CN114240208A - Method, device, equipment, medium and product for determining risk value - Google Patents
Method, device, equipment, medium and product for determining risk value Download PDFInfo
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Abstract
The invention provides a method, a device, equipment, a medium and a product for determining an in-risk value, which are applied to the technical field of data processing. The specific implementation scheme comprises the following steps: acquiring a target data change state of target type data in a preset time period; acquiring factor data change states of a plurality of preset factor data in a preset time period; matching each preset factor data with the target type data; determining an at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and a preset at-risk value determination strategy; determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data; and outputting the in-risk value corresponding to the target type data to a user terminal so that the user can check the in-risk value. According to the method for determining the in-risk value, the in-risk value corresponding to the target type data is determined in a mode of combining multi-factor data, and the accuracy of determining the in-risk value is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method, a device, equipment, a medium and a product for determining an in-risk value.
Background
With the advent of the big data age, business processing is performed in many fields such as communication, artificial intelligence, finance and the like through big data technology. In the financial field, data processing technology is more important and dependent because data often needs to be dealt with.
The Risk Value (called Value at Risk in English, or VaR in English) is a data index commonly used in financial systems, and a worker can perform related business processing for a user through the data index, or the user performs related business processing through the data index.
At present, when the index of the at-risk value is determined, the factor data types influencing the at-risk value are more generally through single factor data and a calculation model, and the accuracy of determining the at-risk value in a single factor data mode is lower.
Disclosure of Invention
The invention provides a method, a device, equipment, a medium and a product for determining an in-risk value, which are used for solving the problem of low accuracy of determining the in-risk value in a single-factor data mode at present.
The invention provides a method for determining an at-risk value, which comprises the following steps:
acquiring a target data change state of target type data in a preset time period;
acquiring factor data change states of a plurality of preset factor data in a preset time period; each preset factor data is matched with the target type data;
determining an at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and a preset at-risk value determination strategy;
determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data;
and outputting the in-risk value corresponding to the target type data to a user terminal so that the user can check the in-risk value.
Further, the method for determining the at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state, and a preset at-risk value determination policy includes:
determining a target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state; the target secondary data change state is used for representing target type data change caused by the change of each preset factor data;
and determining the corresponding in-risk value of each preset factor data according to the change state of each target secondary data and a preset in-risk value determination strategy.
Further, the method as described above, the determining a target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state includes:
calculating to obtain corresponding regression coefficients by using the target data change state as a dependent variable and using each factor data change state as an independent variable in a unit linear regression mode;
and determining each regression coefficient as a target secondary data change state corresponding to each factor data change state.
Further, the method as described above, wherein the preset time period is composed of a plurality of secondary time periods;
the determining of the at-risk value corresponding to each preset factor data according to each target secondary data change state and a preset at-risk value determining strategy comprises:
acquiring a preset look-ahead time and a preset confidence coefficient matched with the at-risk value;
dividing each target secondary data change state into a plurality of small data change states according to the secondary time period; the plurality of small data change states are matched with each target secondary data change state;
and determining the in-risk value corresponding to each preset factor data according to the preset look-ahead time, the preset confidence coefficient, the change state of each small data and a preset in-risk value determining algorithm.
Further, the determining, according to the preset look-ahead time, the preset confidence, the change state of each of the small data, and the preset at-risk value determining algorithm, the at-risk value corresponding to each of the preset factor data includes:
arranging the plurality of small data change states in a descending order, and determining the number of the plurality of small data change states;
determining corresponding algorithm intermediate parameters according to the preset confidence coefficient and each quantity;
and inputting the intermediate parameters of each algorithm and the preset look-ahead time into a preset risk value determination algorithm so as to output the corresponding in-risk value of each preset factor data.
Further, in the method described above, the preset risk value determination algorithm is as follows:
wherein, VaRTRepresenting the risk value, R representing the algorithm intermediate parameter, and T representing the preset look-ahead time.
