CN115587683A

CN115587683A - Data processing method, device, equipment and computer readable storage medium

Info

Publication number: CN115587683A
Application number: CN202210992311.5A
Authority: CN
Inventors: 何杰斌
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2022-08-18
Filing date: 2022-08-18
Publication date: 2023-01-10

Abstract

The application discloses a data processing method, a device, equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring risk factors of measurement dimensions and positions; determining the liquidity time limit of the risk factor according to the label of the risk factor; carrying out estimation cash flow discount treatment on the position to obtain the current value of the position with different flowing time limits on the same day; subtracting the current value of the current day from the current value in the historical current value sequence in the observation period to obtain a profit-and-loss sequence in the observation period; classifying the risk factors according to the fluidity time limit, and determining a risk factor subset; determining an amplification factor according to the fluidity time limit corresponding to the risk factor subset, and obtaining expected tail loss under the risk factor subset according to the measurement dimension, the profit and loss sequence and the amplification factor; and summarizing the expected tail loss under each risk factor subset to obtain the expected tail loss of the position under each risk factor subset. According to the embodiment of the application, the expected tail loss with high accuracy is obtained.

Description

Data processing method, device, equipment and computer readable storage medium

Technical Field

The present application belongs to the field of information technology, and in particular, to a data processing method, apparatus, device, and computer-readable storage medium.

Background

Information technology refers to a technology that expands the functions of human information under the guidance of the basic principles and methods of information science. Generally, the information technology is a sum of technologies of functions such as acquisition, processing, transmission and utilization of information by taking electronic computers and modern communication as main means.

In the latest revised market minimum capital requirements for risk, the institution implementing the interior model calculates the expected tail loss for all positions involved based on the risk factor eligibility test results.

In the prior art, the accuracy of the expected tail loss obtained by calculation is low.

Disclosure of Invention

The embodiment of the application provides a data processing method, a data processing device, data processing equipment and a computer readable storage medium, and expected tail loss with high accuracy is obtained through calculation.

In one aspect, an embodiment of the present application provides a data processing method, where the method includes:

acquiring risk factors of measurement dimensions and positions;

determining the liquidity time limit of the risk factor according to the label of the risk factor;

carrying out valuation cash flow discount treatment on the position to obtain the current values of positions with different flowing time limits on the same day;

subtracting the current values of the positions with different fluidity periods from the current values in the historical current value sequence in the observation period to obtain a profit-and-loss sequence in the observation period;

classifying the risk factors according to the liquidity time limit, and determining a risk factor subset to which each risk factor belongs;

performing the following operations separately for each of the subsets of risk factors: determining an amplification factor corresponding to the risk factor subset according to the fluidity time limit corresponding to the risk factor subset, and obtaining an expected tail loss under the risk factor subset according to the measurement dimension, the profit-and-loss sequence and the amplification factor;

and summarizing the expected tail loss under each risk factor subset to obtain the expected tail loss of the position under each risk factor subset.

In one possible implementation, before the performing the valuation cash flow discount process on the position, the method further comprises:

acquiring the maximum fluidity time limit of the risk factor class to which the risk factor belongs and the remaining time limit of the position;

comparing the remaining duration and the maximum fluidity duration of the position;

when the remaining period of the position is less than the maximum fluidity period and the target difference is less than the fluidity period, updating the target difference to the fluidity period;

the target difference is the difference between the maximum fluidity term of the risk factor for the position and the remaining term of the position.

In one possible implementation, the label includes at least a risk factor versus time curve; before the determining the liquidity term according to the label of the risk factor, the method further comprises:

processing the daily market data in the observation period to obtain a market data sequence;

and carrying out relative estimation or absolute estimation processing on the market data of the estimation day according to each element in the market data sequence to obtain a relation curve of the risk factors in the observation period changing along with time.

In a possible implementation manner, the obtaining, according to the measure dimension, the profit-and-loss sequence, and the amplification factor, an expected tail loss under the risk factor subset specifically includes:

obtaining a preset number of loss and benefit data in the loss and benefit sequence under the measurement dimension, and calculating according to the preset number of loss and benefit data to obtain the expected tail loss of the position;

and calculating according to the expected tail loss of the position and the amplification factor to obtain the expected tail loss under the risk factor subset.

