CN118014735A - Asset value fluctuation determination method and device, storage medium and electronic equipment - Google Patents

Asset value fluctuation determination method and device, storage medium and electronic equipment Download PDF

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CN118014735A
CN118014735A CN202410174871.9A CN202410174871A CN118014735A CN 118014735 A CN118014735 A CN 118014735A CN 202410174871 A CN202410174871 A CN 202410174871A CN 118014735 A CN118014735 A CN 118014735A
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risk
asset value
loss
sample
migration
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范悦
李超
唐静
李楠
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China Construction Bank Corp
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China Construction Bank Corp
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    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

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Abstract

The embodiment of the application provides a method and a device for determining asset value fluctuation, a storage medium and electronic equipment, wherein the method comprises the following steps: determining a risk change level of the target client, wherein the risk change level is used for representing the degree of change of the asset value risk of the target client; determining migration probability of the risk change level to migrate to other risk change levels, asset value losses corresponding to the risk change levels and other asset value losses corresponding to other risk change levels; calculating unexpected losses to the target object based on the migration probability, asset value losses, and other asset value losses; calculating migration risk influence multipliers according to unexpected losses; an asset value fluctuation parameter corresponding to the target customer is determined based on the migration risk impact multiplier and the transaction asset data of the target customer. The application solves the problem of lower accuracy of calculating the value fluctuation of the asset in the related technology, and achieves the effect of improving the accuracy of calculating the value fluctuation of the asset.

Description

Asset value fluctuation determination method and device, storage medium and electronic equipment
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for determining asset value fluctuation, a storage medium and electronic equipment.
Background
Commercial banks are concerned about the fluctuation of asset value during the process of performing risk management on portfolios. Because the greater the volatility of the asset value loss, the greater the risk the bank is faced with.
As banking business scale is continuously expanded, business diversity and complexity are increased, and when the bank asset portfolio loss distribution is measured, the asset value fluctuation caused by the client risk state change, namely the asset migration risk, needs to be fully considered. At present, the social and economic situations at home and abroad are complex and changeable, enterprises present risks of different degrees, and how to describe the influence of migration risks by a quantitative method and integrate the influence and capital metering into a management flow is a difficult problem to be solved in the current commercial bank risk management.
Most of the existing commercial banks do not consider the impact of asset migration risk in metering asset value fluctuations. Some of the indicators associated with risk migration, while applied in commercial banking risk management, have not been embedded in loss metering of asset fluctuations. For example, focusing on the migration rate of loan-like, having a certain hysteresis, is the case of post-statistics of the risk results that have occurred, and failing to represent the normal risk migration of loan-like.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining asset value fluctuation, a storage medium and electronic equipment, which are used for at least solving the problem of low accuracy of calculating asset value fluctuation in the related technology.
According to one embodiment of the present application, there is provided a method of determining asset value fluctuation, including: determining a risk change level of a target client, wherein the risk change level is used for representing the degree of change of the asset value risk of the target client; determining migration probability of the risk change level to migrate to other risk change levels, asset value loss corresponding to the risk change level, and other asset value loss corresponding to the other risk change levels, wherein the asset value loss is used for representing asset value lost by a target object when the asset value risk of the target client changes; calculating an unexpected loss from the target object based on the migration probability, the asset value loss, and the other asset value losses, wherein the unexpected loss is indicative of an asset value lost by the target client due to an unexpected event; calculating migration risk influence multipliers according to the unexpected losses; and determining an asset value fluctuation parameter corresponding to the target client based on the migration risk influence multiplier and transaction asset data of the target client, wherein the asset value fluctuation parameter is used for representing fluctuation amplitude of asset value of the target object.
In one exemplary embodiment, determining a risk variation level of a target customer includes: acquiring the risk factor data of the target client, wherein the risk factor data comprises at least one of the following: risk preference data, market risk data, personal risk data; the risk variation level is determined based on an evaluation of the risk factor data.
In an exemplary embodiment, determining the migration probability of the risk variation level to migrate to another risk variation level, the asset value loss corresponding to the risk variation level, and the other asset value loss corresponding to the other risk variation level includes: searching migration probabilities of the risk change levels migrating to other risk change levels from a migration probability table, wherein the migration probability table comprises migration probability relations between the risk change levels and the other risk change levels; and searching for the asset value loss corresponding to the risk change level and other asset value losses corresponding to the other risk change levels from an asset value loss table, wherein the asset value loss table comprises the association relation between the risk change level and the corresponding asset value loss and the association relation between the other risk change levels and the corresponding other asset value losses.