Further, the method for determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data includes:
and fusing the in-risk value corresponding to each preset factor data according to the preset weight matched with each preset factor data to determine the in-risk value corresponding to the target type data.
A second aspect of the present invention provides an at-risk value determination apparatus comprising:
the target data acquisition module is used for acquiring the target data change state of the target type data within a preset time period;
the factor data acquisition module is used for acquiring factor data change states of a plurality of preset factor data in a preset time period; each preset factor data is matched with the target type data;
a first determining module, configured to determine, according to the target data change state, each factor data change state, and a preset at-risk value determining policy, an at-risk value corresponding to each preset factor data;
the second determining module is used for determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data;
and the output module is used for outputting the in-risk value corresponding to the target type data to the user terminal so as to enable the user to check the in-risk value.
Further, in the apparatus described above, the first determining module is specifically configured to:
determining a target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state; the target secondary data change state is used for representing target type data change caused by the change of each preset factor data; and determining the corresponding in-risk value of each preset factor data according to the change state of each target secondary data and a preset in-risk value determination strategy.
Further, in the apparatus as described above, when determining the target secondary data change status corresponding to each of the factor data change statuses according to the target data change status and each of the factor data change statuses, the first determining module is specifically configured to:
calculating to obtain corresponding regression coefficients by using the target data change state as a dependent variable and using each factor data change state as an independent variable in a unit linear regression mode; and determining each regression coefficient as a target secondary data change state corresponding to each factor data change state.
Further, the apparatus as described above, the preset time period is composed of a plurality of sub-time periods;
when the first determining module determines the at-risk value corresponding to each preset factor data according to each target secondary data change state and a preset at-risk value determining strategy, the first determining module is specifically configured to:
acquiring a preset look-ahead time and a preset confidence coefficient matched with the at-risk value; dividing each target secondary data change state into a plurality of small data change states according to the secondary time period; the plurality of small data change states are matched with each target secondary data change state; and determining the in-risk value corresponding to each preset factor data according to the preset look-ahead time, the preset confidence coefficient, the change state of each small data and a preset in-risk value determining algorithm.
Further, in the apparatus as described above, when the first determining module determines the at-risk value corresponding to each of the preset factor data according to the preset look-ahead time, the preset confidence level, the change state of each of the plurality of small data, and a preset at-risk value determining algorithm, the first determining module is specifically configured to:
arranging the plurality of small data change states in a descending order, and determining the number of the plurality of small data change states; determining corresponding algorithm intermediate parameters according to the preset confidence coefficient and each quantity; and inputting the intermediate parameters of each algorithm and the preset look-ahead time into a preset risk value determination algorithm so as to output the corresponding in-risk value of each preset factor data.
Further, in the apparatus as described above, the preset risk value determination algorithm is as follows:
wherein, VaRTRepresenting the risk value, R representing the algorithm intermediate parameter, and T representing the preset look-ahead time.
Further, in the apparatus described above, the second determining module is specifically configured to:
and fusing the in-risk value corresponding to each preset factor data according to the preset weight matched with each preset factor data to determine the in-risk value corresponding to the target type data.
A third aspect of the present invention provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the at-risk value determination method of any of the first aspects.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the at-risk value determination method of any one of the first aspects when executed by a processor.
A fifth aspect of the invention provides a computer program product comprising a computer program which, when executed by a processor, implements the at-risk value determination method of any one of the first aspects.