In one possible implementation, the subset of risk factors has a sequence and liquidity deadline range; determining the amplification factor corresponding to the risk factor subset according to the liquidity deadline corresponding to the risk factor subset, specifically including:

determining a previous risk factor subset according to the risk factor subset and the sequence;

and calculating to obtain an amplification factor according to the maximum fluidity time limit corresponding to the risk factor subset and the maximum fluidity time limit corresponding to the previous risk factor subset.

In one possible implementation, the following operations are performed separately for each of the risk factor subsets: determining an amplification factor corresponding to the risk factor subset according to the liquidity deadline corresponding to the risk factor subset, and before obtaining an expected tail loss under the risk factor subset according to the measurement dimension, the profit-and-loss sequence and the amplification factor, the method further includes:

when the number of the risk factors in the risk factor subset is larger than 1, carrying out valuation cash flow discount processing on the positions corresponding to the risk factors in the risk factor subset again to obtain the current value on the correction day;

updating the current value of the current day according to the current value of the current day of correction;

subtracting the current values of the positions with different fluidity periods on the current correction day from the current values in the historical current value sequence in the observation period to obtain a corrected damage sequence in the observation period;

and updating the current value of the day according to the loss and gain sequence of the correction day.

In one possible implementation, before the obtaining risk factors measuring dimensions and positions, the method further includes:

displaying a parameter configuration interface;

and receiving parameter configuration input of a user on the interface, and determining the risk factors of the measurement dimension and the position.

In another aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes:

the acquisition module is used for acquiring risk factors of measurement dimensions and positions;

the liquidity duration determining module is used for determining the liquidity duration of the risk factor according to the label of the risk factor;

the estimation module is used for carrying out valuation cash flow discount processing on the positions to obtain the current values of the positions with different flowing time periods;

an expected tail loss output module, configured to classify the risk factors according to the liquidity duration, and determine a risk factor subset to which each risk factor belongs;

performing the following operations separately for each of the subsets of risk factors: determining an amplification factor corresponding to the risk factor subset according to the fluidity deadline corresponding to the risk factor subset, and obtaining an expected tail loss under the risk factor subset according to the measurement dimension, the profit-and-loss sequence and the amplification factor;

In another aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a data processing method as described in an aspect.

In another aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores computer program instructions, and the computer program instructions, when executed by a processor, implement a data processing method according to an aspect.

In yet another aspect, an embodiment of the present application provides a computer program product, where instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform a data processing method according to an aspect.

According to the data processing method, the data processing device, the data processing equipment and the computer readable storage medium, the mobility time limit is divided according to the risk factors, on the basis, the expected tail loss of positions with different mobility time limits is measured, and the high-accuracy expected tail loss is obtained through calculation under different mobility time limits and risk factor subsets. In addition, the number of the positions participating in metering can be reduced according to the flowing time limit of the risk factors of the positions, and the metering efficiency is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings may be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a data processing method provided in an embodiment of the present application;

fig. 2a is a schematic diagram illustrating a first judgment of a fluidity deadline provided in an embodiment of the present application;

FIG. 2b is a diagram illustrating a second determination of the fluidity deadline provided by the embodiment of the present application;

FIG. 2c is a third schematic diagram illustrating a flow deadline provided by an embodiment of the present application;

FIG. 2d is a fourth schematic diagram illustrating the determination of the fluid duration according to the embodiment of the present application;

fig. 2e is a fifth schematic diagram illustrating the term of fluidity provided in the embodiment of the present application;

FIG. 3 is a schematic structural diagram of a subset of risk factors provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a data processing device according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of, and not restrictive on, the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.

To facilitate understanding of the present application, the following terms are explained.

Risk factors, factors that affect the expected tail loss. The risk factors in this application include: interest rate, exchange rate risk, commodity quote and volatility.

The risk factor can be modeled and refers to the risk factor passing the qualification test of the risk factor.

The position refers to the amount of currency on the market, namely the amount of cash.

Market data, historical operational data, transactional data, and the like.