In one exemplary embodiment, the migration probability table and the asset value loss table are determined by: obtaining a plurality of sample asset data of a plurality of sample clients, wherein the sample asset data comprises: sample default data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data; determining a first correspondence between a sample risk variation level of each of the sample clients and a sample migration probability, and a second correspondence between a sample risk variation level of each of the sample clients and a sample asset value loss, wherein the sample risk variation level is determined based on the sample risk preference data, the sample market risk data, and the sample personal risk data, the sample migration probability is determined based on the sample breach data and the sample transaction data, and the sample asset value loss is determined based on the sample breach data and the sample transaction data; and constructing the migration probability table according to the first corresponding relation, and constructing the asset value loss table according to the second corresponding relation.
In one exemplary embodiment, calculating the unexpected loss from the target object based on the migration probability, the asset value loss, and the other asset value losses includes: calculating the product between the migration probability and the asset value loss to obtain a first product; determining other migration probabilities corresponding to the other risk change levels; calculating a product between the other asset value losses and the other migration probabilities to obtain a second product, wherein the second product comprises N numbers when the other risk variation levels comprise N numbers, and N is a natural number greater than or equal to 1; calculating a sum of the first product and the N second products to obtain the expected loss, wherein the expected loss is used for representing the asset value loss of the target object caused by the non-unexpected event of the target client; the unexpected loss is determined using the expected loss.
In one exemplary embodiment, determining the unexpected loss using the expected loss includes: calculating a difference between the asset value loss and the expected loss to obtain a first difference; calculating a difference between the other asset value losses and the expected losses to obtain a second difference, wherein the second difference comprises N, and N is a natural number greater than or equal to 1 when the other risk variation level comprises N; calculating the unexpected loss using the first difference, the asset value loss, the N second differences, and the other asset value losses.
In one exemplary embodiment, calculating a migration risk impact multiplier from the unexpected loss includes: calculating the ratio between the unexpected loss and a preset loss, and obtaining the migration risk influence multiplier, wherein the preset loss is the asset value loss of the target object generated when the target client does not undergo risk change grade migration.
According to an embodiment of the present application, there is provided an apparatus for determining asset transition, including: the first determining module is used for determining a risk change level of a target client, wherein the risk change level is used for indicating the degree of change of the asset value risk of the target client; a second determining module, configured to determine a migration probability of the risk change level migrating to another risk change level, an asset value loss corresponding to the risk change level, and another asset value loss corresponding to the other risk change level, where the asset value loss is used to represent an asset value lost by the target object when the asset value risk of the target client changes; a first calculation module configured to calculate an unexpected loss with the target object according to the migration probability, the asset value loss, and the other asset value loss, where the unexpected loss is used to represent an asset value lost by the target client due to an unexpected event; the second calculation module is used for calculating migration risk influence multipliers according to the unexpected losses; and a third determining module, configured to determine an asset value fluctuation parameter corresponding to the target client based on the migration risk impact multiplier and transaction asset data of the target client, where the asset value fluctuation parameter is used to represent a fluctuation range of the asset value of the target object.
In an exemplary embodiment, the first determining module includes: the first obtaining sub-module is configured to obtain risk factor data of the target client, where the risk factor data includes at least one of: risk preference data, market risk data, personal risk data; and the first determination submodule is used for determining the risk change level based on the evaluation of the risk factor data.
In an exemplary embodiment, the second determining module includes: the first searching sub-module is used for searching the migration probability of the risk change level to the other risk change levels from a migration probability table, wherein the migration probability table comprises migration probability relations between the risk change levels and the other risk change levels; and the second searching sub-module is used for searching the asset value loss corresponding to the risk change level and other asset value losses corresponding to the other risk change levels from an asset value loss table, wherein the asset value loss table comprises the association relation between the risk change level and the corresponding asset value loss and the association relation between the other risk change levels and the corresponding other asset value losses.
In one exemplary embodiment, the migration probability table and the asset value loss table are determined by: obtaining a plurality of sample asset data of a plurality of sample clients, wherein the sample asset data comprises: sample default data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data; determining a first correspondence between a sample risk variation level of each of the sample clients and a sample migration probability, and a second correspondence between a sample risk variation level of each of the sample clients and a sample asset value loss, wherein the sample risk variation level is determined based on the sample risk preference data, the sample market risk data, and the sample personal risk data, the sample migration probability is determined based on the sample breach data and the sample transaction data, and the sample asset value loss is determined based on the sample breach data and the sample transaction data; and constructing the migration probability table according to the first corresponding relation, and constructing the asset value loss table according to the second corresponding relation.