The invention provides a method, a device, equipment, a medium and a product for determining an insurance value, wherein the method comprises the following steps: acquiring a target data change state of target type data in a preset time period; acquiring factor data change states of a plurality of preset factor data in a preset time period; each preset factor data is matched with the target type data; determining an at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and a preset at-risk value determination strategy; determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data; and outputting the in-risk value corresponding to the target type data to a user terminal so that the user can check the in-risk value. According to the method for determining the in-risk value, the target data change state of the target type data in the preset time period and the factor data change state of the plurality of preset factor data in the preset time period are obtained, the in-risk value corresponding to each preset factor data is determined according to the target data change state, each factor data change state and the preset in-risk value determination strategy, and then the in-risk value corresponding to the target type data is determined according to the in-risk value corresponding to each preset factor data, so that the in-risk value corresponding to the target type data is determined in a mode of combining the plurality of factor data, and the accuracy of determining the in-risk value is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a scene diagram of a method for determining a risk value that can implement an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for determining an at-risk value according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for determining an at-risk value according to a second embodiment of the present invention;
FIG. 4 is a detailed flowchart of step 204 of the method for determining a risk value according to the second embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an apparatus for determining a risk value according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
With the above figures, certain embodiments of the invention have been illustrated and described in more detail below. The drawings and the description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate it by those skilled in the art with reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the technical scheme of the embodiment of the invention, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
For a clear understanding of the technical solutions of the present application, a detailed description of the prior art solutions is first provided. The risk value is a data index commonly used in a financial system, and generally has two elements, namely a confidence coefficient and a look-ahead time, for example, the confidence coefficient is 95 percent, and the look-ahead time is 5 days, which means that the 95 percent possibility change degree of the target data in 5 days does not exceed the risk value. At present, when the index of the at-risk value is determined, the factor data types influencing the at-risk value are more generally through single factor data and a calculation model, and the accuracy of determining the at-risk value in a single factor data mode is lower.
Therefore, the inventor finds that in order to solve the problem that the accuracy of determining the at-risk value is low in a single-factor data mode in the prior art, the at-risk value can be determined by combining multiple factor data, and the accuracy of determining the at-risk value is improved.
Specifically, a target data change state of the target type data in a preset time period and a factor data change state of the plurality of preset factor data in the preset time period are obtained, an at-risk value corresponding to each preset factor data is determined according to the target data change state, the factor data change state and a preset at-risk value determination strategy, and an at-risk value corresponding to the target type data is determined according to the at-risk value corresponding to each preset factor data, so that the at-risk value corresponding to the target type data is determined in a mode of combining the multi-factor data, and accuracy of determining the at-risk value is improved.
The inventor proposes a technical scheme of the application based on the creative discovery.
An application scenario of the method for determining the risk value provided by the embodiment of the invention is described below. As shown in fig. 1, 1 is a first electronic device, 2 is a second electronic device, and 3 is a user terminal. The network architecture of the application scenario corresponding to the risk value determination method provided by the embodiment of the invention comprises the following steps: a first electronic device 1, a second electronic device 2 and a user terminal 3. The second electronic device 2 stores various types of data including target type data, a target data change state, preset factor data, and a factor data change state. The data stored in the second electronic device 2 may be input by a worker or collected from a financial system. The terminal device 3 may be an electronic device such as a computer or a mobile phone.
When the risk value needs to be determined, the first electronic device 1 acquires, from the second electronic device 2, a target data change state of the target type data within a preset time period and a factor data change state of the plurality of preset factor data within the preset time period. And then the first electronic device 1 determines the at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and the preset at-risk value determination strategy. Meanwhile, the first electronic device 1 determines the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data, and outputs the at-risk value to the user terminal 3, so that the user can view the at-risk value through the user terminal 3. After the user views the in-risk value, the user can perform related business processing by taking the in-risk value as a reference. Therefore, the in-risk value corresponding to the target type data is determined in a mode of combining the multi-factor data, and the accuracy of determining the in-risk value is improved.
The embodiments of the present invention will be described with reference to the accompanying drawings.
Fig. 2 is a schematic flow chart of an at-risk value determining method according to a first embodiment of the present invention, and as shown in fig. 2, in this embodiment, an execution subject of the embodiment of the present invention is an at-risk value determining device, and the at-risk value determining device may be integrated in an electronic device. The method for determining the risk value provided by the embodiment comprises the following steps:
step S101, acquiring a target data change state of the target type data in a preset time period.
In this embodiment, the target type data may be a certain category of data in the financial system, such as financial product data. The preset time period can be set according to actual requirements, such as one month, one week, and the like. The target data change state refers to a data change condition of the target type data within a preset time period, for example, the target type data is continuously increased, intermittently increased, fluctuated, and the like within one week.