An Expected tail loss value (ES), which is an amount of loss that can be Expected, is an indicator of the risk of reaction. The generally expected tail loss value refers to the expected value that the portfolio will experience if the loss exceeds VaR, as shown in equation 1 below:

ES = E { P & L > VaR } (formula 1)

The Profit and Loss account (P & L) is an account used to compare the business income and other income in a certain period with the business expenses and other expenses in the same period to determine the Profit and Loss of the enterprise.

The Value at Risk model (VaR) refers to the maximum possible loss of a financial asset or portfolio under normal market fluctuations.

Present Value (PV) refers to the Value of the estimated day of future cash flow after the time Value is subtracted. The calculation of the present value depends on the selection of the discount rate, and the future cash flow is usually discounted by the risk-free interest rate.

And the confidence interval is used for estimating the value range of the parameter.

The difference between the holding period, purchase date and redemption date.

The existing method for calculating the expected tail loss cannot divide the flow, and the accuracy is low. In order to solve the problems, the inventor divides the fluidity time limit by the label of the risk factor related to the position, processes the positions with different fluidity time limits and obtains the expected tail loss with high accuracy.

According to the technical scheme, the data acquisition, storage, use, processing and the like meet the relevant regulations of national laws and regulations.

In order to solve the problem of the prior art, embodiments of the present application provide a data processing method, apparatus, device, and computer-readable storage medium. Fig. 1 is a schematic flowchart of a data processing method provided in an embodiment of the present application, and the data processing method provided in the embodiment of the present application is described below with reference to fig. 1.

Step S101, acquiring risk factors for measuring dimensions and positions;

specifically, risk factors of the measurement dimension and the position are obtained according to the received measurement instruction or according to preset interval time. The measurement dimension refers to a range set by a user, such as 12 months of observation period, 97.5% of confidence interval, and 10 days of holding period.

It should be noted that the risk factors referred to in the present application are all modelable risk factors qualified in the risk factor test, and for convenience of description, all risk factors are replaced with the risk factors.

Step S102, determining the liquidity time limit of the risk factor according to the label of the risk factor;

specifically, each risk factor has a plurality of tags, including: regulatory risk categories, curve IDs, currency/currency pairs, issuing subject, issuer rating, commodity label, curve surface type, deadline point/line rights price, and the like. And determining the liquidity time limit corresponding to the risk factor through the combined judgment of the labels of the risk factor.

Fig. 2a is a schematic diagram of first determination of a liquidity period according to an embodiment of the present application, which is used for determining a liquidity period corresponding to a risk factor interest rate. As shown in fig. 2a, when the curve surface type of the risk factor interest rate is the yield rate curve and the currency type is the designated currency, the liquidity period is 10; when the curve surface type of the risk factor interest rate is a yield rate curve and the currency type is unspecified currency, the liquidity period is 20; when the curve surface type of the risk factor interest rate is other, the fluidity deadline is 60.

Fig. 2b is a schematic diagram illustrating a second determination of a liquidity period according to an embodiment of the present application, for determining a liquidity period corresponding to a risk factor foreign exchange. As shown in fig. 2b, when the curve surface type of the risk factor foreign currency is the exchange rate curve, and the currency pair type is the designated currency pair, the liquidity period is 10; when the curve surface type of the risk factor foreign currency is an exchange rate curve and the currency pair type is other currency pairs, the liquidity time limit is 20; when the curve surface type of the risk factor foreign currency is other, the fluidity period is 40.

Fig. 2c is a schematic diagram of third determination of the liquidity period provided in the embodiment of the present application, and is used for determining the liquidity period corresponding to the risk factor product. As shown in fig. 2c, when the curve surface type of the risk factor commodity is a fluctuation rate curve and the commodity target is energy/carbon emission/precious metal/nonferrous metal, the fluidity period is 60; when the curve surface type of the risk factor commodity is a fluctuation rate curve and the commodity is marked as other, the fluidity time limit is 120; when the curve surface type of the risk factor commodity is a price curve and the commodity is marked by energy/carbon emission/precious metal/nonferrous metal, the fluidity period is 20; when the curve surface type of the risk factor commodity is a price curve and the commodity label is other, the liquidity period is 60; when the curve surface type of the risk factor commodity is other, the liquidity term is 120.