In an exemplary embodiment, the first computing module includes: the first calculation sub-module is used for calculating the product between the migration probability and the asset value loss to obtain a first product; determining other migration probabilities corresponding to the other risk change levels; the second calculation sub-module is used for calculating the product between the other asset value loss and the other migration probability to obtain a second product, wherein the second product comprises N numbers when the other risk change levels comprise N numbers, and the N numbers are natural numbers which are larger than or equal to 1; a third calculation sub-module, configured to calculate a sum of the first products and N second products to obtain the expected loss, where the expected loss is used to represent an asset value loss of the target object caused by a non-unexpected event of the target client; and a second determining sub-module for determining the unexpected loss using the expected loss.
In an exemplary embodiment, the second determining sub-module includes: a first calculation unit for calculating a difference between the asset value loss and the expected loss to obtain a first difference; a second calculating unit, configured to calculate a difference between the other asset value losses and the expected loss to obtain a second difference, where the second difference includes N, and N is a natural number greater than or equal to 1, in a case where the other risk variation level includes N; a third calculation unit for calculating the unexpected loss using the first difference, the asset value loss, the N second differences, and the other asset value losses.
In an exemplary embodiment, the second computing module includes: and a fourth calculation sub-module, configured to calculate a ratio between the unexpected loss and a preset loss, to obtain the migration risk influence multiplier, where the preset loss is an asset value loss of the target object generated when the target client does not undergo risk change level migration.
According to a further embodiment of the present application, there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to be executed by a processor for performing the steps of any of the method embodiments described above.
According to a further embodiment of the application there is also provided an electronic device comprising a memory, a processor and a computer program stored on and executable on said memory, said processor being arranged to execute said computer program to perform the steps of any of the method embodiments described above.
According to the method and the system, based on the risk change level of the target client, the migration probability of the risk change level to other risk change levels, the asset value loss corresponding to the risk change level and other asset value losses corresponding to other risk change levels are determined, unexpected losses of the target object are calculated according to the migration probability, the asset value loss and the other asset value losses, a migration risk influence multiplier is calculated according to the unexpected losses, and finally the amplitude of asset value fluctuation of the target object is quantitatively determined according to the migration risk influence multiplier of the target client. Therefore, the problem of lower accuracy of calculating the value fluctuation of the asset in the related technology is solved, and the effect of improving the accuracy of calculating the value fluctuation of the asset is achieved.
Drawings
FIG. 1 is a block diagram of a hardware architecture of a mobile terminal for determining asset value fluctuations according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of determining asset value fluctuations according to an embodiment of the application;
FIG. 3 is a schematic diagram of an address defined alias specification according to an embodiment of the application;
FIG. 4 is a block diagram of an apparatus for determining asset value fluctuations according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in detail below with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the operation on the mobile terminal as an example, fig. 1 is a block diagram of a hardware structure of the mobile terminal according to a method for determining asset value fluctuation according to an embodiment of the present application. As shown in fig. 1, a mobile terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting of the structure of the mobile terminal described above. For example, the mobile terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a method for determining asset value fluctuation in an embodiment of the present application, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, to implement the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as a NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
In this embodiment, a method for determining asset value fluctuation is provided, and fig. 2 is a flowchart of a method for determining asset value fluctuation according to an embodiment of the present application, as shown in fig. 2, the flowchart includes the steps of:
Step S202, determining a risk change level of a target client, wherein the risk change level is used for representing the degree of change of the asset value risk of the target client;
Step S204, determining migration probability of the risk change level to other risk change levels, asset value loss corresponding to the risk change level and other asset value loss corresponding to other risk change levels, wherein the asset value loss is used for representing the asset value lost by the target object when the asset value risk of the target client changes;
Step S206, calculating unexpected loss of the target object according to the migration probability, the asset value loss and other asset value losses, wherein the unexpected loss is used for representing the asset value of the target object lost by the target client due to unexpected events;
Step S208, calculating migration risk influence multipliers according to unexpected losses;
step S210, determining an asset value fluctuation parameter corresponding to the target client based on the migration risk influence multiplier and the transaction asset data of the target client, wherein the asset value fluctuation parameter is used for representing the fluctuation range of the asset value of the target object.
The main body of execution of the steps in this embodiment may be a specific processor provided in a terminal, a server, a terminal or a server, or a processor or a processing device provided separately from the terminal or the server, but is not limited thereto. Such as commercial banks' existing capital management systems.
Optionally, the target clients include individual clients and enterprise clients, etc. The target object is a financial institution, such as a retail bank, an investment bank, or the like.
Optionally, the level of risk variation of the target customer may affect its credit rating, loan amount, portfolio configuration, etc. at the financial institution. The financial institution needs to monitor and evaluate the risk changes of the customers in order to take corresponding risk management measures in time.
Optionally, the influence of the risk change level of the target client on the asset value fluctuation of the target object is reflected to unexpected loss, and the migration risk influence multiplier is finally obtained through metering, so that the method can be closely combined with the internal economic capital management of the commercial bank in practical application, and the refinement degree of the bank capital management is further improved.