Step S102, acquiring factor data change states of a plurality of preset factor data in a preset time period. Each preset factor data is matched with the target type data.
In this embodiment, the factor data may be input by a worker in advance, for example, non-quantitative data such as regions and categories may be converted into the factor data by using a matching algorithm, or factors may be searched in advance by using various artificial intelligence and machine learning methods at regular time, and then tracked and analyzed, so as to be converted into the factor data.
The preset factor data can be a region factor, a category factor, a service factor, a momentum factor and the like.
The factor data change state is the change condition of preset factor data within a preset time period, for example, the preset factor data is continuously increased, discontinuously increased, floated up and down, and the like within a week.
The acquired preset factor data are matched with the target type data, and the preset factor data can be stored in a database in advance and establish a matching relation with the target type data.
The matching relationship can be obtained by firstly obtaining factor data change states of all factor data in the factor database, then obtaining a target data change state of target type data, taking the target data change state as a dependent variable Y, taking each factor data change state as an independent variable X, performing multiple linear regression, and calculating a regression coefficient, so that the target type data is more greatly influenced by which combination under different factor data combinations is compared, and the target type data is more suitable for the target type data.
For example, there are 3 factor data A, B, C in the factor database, and different factor data combinations can be formed by different combinations: A. b, C, AB, BC, AC, ABC. Respectively acquiring the target numberAccording to the change state (denoted as Y) and the change state of each factor data (X)A，XB，XC). And performing linear regression on the data change states after different factor data combinations by using Y, respectively calculating coefficients, and then determining the factor combination with the highest matching degree through a preset model to serve as the factor data matched with the target type data.
After the matched factor data is determined, each factor data can be assigned with a weight value, so that a basis is provided for determining the corresponding risk value of the target type data according to the multi-factor data.
And S103, determining the at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and a preset at-risk value determination strategy.
In this embodiment, the preset at-risk value determination policy may be set according to actual requirements, for example, the target data change state and the factor data change state may be divided into a plurality of equal-time secondary data change states, so as to determine the at-risk value according to the secondary data change states.
And step S104, determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data.
In this embodiment, the at-risk values corresponding to the preset factor data may be fused according to the weight of the preset factor data, so as to determine the at-risk value corresponding to the target type data. Fusion may also be performed by other means, such as fusion algorithms set at risk value.
And step S105, outputting the risk value corresponding to the target type data to a user terminal so that the user can check the risk value.
In this embodiment, the in-risk value corresponding to the target type data is output to the user terminal, so that the user can view the in-risk value, and then perform subsequent operations according to the in-risk value. Such as the at-risk value of the financial product, from which the user can decide the flow and operation of subsequent business processes.
The embodiment of the invention provides a method for determining an in-risk value, which comprises the following steps: and acquiring the target data change state of the target type data in a preset time period. And acquiring the factor data change states of a plurality of preset factor data in a preset time period. Each preset factor data is matched with the target type data. And determining an at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and a preset at-risk value determination strategy. And determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data. And outputting the in-risk value corresponding to the target type data to a user terminal so that the user can check the in-risk value.
Meanwhile, an early warning threshold value can be set, and when the risk value exceeds the early warning threshold value, the user is reminded.
According to the method for determining the in-risk value, the target data change state of the target type data in the preset time period and the factor data change state of the plurality of preset factor data in the preset time period are obtained, the in-risk value corresponding to each preset factor data is determined according to the target data change state, the factor data change state and the preset in-risk value determination strategy, and then the in-risk value corresponding to the target type data is determined according to the in-risk value corresponding to each preset factor data, so that the in-risk value corresponding to the target type data is determined in a mode of combining the multi-factor data, and the accuracy of determining the in-risk value is improved.
Fig. 3 is a schematic flow chart of the method for determining an at-risk value according to the second embodiment of the present invention, and as shown in fig. 3, the method for determining an at-risk value according to the present embodiment is further refined on the basis of the method for determining an at-risk value according to the previous embodiment of the present invention. The method for determining the risk value provided by the embodiment comprises the following steps.