Fig. 2d is a fourth schematic diagram for determining a liquidity deadline according to an embodiment of the present application, and is a schematic diagram for determining a liquidity deadline corresponding to a risk factor credit difference. As shown in fig. 2d, when the curve surface type of the risk factor credit interest difference is a price curve, the issuing principal is the principal right, and the issuing principal is the investment level, the liquidity period is 20; when the curve surface type of the risk factor credit interest difference is a price curve, the issuing subject is the principal right, and the issuing subject is rated as a high-yield grade, the liquidity period is 40; when the curve surface type of the risk factor credit profit difference is a price curve, the issuing subject is a company, and the issuing subject is rated as an investment grade, the liquidity period is 40; when the curve surface type of the risk factor credit profit difference is a price curve, the issuing subject is a company, and the issuing subject is rated as a high-earning-rate grade, the liquidity period is 60; when the risk factor credit profit difference curve surface type is other, the liquidity period is 120.

Fig. 2e is a fifth schematic diagram of determining liquidity time limit according to the embodiment of the present application, which is used to determine liquidity time limit corresponding to risk factor stocks. As shown in fig. 2e, when the issuing entity of the risk factor stock is a large stock and the curved surface type is a stock price type, the liquidity period is 10; when the main body of the risk factor stock is the large stock and the curve surface type is the fluctuation rate type, the mobility period is 20; when the main body for issuing the risk factor stock is the large stock and the curve surface type is other types, the fluidity time limit is 60; when the main body of the risk factor stock is the small stock and the curve surface type is the stock price type, the liquidity period is 20; when the main issue of the risk factor stock is the small stock and the curve surface type is other types, the liquidity period is 60.

The labels of the partial risk factors are obtained from the outside, and the labels of the partial risk factors are determined by the labels of other risk factors.

In one example, since the bond yield curve is not stripped of risk-free interest and credit interest, the supervised risk categories corresponding to the bond yield curve include a general interest category and a credit interest category. Therefore, when a company's position of debt is given, the curve of its position is the bond yield curve a, and the tag regulatory risk categories include the general interest rate category and the credit interest difference category.

Step S103, carrying out valuation cash flow discount treatment on the head size to obtain the current value of the day of the positions with different fluidity time limits;

specifically, the reference interest rate for the future payoff interval is calculated according to equations 2 and 3:

r _i ＝r _-1 i = j (equation 2)

Wherein r is _i Representing the reset interest rate of i interest intervals, wherein the value of i is from j +1 to m, t _j-1 Reset date for jth pay interval, t _j Paying date, t, for jth paying interval _v To estimate the day, and t _j-1 <t _v <t _j The cash flow of the jth pay zone is from t _j-1 Reference interest rate generation determined at the moment r _-1 Indicating the established reference interest rate, DF (t), of the jth interest zone _v ,t _i-1 )，DF(t _v ,t _i ) Respectively representing t derived from the surface curve _i-1 ，t _i The discount factor of (c).

The calculated reset interest rate per period according to equation 4, the cash flow of the payoff interval can be determined:

wherein, the cash flow CF _i Future current of finger bonds not occurring on evaluation dayGold flow, Δ r represents the initial point difference, r _i Indicates the reset interest rate of i interest intervals, t _i-1 Reset date for ith payment interval, and t _i Is the paying date of the ith paying interval.

Calculating according to a formula 5 to obtain the current value of the position:

wherein V represents the current value, i takes values from j to m, represents the ith cash flow period number generated after the estimation date, m represents the last cash flow period number, CF _i Denotes the ith cash flow, DF _i Indicating the discount factor corresponding to the ith paying date.

Step S104, subtracting the current values of the positions with different fluidity periods from the current values in the historical current value sequence in the observation period to obtain a profit-and-loss sequence in the observation period;

specifically, position estimates can be discounted to correspond to daily present values during the observation period using position data for the day and market data for different liquidities for each day during the observation period, respectively. And subtracting the current value of the current day from the current value of each day in the observation period to obtain the profit-and-loss sequence in the observation period.

Step S105, classifying the risk factors according to the fluidity time limit, and determining a risk factor subset to which each risk factor belongs;

specifically, preset risk factor subsets are obtained, and each preset risk factor subset has a mobility deadline range. Determining the subset of risk factors based on the fluidity deadline for each risk factor.