Through the steps, based on the risk change level of the target client, the migration probability of the risk change level to other risk change levels, the asset value loss corresponding to the risk change level and other asset value losses corresponding to other risk change levels are determined, unexpected losses of the target object are calculated according to the migration probability, the asset value losses and the other asset value losses, a migration risk influence multiplier is calculated according to the unexpected losses, and finally the amplitude of asset value fluctuation of the target object is quantitatively determined according to the migration risk influence multiplier of the target client. Therefore, the problem of lower accuracy of calculating the value fluctuation of the asset in the related technology is solved, and the effect of improving the accuracy of calculating the value fluctuation of the asset is achieved.
In one exemplary embodiment, determining a risk variation level of a target customer includes: acquiring the risk factor data of the target client, wherein the risk factor data comprises at least one of the following: risk preference data, market risk data, personal risk data; the risk variation level is determined based on an evaluation of the risk factor data.
Optionally, the risk preference data of the target client includes, but is not limited to, investment preference, investment objective, investment period, and the like. For example, a target customer may tend to choose a robust portfolio to warrant a primary goal, rather than being willing to take on excessive market risk. Market risk data for the target customer includes, but is not limited to, market volatility, stock market performance, interest rate variation, and the like. For example, the stock market has a large volatility and frequent interest rate changes, which are part of the market risk data. The personal risk data of the target customer includes, but is not limited to, personal credit status, economic status, investment experience, etc. Determining the risk variation level requires comprehensive consideration of the risk factor data of the target customer and may be assessed using a number of techniques including, but not limited to, risk assessment models, data mining and machine learning, expert systems. Technical means include, but are not limited to: risk assessment model: constructing a risk assessment model by using a mathematical and statistical method, and comprehensively assessing according to the risk preference data, the market risk data and the personal risk data of the target client, wherein the model comprises a risk value model, a Markov model and the like; data mining and machine learning: analyzing a large amount of risk factor data by utilizing data mining and machine learning technologies, finding patterns and rules in the data, thereby determining the risk change level of a target client, for example, clustering the clients by using a cluster analysis technology, and identifying client groups with different risk preferences; expert system: and establishing an expert system, combining experience and knowledge of professionals, evaluating according to risk factor data of the target clients, and performing personalized risk evaluation according to specific conditions of the target clients by the expert system. According to the embodiment, the purpose of accurately determining the risk change level can be achieved by comprehensively considering the risk factor data of the target client.
In an exemplary embodiment, determining the migration probability of the risk variation level to migrate to another risk variation level, the asset value loss corresponding to the risk variation level, and the other asset value loss corresponding to the other risk variation level includes: searching migration probabilities of the risk change levels migrating to other risk change levels from a migration probability table, wherein the migration probability table comprises migration probability relations between the risk change levels and the other risk change levels; and searching for the asset value loss corresponding to the risk change level and other asset value losses corresponding to the other risk change levels from an asset value loss table, wherein the asset value loss table comprises the association relation between the risk change level and the corresponding asset value loss and the association relation between the other risk change levels and the corresponding other asset value losses.
Alternatively, assuming that there are 4 levels (A, B, C, D) of risk change for the target customer, the loss rate is 100% after the target customer violates, the migration probability table is shown in table 1, and the asset value loss table is shown in table 2, where B is the risk change level, a is the up risk level, C is the down risk change level, and D is the default risk change level. The migration probability table includes migration probabilities of the risk change level B migrating to other risk change levels A, B, C, D, and the asset value loss table includes asset value losses of the target object when the risk change level B migrates to other risk change levels A, B, C, D.
Table 1:
Table 2:
according to the method and the device, the influence on the asset value fluctuation of the target client during the migration of the risk change level of the target client can be rapidly quantified by searching the migration probability relation and the asset value loss relation from the migration probability table and the asset value loss table.
In one exemplary embodiment, the migration probability table and the asset value loss table are determined by: obtaining a plurality of sample asset data of a plurality of sample clients, wherein the sample asset data comprises: sample default data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data; determining a first correspondence between a sample risk variation level of each of the sample clients and a sample migration probability, and a second correspondence between a sample risk variation level of each of the sample clients and a sample asset value loss, wherein the sample risk variation level is determined based on the sample risk preference data, the sample market risk data, and the sample personal risk data, the sample migration probability is determined based on the sample breach data and the sample transaction data, and the sample asset value loss is determined based on the sample breach data and the sample transaction data; and constructing the migration probability table according to the first corresponding relation, and constructing the asset value loss table according to the second corresponding relation.