In step S201, a target data change state of the target type data in a preset time period is obtained.
In this embodiment, the implementation manner of step 201 is similar to that of step 101 in the previous embodiment of the present invention, and is not described in detail here.
Meanwhile, the target type data can be further subdivided, and the risk value of the data in different layers is determined according to different layers. Before acquiring the target data change state of the target type data within a preset time period, a factor data database, a factor data verification process and the like can be preset.
The factor data verification process is mainly used for verifying the validity of newly acquired or newly configured factor data. All the factor data need to be normalized after being configured, and the normalization mode can be configured (such as median mean, 3 σ method, etc.). The valid/invalid state refers to whether the factor data has use value after the factor data is analyzed. The general idea of the method for checking the validity of the factor data is to detect whether the change state of the factor data and the change state of the target data have correlation. The higher the correlation, the better the effectiveness.
Step S202, acquiring factor data change states of a plurality of preset factor data in a preset time period. Each preset factor data is matched with the target type data.
In this embodiment, the implementation manner of step 202 is similar to that of step 102 in the previous embodiment of the present invention, and is not described herein again.
Step S203, determining the target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state. The target secondary data change state is used for representing target type data change caused when each preset factor data is changed.
In this embodiment, the target secondary data change state mainly represents a change of target type data caused when each preset factor data is changed, for example, when the factor data a is changed, a change of the target type data B can be affected, and the association relationship may be determined by the target data change state and each factor data change state. If the data is a financial product in the target type, the target secondary data change status may refer to a factor profitability.
Specifically, a unit linear regression mode may be adopted, and the target data change state is used as a dependent variable, and each factor data change state is used as an independent variable to calculate and obtain each corresponding regression coefficient.
And determining each regression coefficient as a target secondary data change state corresponding to each factor data change state.
Through a unit linear regression mode, the target secondary data change state corresponding to each factor data change state can be determined more efficiently.
And S204, determining the corresponding in-risk value of each preset factor data according to the change state of each target secondary data and a preset in-risk value determination strategy.
In this embodiment, since the change state of each target secondary data may represent a change of target type data caused when each preset factor data changes, the at-risk value corresponding to each preset factor data may be determined by a preset-at-risk-value determining policy.
It should be noted that the preset time period is composed of a plurality of sub-time periods. Thus, as shown in fig. 4, step 204 may be specifically:
step S2041, acquiring a preset look-ahead time and a preset confidence degree matched with the risk value.
The look-ahead time and the confidence level are important parameters corresponding to the risk value, the look-ahead time is set to be 5 days, for example, the risk value in 5 days in the future is meant, and the confidence level represents the probability, and the confidence level is set to be 95 percent, for example, the probability of 95 percent in 5 days in the future is represented.
Step S2042, dividing each target secondary data change state into a plurality of small data change states according to a secondary time period, and matching the plurality of small data change states with each target secondary data change state.
Since the preset time period is generally long, the preset time period may be divided into a plurality of secondary time periods, for example, if the preset time period is one week, the secondary time period may be set to one day. Each target secondary data change state may be divided into 7 small data change states, each of which is within a certain day.
And step S2043, determining the at-risk value corresponding to each preset factor data according to the preset look-ahead time, the preset confidence coefficient, the change state of each plurality of small data and a preset at-risk value determination algorithm.
In this embodiment, the preset risk value determining algorithm may include two types, the first type is as follows:
wherein, VaRTRepresents the value at risk, σ represents the standard deviation between a plurality of small data change states, T represents the preset look-ahead time, ZαRepresenting the corresponding variable Z value of the confidence a in the standard positive distribution.
The second preset risk value determining algorithm is determined by adopting a historical simulation method, and the specific flow and algorithm are as follows:
and arranging the plurality of small data change states in a descending order, and determining the number of the plurality of small data change states.