In an example, fig. 3 is a schematic structural diagram of risk factor subsets provided in this embodiment of the present application, as shown in fig. 3, there are 5 preset risk factor subsets Q (P, j), where P is a position, j is a sequence of risk factor subsets, and as seen from below, the risk factor subset Q (P, 1) includes risk factors with a mobility duration of 10 days and more than 10 days; q (P, 2) comprises a risk factor with a fluidity duration of 20 days and more than 20 days; q (P, 3) comprises a risk factor with a fluidity duration of 40 days and more than 40 days; q (P, 4) comprises a risk factor with a fluidity duration of 60 days and more than 60 days; q (P, 5) comprises a risk factor with a fluidity duration of 120 days.

Performing the following for each subset of risk factors, respectively: step S106, determining an amplification factor corresponding to the risk factor subset according to the fluidity time limit corresponding to the risk factor subset, and obtaining the expected tail loss under the risk factor subset according to the measurement dimension, the profit-and-loss sequence and the amplification factor;

specifically, the subset of risk factors has a range of sequence and fluidity deadlines; determining a previous risk factor subset according to the risk factor subset and the sequence; and calculating to obtain the amplification factor according to the maximum fluidity time limit corresponding to the risk factor subset and the maximum fluidity time limit corresponding to the previous risk factor subset. And acquiring a preset number of loss data in the loss sequence under the measurement dimension, and calculating according to the preset number of loss data to obtain the expected tail loss of the position. And calculating according to the expected tail loss of the position and the amplification factor to obtain the expected tail loss under the risk factor subset.

In one example, the amplification factor is calculated according to equation 6:

wherein LH _j The maximum liquidity period corresponding to the subset of risk factors is the maximum liquidity period corresponding to the previous subset of risk factors.

The expected tail loss for the position is calculated according to equation 7:

wherein, ES _T (P, j) is the expected tail loss for position P at the overall impact of risk factor subset Q (P, j),

to anticipate tail losses.

And S107, summarizing the expected tail loss under each risk factor subset to obtain the expected tail loss of the position under each risk factor subset.

Specifically, the expected tail loss of position at each risk factor subset is calculated according to equation 8:

wherein j is the number of the fluidity term, ES _F,C Expected tail loss for position at each subset of risk factors, ES _T (P, j) expected tail loss under the subset of risk factors, T is the fluidity deadline. ES (ES) _F,C For the expected tail loss at different fluidity periods, C is the confidence interval and F is the holding period.

Therefore, the flow duration is divided according to the risk factors, on the basis, the expected tail loss of positions of different flow durations is measured, and the high-accuracy expected tail loss is calculated under different flow durations and risk factor subsets.

In some embodiments, prior to step S101, the method further comprises: displaying a parameter configuration interface; and receiving parameter configuration input of a user on an interface, and determining risk factors for measuring dimensions and positions.

Therefore, user-defined parameter configuration can be realized, and the final output expected tail loss is more in line with the requirements of users.

In some embodiments, the label includes at least a time-varying relationship of the risk factor. Prior to step S102, the method further comprises: and processing the daily market data in the observation period to obtain a market data sequence. And carrying out relative estimation or absolute estimation processing on the market data of the estimation day according to each element in the market data sequence to obtain a relation curve of the risk factors in the observation period changing along with time.

In one example, if the observation period is 250 days, then market data of 250 working days before the evaluation day is processed to obtain corresponding market data sequence data, and the market data of the evaluation day is processed according to the market data of 250 working days for relative evaluation or absolute evaluation, as follows:

history time is t _i (i =1,2 \8230250; 250) with an estimated day of t ₂₅₀ History time t _i The curve data (i =1,2 \ 823030250) should be:

interest Rate Curve data t _i Interest rate value = t at time instant ₂₅₀ Interest rate value + t at time _i Interest rate value-t at time _i-1 The interest rate value at the moment, so that 250-day interest rate curve data can be obtained;

data of the foreign exchange rate curve: t is t _i Exchange rate value = t at time instant ₂₅₀ Exchange rate value at time t _i Exchange rate value/t of time _i-1 The exchange rate value at the moment can obtain 250-day exchange rate curve data;

commercial product curve data: t is t _i Commodity offer = t at time ₂₅₀ Time of day commodity quote t _i Temporal commodity quote/t _i-1 The commodity quotation at the moment, so that 250-day commodity curve data can be obtained;

forex fluctuation rate curve data: t is t _i Fluctuation ratio of time = t ₂₅₀ Fluctuation rate of time t _i Fluctuation rate of time/t _i-1 The fluctuation rate of the moment can obtain the fluctuation rate curve data of 250 days.