Optionally, the probabilities and loss values in the migration probability table and the asset value loss table consist of the default probability (Probability of Default) PD, the customer rating mobility X, the asset value fluctuation Δ, so the quantitative measurement of the migration probability table and the asset value loss table requires the financial institution to have the ability to meter the default probability PD, the customer rating mobility X, the asset value fluctuation Δ. Specifically, the default probability PD refers to the probability that the target client has default in a certain period. The breach probability PD may be metered based on sample breach data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data, using common PD metering models including logistic regression models, probabilistic regression models, bayesian models, and the like. The client rating mobility X refers to the probability that the target client migrates at risk change level over a period of time. The customer rating mobility xmeter may be estimated using a markov chain model based on sample breach data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data. Asset value loss delta refers to the degree to which the price or value of an asset fluctuates over a period of time. The metering of asset value fluctuations may be estimated using a volatility model based on sample breach data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data. According to the embodiment, the migration probability table and the asset value loss table are determined through the sample default data, the sample asset value loss, the sample risk change level, the sample transaction data, the sample risk preference data, the sample market risk data and the sample personal risk data, so that the asset value fluctuation caused to the target object when the target client risk change level is migrated is accurately determined.
In one exemplary embodiment, calculating the unexpected loss from the target object based on the migration probability, the asset value loss, and the other asset value losses includes: calculating the product between the migration probability and the asset value loss to obtain a first product; determining other migration probabilities corresponding to the other risk change levels; calculating a product between the other asset value losses and the other migration probabilities to obtain a second product, wherein the second product comprises N numbers when the other risk variation levels comprise N numbers, and N is a natural number greater than or equal to 1; calculating a sum of the first product and the N second products to obtain the expected loss, wherein the expected loss is used for representing the asset value loss of the target object caused by the non-unexpected event of the target client; the unexpected loss is determined using the expected loss.
Alternatively, assuming that there are 4 levels (A, B, C, D) of risk change for the target customer, the loss rate is 100% after the target customer violates, the migration probability table is shown in table 1, and the asset value loss table is shown in table 2, where B is the risk change level, a is the up risk level, C is the down risk change level, and D is the default risk change level. The migration probability table includes migration probabilities of the risk change level B migrating to other risk change levels A, B, C, D, and the asset value loss table includes asset value losses of the target object when the risk change level B migrates to other risk change levels A, B, C, D. According to the desired definition, the expected loss is the sum of migration probability in various cases multiplied by the corresponding asset value loss, namely: the first product is: b×0; the second product is: a×e, c×f, d×1; the expected losses are: b× 0+a ×e+c×f+d×1. According to the method and the device, the migration probability relation and the asset value loss relation are searched from the migration probability table and the asset value loss table, so that the asset value loss of the target object when the risk change level is migrated due to the non-unexpected event of the target client can be rapidly quantified.
In one exemplary embodiment, determining the unexpected loss using the expected loss includes: calculating a difference between the asset value loss and the expected loss to obtain a first difference; calculating a difference between the other asset value losses and the expected losses to obtain a second difference, wherein the second difference comprises N, and N is a natural number greater than or equal to 1 when the other risk variation level comprises N; calculating the unexpected loss using the first difference, the asset value loss, the N second differences, and the other asset value losses.
Alternatively, assuming that there are 4 levels (A, B, C, D) of risk change for the target customer, the loss rate is 100% after the target customer violates, the migration probability table is shown in table 1, and the asset value loss table is shown in table 2, where B is the risk change level, a is the up risk level, C is the down risk change level, and D is the default risk change level. The migration probability table includes migration probabilities of the risk change level B migrating to other risk change levels A, B, C, D, and the asset value loss table includes asset value losses of the target object when the risk change level B migrates to other risk change levels A, B, C, D. By definition, the unexpected loss is the corresponding standard deviation, namely: the first difference is: 0- (b× 0+a ×e+c×f+d×1); the second difference is: e- (b× 0+a ×e+c×f+d×1), f- (b× 0+a ×e+c×f+d×1), 1- (b× 0+a ×e+c×f+d×1); unexpected loss ：a×[e-(b×0+a×e+c×f+d×1)]2+b×[0-(b×0+a×e+c×f+d×1)]2+c×[f-(b×0+a×e+c×f+d×1)]2+d×[1-(b×0+a×e+c×f+d×1)]2. in this embodiment, the migration probability relationship and the asset value loss relationship are searched from the migration probability table and the asset value loss table, so that unexpected loss is determined, and the asset value loss of the target object when the risk change level is migrated due to the unexpected event of the target client can be rapidly quantified.
In one exemplary embodiment, calculating a migration risk impact multiplier from the unexpected loss includes: calculating the ratio between the unexpected loss and a preset loss, and obtaining the migration risk influence multiplier, wherein the preset loss is the asset value loss of the target object generated when the target client does not undergo risk change level migration.