And determining corresponding algorithm intermediate parameters according to the preset confidence level and each quantity.
And inputting the intermediate parameters of each algorithm and the preset look-ahead time into a preset risk value determination algorithm so as to output the in-risk value corresponding to each preset factor data.
The adopted preset at-risk value determination algorithm is as follows:
wherein, VaRTRepresenting the risk value, R representing the algorithm intermediate parameter, and T representing the preset look-ahead time.
In this embodiment, determining the corresponding algorithm intermediate parameters according to the preset confidence level and each quantity specifically includes:
and calculating the difference between 1 and the preset confidence coefficient, and determining the product of the difference and each quantity as an algorithm intermediate parameter. Meanwhile, the determined preset factor data is higher in the accuracy of the corresponding at-risk value by adopting a historical simulation method.
Meanwhile, the specific process for determining the value at risk is as follows:
first, specific factor data, such as factor data a, factor data B, and factor data C, is determined.
Then, the target secondary data change state is respectively obtained through the target type data and the factor data change template which are long enough, for example, the sample days are 100 days: a1, a2, …, a 100. B1, B2, …, B100. C1, C2, …, C100.
Calculating the T-day VaR (in-risk value) of the factor data, firstly finding out the standard positive-Tai distribution variable Z value corresponding to the confidence coefficient alpha from the standard normal distribution table, and recording as Zα。
Taking financial products as an example, the target type data is financial product data, and the target secondary data change state can be factor yield.
History simulation method
Mode 1: the daily profitability pattern: sequencing the change states of the target secondary data from small to large, and taking ZαThe change state of the target secondary data at the (rounding) position is the factor of 1 day VaR, and then multiplied by the factor
The T-day VaR is obtained.
Mode 2: the mode of the T day yield: converting the daily rate of return of the factor into the T-day rate of return, and the specific method comprises the following steps:
(1+ r) daily profitability1)*(1+r2)*...*(1+rT)-1；
Wherein r is1-rTRespectively, first day profitability-low day profitability.
For example, in 100 sample days, the first day two-day yield rate (1+ first day daily yield rate) × (1+ second day daily yield rate) -1, the second day two-day yield rate (1+ second day daily yield rate) × (1+ third day daily yield rate) -1, and so on, 99 2-day yield rates can be obtained.
Then sorting the T day factor yield from small to large, and taking ZαThe yield at the (rounded) position is the VaR value for the factor T day.
Second, parameter method
Mode 1: calculating the standard deviation sigma of the daily profitability of the factor, sigma multiplied by ZαObtaining the factor of 1 day VaR, and multiplying by
The T-day VaR is obtained.
Mode 2: converting the daily rate of return of the factor into the daily rate of return of the factor T, and calculating the standard deviation sigma of the daily rate of return of the factor TT，σTMultiplied by ZαThe factor T day VaR is obtained.
Step S205, fusing the at-risk values corresponding to the preset factor data according to the preset weight value matched with the preset factor data to determine the at-risk value corresponding to the target type data.
In this embodiment, because the multi-factor data has different influences on the change of the target type data, the at-risk values corresponding to the preset factor data may be fused according to the preset weight.
And step S206, outputting the risk value corresponding to the target type data to the user terminal so that the user can check the risk value.
In this embodiment, the implementation manner of step 206 is similar to that of step 105 in the previous embodiment of the present invention, and is not described in detail here.
Fig. 5 is a schematic structural diagram of an at-risk value determining apparatus according to a third embodiment of the present invention, and as shown in fig. 5, in this embodiment, the at-risk value determining apparatus 300 includes:
the target data acquiring module 301 is configured to acquire a target data change state of the target type data within a preset time period.
The factor data obtaining module 302 is configured to obtain a factor data change state of a plurality of preset factor data within a preset time period. Each preset factor data is matched with the target type data.
The first determining module 303 is configured to determine, according to the target data change state, the factor data change states, and a preset at-risk value determining policy, at-risk values corresponding to the preset factor data.
The second determining module 304 is configured to determine an at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data.