Therefore, a curve of the risk factor changing with time in the observation period is obtained, and the subsequent determination of the liquidity period of the risk factor based on the label of the risk factor is convenient.

In some embodiments, prior to step S103, the method further comprises: acquiring the maximum fluidity duration and the remaining duration of the position of the risk factor class to which the risk factor belongs; comparing the remaining duration and the maximum fluidity duration of the position; when the remaining period of the position is less than the maximum fluidity period and the target difference is less than the fluidity period, updating the target difference to be the fluidity period; the target difference is the difference between the maximum fluidity term of the risk factor for the position and the remaining term for the position.

The adjusted fluidity period is specifically calculated according to the following equation 9,

wherein i is a risk factor, j is the number of fluidity deadline,

the original fluidity period of the risk factor i for position;

adjusted fluidity duration for the risk factor i of position; t is _pos The remaining period of the position.

Therefore, the liquidity duration of the risk factors is adjusted through comparison of the maximum liquidity duration of the risk factor major class and the remaining duration of the positions, the number of the positions participating in metering is reduced according to the adjusted liquidity duration before the expected tail loss is metered, and the metering efficiency is improved.

In some embodiments, prior to step S106, the method further comprises: when the number of the risk factors in the risk factor subset is larger than 1, carrying out valuation cash flow discount processing on the positions corresponding to the risk factors in the risk factor subset again to obtain the current value on the correction day; updating the current value of the day according to the current value of the correction day; subtracting the current values of the positions with different fluidity periods on the current correction day from the current values in the historical current value sequence in the observation period to obtain a corrected damage sequence in the observation period; and updating the current value of the day according to the loss and gain sequence of the correction day.

Since risk is considered instead of position itself by taking the risk factor as the minimum unit, the thought of considering risk by limiting to position should be removed, and for the case that a certain position includes multiple risk factors, the positions are grouped according to the risk factor subset to calculate the expected tail loss from the risk factor dimension.

For example, when the interest rate risk factor and the foreign exchange risk factor are in the same risk factor subset, all risk factors in the risk factor subset need to be changed integrally, and the relevant positions corresponding to the risk factors are subjected to cash flow discount again to obtain a new profit-and-loss sequence.

Therefore, risks are considered by taking the risk factors as the minimum unit instead of the positions, the influence of the risk factors is considered, for the condition that a certain position comprises multiple risk factors, the positions are grouped according to the risk factor subsets from the consideration of the risk factor dimensions, the expected tail loss is calculated, and the accuracy of the measured expected tail loss is improved.

Based on the data method provided by the above embodiment, correspondingly, the application also provides a specific implementation manner of the data processing device. Please see the examples below.

Fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application, and as shown in fig. 4, the data processing apparatus according to the embodiment of the present application includes:

the acquisition module 1 is used for acquiring risk factors of measurement dimensions and positions;

the liquidity duration determining module 2 is used for determining the liquidity duration of the risk factor according to the label of the risk factor;

the estimation module 3 is used for carrying out valuation cash flow discount treatment on the dimensions to obtain the current values of the dimensions with different fluidity time limits on the same day;

the expected tail loss output module 4 is used for classifying the risk factors according to the fluidity time limit and determining a risk factor subset to which each risk factor belongs;

performing the following operations for each subset of risk factors, respectively: determining an amplification factor corresponding to the risk factor subset according to the fluidity time limit corresponding to the risk factor subset, and obtaining expected tail loss under the risk factor subset according to the measurement dimension, the profit-loss sequence and the amplification factor;

The data processing device provided by the embodiment of the application divides the flowing time limit according to the risk factors, measures the expected tail loss of the positions with different flowing time limits on the basis, and calculates the expected tail loss with high accuracy under different flowing time limits and risk factor subsets. In addition, the number of positions participating in metering can be reduced according to the flowing time limit of the risk factors of the positions, and the metering efficiency is improved.