Alternatively, assume that there are only two states, default and non-default, regardless of the risk variation level migration. That is, the customer rating mobility X in the unexpected loss is taken to be 0, and the preset loss can be obtained. The embodiment fully considers the asset value loss when the risk change equivalent migration occurs and when the risk change equivalent migration does not occur, and realizes the accurate quantification of the asset value loss of the target object when the target client occurs the risk change grade migration.
The invention is illustrated below with reference to specific examples:
in this embodiment, taking the creation of an asset value fluctuation quantitative measurement model as an example, fig. 3 is a flowchart of the creation of an asset value fluctuation quantitative measurement model according to an embodiment of the present application, which includes the steps of:
S302, respectively assuming two situations of migration and non-migration of the risk change level of the target customer;
S304, under the condition that the target client risk change level does not migrate, only two states of target client default and non-default exist. When the asset combination of the target client is fully dispersed, and the default probability and the default loss rate are respectively PD and 100%, the unexpected loss of the target object is [ (1-PD) PD ] (1/2); in the case of migration of the risk change level of the target client, assuming that the risk change of the target client has 4 levels (A, B, C, D), the loss rate is 100% after the target client breaks the contract, the migration probability table is shown in table 3, and the asset value loss table is shown in table 4, wherein B is the risk change level, a is the upward migration risk level, C is the downward migration risk change level, and D is the default risk change level. The migration probability table includes migration probabilities of the risk change level B migrating to other risk change levels A, B, C, D, and the asset value loss table includes asset value losses of the target object when the risk change level B migrates to other risk change levels A, B, C, D. The probability and loss values in the migration probability table and the asset value loss table consist of the default probability (Probability of Default) PD, the customer rating mobility X and the asset value fluctuation delta, so the quantitative measurement and calculation of the migration probability table and the asset value loss table require the financial institution to have the capability of measuring the default probability PD, the customer rating mobility X and the asset value fluctuation delta;
Table 3:
TABLE 4 asset value loss List
S306, multiplying the probability under various risk levels by the corresponding asset value Loss according to the Expected definition, and adding the probability to obtain an Expected Loss (Expected Loss) EL, wherein EL=X/2 (-delta) + (1-PD-X) 0+X/2 delta+PD;
according to definition, the variance is the sum of the probability under various risk classes multiplied by the corresponding asset value loss variance, and the unexpected loss (Unexpected Loss) UL is the corresponding standard deviation:
In this embodiment, when migration occurs without considering the risk level of the target client, that is, when the value of X in the unexpected loss is 0, the value is consistent with that in S304, that is, the unexpected loss of the target object is: UL 0＝[(1-PD)PD]1/2, readily available migration risk impact multiplier = UL/UL 0;
economic Capital (EC) is a quantitative indicator reflecting unexpected loss of UL, and has a multiple relationship with UL, the impact multiplier of risk level changing Economic Capital is:
And S308, the migration risk influence multiplier can be adaptively split and applied according to different fields (such as areas, industries, products and other key management dimensions), so that a financial institution can conveniently and specifically develop risk management and control measures.
Wherein/> Where Mi is a migration risk impact multiplier for field i, which may be a specific branch, industry, product, business, or custom management dimension. w Full row is a calibration parameter, which can be other related risk management indexes, such as early warning migration rate, and w i is a calibration parameter of other fields, and w i can be obtained by splitting in specific dimensions, namely splitting of client rating migration rate X can be obtained by relying on the splitting property of w i in the specific fields, so that the differences of risk grade change migration in different areas, industries and products can be explored, differentiated risk management by financial institutions is facilitated, and risk grade change migration management and control are performed prospectively;
S310, metering of the migration risk influence multiplier has a clear analytical formula, an application module is easy to develop, and an existing capital management system of a commercial bank is embedded, namely, the migration risk influence multiplier is directly multiplied on the basis of the existing economic capital metering result, so that the refinement degree of bank capital management is improved;
S312, from the specific management practice, the risk level change migration quantitative measurement result mainly influences the economic capital measurement result. Through economic capital management, migration risk influence multipliers are embedded into various business links of financial institutions, such as links of pricing, approval decision, quota management, performance assessment and the like, and the migration risk influence multipliers have obvious effects on scientificity, effectiveness and accuracy improvement of risk management and capital management.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the various embodiments of the present application.