And the output module 305 is configured to output the at-risk value corresponding to the target type data to the user terminal, so that the user can view the at-risk value.
The apparatus for determining a risk value provided in this embodiment may implement the technical solution of the method embodiment shown in fig. 2, and the implementation principle and technical effect thereof are similar to those of the method embodiment shown in fig. 2, and are not described in detail herein.
The device for determining the risk value provided by the invention is further refined on the basis of the device for determining the risk value provided by the previous embodiment.
Optionally, in this embodiment, the first determining module 303 is specifically configured to:
and determining the target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state. The target secondary data change state is used for representing target type data change caused when each preset factor data is changed. And determining an at-risk value corresponding to each preset factor data according to the change state of each target secondary data and a preset at-risk value determination strategy.
Optionally, in this embodiment, when determining the target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state, the first determining module 303 is specifically configured to:
and calculating by using a unit linear regression mode and using the change state of the target data as a dependent variable and the change state of each factor data as an independent variable to obtain corresponding regression coefficients. And determining each regression coefficient as a target secondary data change state corresponding to each factor data change state.
Optionally, in this embodiment, the preset time period is composed of a plurality of secondary time periods.
When determining the at-risk value corresponding to each preset factor data according to the change state of each target secondary data and the preset at-risk value determination policy, the first determination module 303 is specifically configured to:
and acquiring a preset look-ahead time and a preset confidence coefficient matched with the at-risk value. And dividing each target secondary data change state into a plurality of small data change states according to the secondary time period. The plurality of small data change states are matched with the target secondary data change states. And determining the in-risk value corresponding to each preset factor data according to a preset look-ahead time, a preset confidence coefficient, the change state of each plurality of small data and a preset in-risk value determination algorithm.
Optionally, in this embodiment, when the first determining module 303 determines the at-risk value corresponding to each preset factor data according to a preset look-ahead time, a preset confidence, a change state of each of the plurality of small data, and a preset at-risk value determining algorithm, the first determining module is specifically configured to:
and arranging the plurality of small data change states in a descending order, and determining the number of the plurality of small data change states. And determining corresponding algorithm intermediate parameters according to the preset confidence level and each quantity. And inputting the intermediate parameters of each algorithm and the preset look-ahead time into a preset risk value determination algorithm so as to output the in-risk value corresponding to each preset factor data.
Optionally, in this embodiment, the preset risk value determining algorithm is as follows:
wherein, VaRTRepresenting the risk value, R representing the algorithm intermediate parameter, and T representing the preset look-ahead time.
Optionally, in this embodiment, the second determining module 304 is specifically configured to:
and fusing the in-risk values corresponding to the preset factor data according to the preset weight matched with the preset factor data to determine the in-risk value corresponding to the target type data.
The apparatus for determining a risk value provided in this embodiment may implement the technical solutions of the method embodiments shown in fig. 2 to 4, and the implementation principles and technical effects thereof are similar to those of the method embodiments shown in fig. 2 to 4, and are not described in detail here.
The invention also provides an electronic device, a computer readable storage medium and a computer program product according to the embodiments of the invention.
As shown in fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. Electronic devices are intended for various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, blade servers, mainframes, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: a processor 401, a memory 402. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device.
The memory 402 is a non-transitory computer readable storage medium provided by the present invention. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the at-risk value determination method provided by the present invention. The non-transitory computer-readable storage medium of the present invention stores computer instructions for causing a computer to execute the at-risk value determination method provided by the present invention.
The memory 402, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the method for determining a risk value in an embodiment of the present invention (for example, the target data acquisition module 301, the factor data acquisition module 302, the first determination module 303, the second determination module 304, and the output module 305 shown in fig. 5). The processor 401 executes various functional applications and data processing of the electronic device by executing the non-transitory software programs, instructions and modules stored in the memory 402, namely, implements the risk value determination method in the above method embodiment.
