefit function.

[0098] Based on the option pricing method provided in the aforementioned embodiment, the present application also provides an option pricing device accordingly. Figure 2 This is a schematic diagram of the structure of an option pricing device provided in an embodiment of the present application. Figure 2 As shown, the option pricing device includes:

[0099] The first acquisition module 201 is used to acquire the initial index of the subject corresponding to the option to be priced. The initial index is the index of the subject when pricing the option. The subject needs to complete multiple stages, and the index of the subject changes at the end of each stage.

[0100] The second acquisition module 202 is used to obtain the initial distribution function of the time required for the subject to complete each stage and the initial probability measure of the subject's indicator after each stage is completed;

[0101] The target distribution function determination module 203 is used to obtain the target distribution function of the time required to complete each stage based on the initial distribution function and the current indicators of the subject in each stage;

[0102] A target probability measure determination module 204 is used to obtain a target probability measure of the subject's indicator after completing each stage based on the subject's current indicator and decision at each stage, wherein the decision is to use the option or not to use the option;

[0103] An overall maximum benefit determination module 205, configured to determine the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage;

[0104] The option pricing module 206 is used to price the option to be priced to a price that is not less than the overall maximum benefit.

[0105] In the embodiment of the present application, the target distribution function of the time required to complete each stage of the dynamic adjustment of the current subject indicator and the target probability measurement of the subject indicator of each stage of the dynamic adjustment based on the current subject's indicators and decisions more truly reflects the random distribution of the time interval of the subject indicator change. At the same time, not only the benefits of a single stage are considered, but also the potential value of the option is evaluated by considering the overall maximum benefits of all stages, thereby ensuring the accuracy and rationality of pricing options with any given random distribution of the time interval of the subject indicator change.

[0106] Optionally, the overall maximum benefit determination module 205 further includes:

[0107] A third acquisition module is used to acquire a predetermined discount coefficient and a decision benefit function; wherein the decision benefit function ensures that benefits can be obtained only when the decision is to use the option for the first time;

[0108] A first profit determination module, used to determine the maximum profit of the last stage among the multiple stages;

[0109] The second profit determination module is used to determine the overall maximum profit of the multiple stages based on the initial indicators, the target distribution function of each stage, the target probability measure of each stage and the maximum profit of the last stage, using the discount coefficient and the decision profit function.

[0110] Optionally, the first benefit determination module is specifically used to calculate the maximum benefit of the last stage among the multiple stages based on the indicators, decisions and decision benefit function of the subject of the last stage.

[0111] Optionally, the target distribution function determination module 203 is specifically used for: the subject's indicator includes an actual indicator and a virtual indicator, when the subject's indicator belongs to the virtual indicator, it means that the option has been used, and when the subject's indicator belongs to the actual indicator, it means that the option has not been used;

[0112] If the subject's current indicator belongs to the actual indicator, the target distribution function is the initial distribution function;

[0113] If the subject's current indicator belongs to the virtual indicator, the target distribution function is an indicator function that is 1 on the interval [1, +∞) and 0 elsewhere.

[0114] Optionally, the target probability measure determination module 204 is specifically used to: if the current indicator of the subject belongs to the actual indicator, when the decision is not to use the option, the target probability measure is the initial probability measure; when the decision is to use the option, the target probability measure is 1;

[0115] If the subject's current indicator belongs to the virtual indicator, the target probability measure is 1.

[0116] Optionally, the option pricing device further comprises a discount factor determination module for predetermining the discount factor based on the time value of the indicator of the subject.

[0117] In addition, an embodiment of the present application also provides an option pricing device, which includes a processor and a memory.

[0118] The memory is used to store computer program code and transmit the program code to the processor;

[0119] The processor is used to execute the steps of the option pricing method introduced in any of the above method embodiments according to the instructions in the program code.

[0120] In addition, an embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the program is executed by a processor, the option pricing method described in any of the method embodiments is implemented.