Fig. 5 is a schematic structural diagram of a data processing device provided in an embodiment of the present application, where the data processing device may include a processor 301 and a memory 302 storing computer program instructions.

Specifically, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 302 may include mass storage for data or instructions. By way of example, and not limitation, memory 302 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 302 may include removable or non-removable (or fixed) media, where appropriate. The memory 302 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 302 is a non-volatile solid-state memory.

The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the application.

The processor 301 realizes any one of the data processing methods in the above-described embodiments by reading and executing computer program instructions stored in the memory 302.

In one example, the data processing device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween.

The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in this embodiment.

Bus 310 includes hardware, software, or both to couple the components of the online data traffic billing device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 310 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The data processing device may execute the online data traffic charging method in the embodiment of the present application based on the currently intercepted spam short messages and the short messages reported by the user, thereby implementing the online data traffic charging method and apparatus described in conjunction with fig. 1 and 2.

In addition, in combination with the online data traffic charging method in the foregoing embodiment, the embodiment of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any one of the above-described embodiments of the online data traffic charging method.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranets, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

In one example, the present application provides a computer readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the data processing method as the above example.

In one example, the present application provides a computer program product, in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the data processing method as the above example.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.

Claims

1. A method of data processing, the method comprising:

acquiring risk factors of measurement dimensions and positions;

performing valuation cash flow discount treatment on the positions to obtain the current values of the positions with different flowing time periods on the same day;

classifying the risk factors according to the fluidity time limit, and determining a risk factor subset to which each risk factor belongs;

performing the following operations for each of the subsets of risk factors, respectively: determining an amplification factor corresponding to the risk factor subset according to the fluidity deadline corresponding to the risk factor subset, and obtaining an expected tail loss under the risk factor subset according to the measurement dimension, the profit-and-loss sequence and the amplification factor;

2. The data processing method of claim 1, wherein prior to said valuation cash flow discount processing of said position, said method further comprises:

acquiring the maximum fluidity duration of the risk factor class to which the risk factor belongs and the remaining duration of the position;

3. The data processing method of claim 1, wherein the label comprises at least a time-dependent relationship curve of the risk factor; before the determining the liquidity term according to the label of the risk factor, the method further comprises:

4. The data processing method according to claim 1, wherein the obtaining the expected tail loss under the risk factor subset according to the measure dimension, the profit-and-loss sequence, and the amplification factor specifically comprises:

5. The data processing method of claim 1, wherein the subset of risk factors has a sequence and liquidity deadline range; determining the amplification factor corresponding to the risk factor subset according to the fluidity deadline corresponding to the risk factor subset specifically includes:

6. The data processing method of claim 1, wherein the following operations are performed separately for each of the subsets of risk factors: determining an amplification factor corresponding to the risk factor subset according to the liquidity deadline corresponding to the risk factor subset, and before obtaining an expected tail loss under the risk factor subset according to the measurement dimension, the profit-and-loss sequence and the amplification factor, the method further includes:

when the number of the risk factors in the risk factor subset is larger than 1, carrying out valuation cash flow discount processing on the position corresponding to the risk factors in the risk factor subset again to obtain the current value of the current correction day;

subtracting the current values of the positions with different fluidity time periods on the current correction day from the current values in the historical current value sequence in the observation period respectively to obtain a corrected profit-and-loss sequence in the observation period;

7. The data processing method of claim 1, wherein prior to said obtaining risk factors that measure dimensions and positions, the method further comprises:

displaying a parameter configuration interface;

8. A data processing apparatus, characterized in that the apparatus comprises:

the estimation module is used for carrying out estimation cash flow discount treatment on the position to obtain the current values of the position with different flowing time limits on the same day;

an expected tail loss output module, configured to classify the risk factors according to the liquidity deadline, and determine a risk factor subset to which each of the risk factors belongs;

9. A data processing apparatus, characterized in that the apparatus comprises: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a data processing method as claimed in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement a data processing method according to any one of claims 1-7.

11. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the data processing method of any of claims 1-7.