Meanwhile, the embodiment also provides a computer product, and when instructions in the computer product are executed by a processor of the electronic device, the electronic device is enabled to execute the at-risk value determination method of the embodiment.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the embodiments of the invention following, in general, the principles of the embodiments of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the embodiments of the invention pertains.
It is to be understood that the embodiments of the present invention are not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of embodiments of the invention is limited only by the appended claims.
Claims (11)
1. An at-risk value determination method, comprising:
acquiring a target data change state of target type data in a preset time period;
acquiring factor data change states of a plurality of preset factor data in a preset time period; each preset factor data is matched with the target type data;
determining an at-risk value corresponding to each preset factor data according to the target data change state, each factor data change state and a preset at-risk value determination strategy;
determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data;
and outputting the in-risk value corresponding to the target type data to a user terminal so that the user can check the in-risk value.
2. The method of claim 1, wherein determining the at-risk value corresponding to each of the predetermined factor data according to the target data change status, each of the factor data change statuses, and a predetermined at-risk value determination policy comprises:
determining a target secondary data change state corresponding to each factor data change state according to the target data change state and each factor data change state; the target secondary data change state is used for representing target type data change caused by the change of each preset factor data;
and determining the corresponding in-risk value of each preset factor data according to the change state of each target secondary data and a preset in-risk value determination strategy.
3. The method of claim 2, wherein determining a target secondary data change status corresponding to each factor data change status according to the target data change status and each factor data change status comprises:
calculating to obtain corresponding regression coefficients by using the target data change state as a dependent variable and using each factor data change state as an independent variable in a unit linear regression mode;
and determining each regression coefficient as a target secondary data change state corresponding to each factor data change state.
4. The method of claim 2, wherein the preset time period consists of a plurality of secondary time periods;
the determining of the at-risk value corresponding to each preset factor data according to each target secondary data change state and a preset at-risk value determining strategy comprises:
acquiring a preset look-ahead time and a preset confidence coefficient matched with the at-risk value;
dividing each target secondary data change state into a plurality of small data change states according to the secondary time period; the plurality of small data change states are matched with each target secondary data change state;
and determining the in-risk value corresponding to each preset factor data according to the preset look-ahead time, the preset confidence coefficient, the change state of each small data and a preset in-risk value determining algorithm.
5. The method of claim 4, wherein the determining the at-risk value corresponding to each of the predetermined factor data according to the predetermined look-ahead time, the predetermined confidence level, the variation state of each of the plurality of small data, and a predetermined at-risk value determining algorithm comprises:
arranging the plurality of small data change states in a descending order, and determining the number of the plurality of small data change states;
determining corresponding algorithm intermediate parameters according to the preset confidence coefficient and each quantity;
and inputting the intermediate parameters of each algorithm and the preset look-ahead time into a preset risk value determination algorithm so as to output the corresponding in-risk value of each preset factor data.
6. The method of claim 5, wherein the predetermined risk value determination algorithm is as follows:
wherein, VaRTRepresenting the risk value, R representing the algorithm intermediate parameter, and T representing the preset look-ahead time.
7. The method according to claim 6, wherein the determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each of the preset factor data comprises:
and fusing the in-risk value corresponding to each preset factor data according to the preset weight matched with each preset factor data to determine the in-risk value corresponding to the target type data.
8. An at-risk value determination apparatus, comprising:
the target data acquisition module is used for acquiring the target data change state of the target type data within a preset time period;
the factor data acquisition module is used for acquiring factor data change states of a plurality of preset factor data in a preset time period; each preset factor data is matched with the target type data;
a first determining module, configured to determine, according to the target data change state, each factor data change state, and a preset at-risk value determining policy, an at-risk value corresponding to each preset factor data;
the second determining module is used for determining the at-risk value corresponding to the target type data according to the at-risk value corresponding to each preset factor data;
and the output module is used for outputting the in-risk value corresponding to the target type data to the user terminal so as to enable the user to check the in-risk value.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the at-risk value determination method of any of claims 1 to 7.
10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the at-risk value determination method of any one of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the at-risk value determination method according to any one of claims 1 to 7.
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