[0121] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and equipment embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The device and equipment embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0122] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An option pricing method, characterized in that: The method comprises: Obtaining the initial index of the subject corresponding to the option to be priced, wherein the initial index is the index of the subject when pricing the option, and the subject needs to complete multiple stages, and the index of the subject changes at the end of each stage; Obtaining an initial distribution function of the time required for the subject to complete each stage and an initial probability measure of the subject's indicator after completion of each stage; Based on the initial distribution function, a target distribution function of the time required to complete each stage is obtained according to the current indicators of the subject in each stage; Based on the initial probability measure, a target probability measure of the subject's indicator after completing each stage is obtained according to the subject's current indicator and decision at each stage, wherein the decision is to use the option or not to use the option; Determining the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage; The option to be priced is priced at a price not less than the overall maximum benefit.

2. The method according to claim 1, characterized in that The target distribution function of the time required to complete each stage is obtained based on the initial distribution function and according to the current indicators of the subject in each stage, including: The subject's index includes an actual index and a virtual index. When the subject's index belongs to the virtual index, it means that the option has been used. When the subject's index belongs to the actual index, it means that the option has not been used. If the subject's current indicator belongs to the actual indicator, the target distribution function is the initial distribution function; If the subject's current indicator belongs to the virtual indicator, the target distribution function is an indicator function that is 1 on the interval [1, +∞) and 0 elsewhere.

3. The method according to claim 2, characterized in that The method of obtaining the target probability measure of the subject's indicator after completing each stage based on the initial probability measure and according to the subject's current indicator and decision at each stage includes: If the subject's current indicator belongs to the actual indicator, when the decision is not to use the option, the target probability measure is the initial probability measure; when the decision is to use the option, the target probability measure is 1; If the subject's current indicator belongs to the virtual indicator, the target probability measure is 1.

4. The method according to claim 1, characterized in that: The determining the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage includes: Obtaining a predetermined discount factor and a decision benefit function; wherein the decision benefit function ensures that benefits can be obtained only when the decision is to use the option for the first time; determining a maximum benefit for a last stage in the plurality of stages; Based on the initial indicator, the target distribution function of each stage, the target probability measure of each stage and the maximum benefit of the last stage, the overall maximum benefit of the multiple stages is determined using the discount coefficient and the decision benefit function.

5. The method according to claim 4, characterized in that The determining of the maximum benefit of the last stage in the multiple stages comprises: The maximum benefit of the last stage among the multiple stages is calculated based on the indicators, decisions and decision benefit function of the subject in the last stage.

6. The method according to claim 5, characterized in that Before determining the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage, the method further includes: The discount factor is predetermined based on the time value of the subject's indicator.

7. An option pricing device, characterized in that: The device comprises: A first acquisition module is used to acquire the initial index of the subject corresponding to the option to be priced, wherein the initial index is the index of the subject when pricing the option, and the subject needs to complete multiple stages, and the index of the subject changes at the end of each stage; A second acquisition module is used to obtain an initial distribution function of the time required for the subject to complete each stage and an initial probability measure of the subject's indicator after each stage is completed; A target distribution function determination module is used to obtain a target distribution function of the time required to complete each stage based on the initial distribution function and the current indicators of the subject in each stage; A target probability measurement determination module is used to obtain a target probability measurement of the subject's indicators after completing each stage based on the subject's current indicators and decisions at each stage, wherein the decision is to use the option or not to use the option; An overall maximum benefit determination module, used to determine the overall maximum benefit of the multiple stages based on the initial indicator, the target distribution function of each stage and the target probability measure of each stage; An option pricing module is used to price the option to be priced at a price that is not less than the overall maximum benefit.

8. The device according to claim 7, characterized in that The overall maximum benefit determination module includes: A third acquisition module is used to acquire a predetermined discount coefficient and a decision benefit function; wherein the decision benefit function ensures that benefits can be obtained only when the decision is to use the option for the first time; A first profit determination module, used to determine the maximum profit of the last stage in the multiple stages; The second profit determination module is used to determine the overall maximum profit of the multiple stages based on the initial indicators, the target distribution function of each stage, the target probability measure of each stage and the maximum profit of the last stage, using the discount coefficient and the decision profit function.

9. An option pricing device, characterized in that: The device comprises: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the option pricing method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the option pricing method described in any one of claims 1 to 6 are implemented.

Images