The embodiment also provides a device for determining the asset value fluctuation, which is used for realizing the above embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 4 is a block diagram of an apparatus for determining asset value fluctuations, according to an embodiment of the present application, as shown in FIG. 4, the apparatus comprising:
a first determining module 402, configured to determine a risk variation level of a target client, where the risk variation level is used to represent a degree to which an asset value risk of the target client varies;
a second determining module 404, configured to determine a migration probability of the risk variation level migrating to another risk variation level, an asset value loss corresponding to the risk variation level, and another asset value loss corresponding to the other risk variation level, where the asset value loss is used to represent an asset value lost by the target object when the asset value risk of the target client changes;
A first calculation module 406 configured to calculate an unexpected loss with the target object according to the migration probability, the asset value loss, and the other asset value loss, where the unexpected loss is used to represent the asset value lost by the target client due to an unexpected event;
a second calculation module 408, configured to calculate a migration risk impact multiplier according to the unexpected loss;
A third determining module 410, configured to determine an asset value fluctuation parameter corresponding to the target client based on the migration risk impact multiplier and transaction asset data of the target client, where the asset value fluctuation parameter is used to represent a fluctuation range of the asset value of the target object.
In an exemplary embodiment, the first determining module includes: the first obtaining sub-module is configured to obtain risk factor data of the target client, where the risk factor data includes at least one of: risk preference data, market risk data, personal risk data; and the first determination submodule is used for determining the risk change level based on the evaluation of the risk factor data.
In an exemplary embodiment, the second determining module includes: the first searching sub-module is used for searching the migration probability of the risk change level to the other risk change levels from a migration probability table, wherein the migration probability table comprises migration probability relations between the risk change levels and the other risk change levels; and the second searching sub-module is used for searching the asset value loss corresponding to the risk change level and other asset value losses corresponding to the other risk change levels from an asset value loss table, wherein the asset value loss table comprises the association relation between the risk change level and the corresponding asset value loss and the association relation between the other risk change levels and the corresponding other asset value losses.
In one exemplary embodiment, the migration probability table and the asset value loss table are determined by: obtaining a plurality of sample asset data of a plurality of sample clients, wherein the sample asset data comprises: sample default data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data; determining a first correspondence between a sample risk variation level of each of the sample clients and a sample migration probability, and a second correspondence between a sample risk variation level of each of the sample clients and a sample asset value loss, wherein the sample risk variation level is determined based on the sample risk preference data, the sample market risk data, and the sample personal risk data, the sample migration probability is determined based on the sample breach data and the sample transaction data, and the sample asset value loss is determined based on the sample breach data and the sample transaction data; and constructing the migration probability table according to the first corresponding relation, and constructing the asset value loss table according to the second corresponding relation.
In an exemplary embodiment, the first computing module includes: the first calculation sub-module is used for calculating the product between the migration probability and the asset value loss to obtain a first product; determining other migration probabilities corresponding to the other risk change levels; the second calculation sub-module is used for calculating the product between the other asset value loss and the other migration probability to obtain a second product, wherein the second product comprises N numbers when the other risk change levels comprise N numbers, and the N numbers are natural numbers which are larger than or equal to 1; a third calculation sub-module, configured to calculate a sum of the first products and N second products to obtain the expected loss, where the expected loss is used to represent an asset value loss of the target object caused by a non-unexpected event of the target client; and a second determining sub-module for determining the unexpected loss using the expected loss.
In an exemplary embodiment, the second determining sub-module includes: a first calculation unit for calculating a difference between the asset value loss and the expected loss to obtain a first difference; a second calculating unit, configured to calculate a difference between the other asset value losses and the expected loss to obtain a second difference, where the second difference includes N, and N is a natural number greater than or equal to 1, in a case where the other risk variation level includes N; a third calculation unit for calculating the unexpected loss using the first difference, the asset value loss, the N second differences, and the other asset value losses.
In an exemplary embodiment, the second computing module includes: and a fourth calculation sub-module, configured to calculate a ratio between the unexpected loss and a preset loss, to obtain the migration risk influence multiplier, where the preset loss is an asset value loss of the target object generated when the target client does not undergo risk change level migration.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
In one exemplary embodiment, the computer readable storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the application also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
In an exemplary embodiment, the electronic device may further include a transmission device connected to the processor, and an input/output device connected to the processor.
Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A method of determining asset value fluctuations, comprising:
determining a risk change level of a target client, wherein the risk change level is used for representing the degree of change of the asset value risk of the target client;
Determining migration probability of the risk change level to other risk change levels, asset value loss corresponding to the risk change level and other asset value loss corresponding to the other risk change levels, wherein the asset value loss is used for representing the asset value lost by a target object when the asset value risk of the target client changes;
calculating an unexpected loss from the target object based on the migration probability, the asset value loss, and the other asset value losses, wherein the unexpected loss is indicative of the asset value lost by the target client to the target object due to an unexpected event;
calculating migration risk influence multipliers according to the unexpected losses;
An asset value volatility parameter corresponding to the target customer is determined based on the migration risk impact multiplier and the transaction asset data of the target customer, wherein the asset value volatility parameter is used to represent a volatility amplitude of the asset value of the target object.
2. The method of claim 1, wherein determining a risk variation level for the target customer comprises:
Obtaining risk factor data of the target client, wherein the risk factor data comprises at least one of the following: risk preference data, market risk data, personal risk data;
the risk variation level is determined based on an evaluation of the risk factor data.
3. The method of claim 1, wherein determining a migration probability of the risk variation level to other risk variation levels, an asset value loss corresponding to the risk variation level, and other asset value losses corresponding to the other risk variation levels, comprises:
Searching migration probability of the risk change level to the other risk change levels from a migration probability table, wherein the migration probability table comprises migration probability relations between the risk change levels and the other risk change levels;
Searching asset value losses corresponding to the risk change levels and other asset value losses corresponding to the other risk change levels from an asset value loss table, wherein the asset value loss table comprises association relations between the risk change levels and the corresponding asset value losses and association relations between the other risk change levels and the corresponding other asset value losses.
4. A method according to claim 3, wherein the migration probability table and the asset value loss table are determined by:
Obtaining a plurality of sample asset data of a plurality of sample clients, wherein the sample asset data comprises: sample default data, sample asset value loss, sample risk variation level, sample transaction data, sample risk preference data, sample market risk data, sample personal risk data;
Determining a first correspondence between a sample risk variation level of each of the sample clients and a sample migration probability, and a second correspondence between a sample risk variation level of each of the sample clients and a sample asset value loss, wherein the sample risk variation level is determined based on the sample risk preference data, the sample market risk data, and the sample personal risk data, the sample migration probability is determined based on the sample breach data and the sample transaction data, the sample asset value loss is determined based on the sample breach data and the sample transaction data;
and constructing the migration probability table according to the first corresponding relation, and constructing the asset value loss table according to the second corresponding relation.
5. The method of claim 1, wherein calculating an unexpected loss from the target object based on the migration probability, the asset value loss, and the other asset value losses comprises:
calculating the product between the migration probability and the asset value loss to obtain a first product;
Determining other migration probabilities corresponding to the other risk change levels;
Calculating the product between the other asset value losses and the other migration probabilities to obtain a second product, wherein in the case that the other risk variation levels comprise N, the second product comprises N, and N is a natural number greater than or equal to 1;
Calculating the sum of the first products and N second products to obtain the expected loss, wherein the expected loss is used for representing the asset value loss of the target object caused by non-unexpected events of the target client;
Determining the unexpected loss using the expected loss.
6. The method of claim 5, wherein determining the unexpected loss using the expected loss comprises:
calculating a difference between the asset value loss and the expected loss to obtain a first difference;
Calculating the difference between the other asset value losses and the expected losses to obtain a second difference, wherein in the case that the other risk variation levels comprise N, the second difference comprises N, and N is a natural number greater than or equal to 1;
Calculating the unexpected loss using the first difference, the asset value loss, N of the second differences, and the other asset value losses.
7. The method of claim 4, wherein calculating a migration risk impact multiplier from the unexpected loss comprises:
calculating the ratio between the unexpected loss and a preset loss, and obtaining the migration risk influence multiplier, wherein the preset loss is the asset value loss of the target object generated under the condition that the target client does not undergo risk change grade migration.
8. A method of determining asset value fluctuations, comprising:
the first determining module is used for determining a risk change level of a target client, wherein the risk change level is used for representing the degree of change of the asset value risk of the target client;
The second determining module is used for determining migration probability of the risk change level to other risk change levels, asset value loss corresponding to the risk change level and other asset value loss corresponding to the other risk change levels, wherein the asset value loss is used for representing asset value lost by a target object when the asset value risk of the target client changes;
a first calculation module configured to calculate an unexpected loss with the target object based on the migration probability, the asset value loss, and the other asset value losses, where the unexpected loss is used to represent an asset value lost by the target client due to an unexpected event;
the second calculation module is used for calculating migration risk influence multipliers according to the unexpected losses;
and a third determining module, configured to determine an asset value fluctuation parameter corresponding to the target client based on the migration risk impact multiplier and transaction asset data of the target client, where the asset value fluctuation parameter is used to represent a fluctuation range of the asset value of the target object.
9. A computer readable storage medium, characterized in that a computer program is stored in the computer readable storage medium, wherein the computer program, when being executed by a processor, implements the steps of the method according to any of the claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
CN202410174871.9A 2024-02-07 2024-02-07 Asset value fluctuation determination method and device, storage medium and electronic equipment Pending CN118014735A (en)

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