CN112116401A - Pressure testing method, device, equipment and storage medium - Google Patents
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Abstract
The invention provides a pressure testing method, which is applied to prediction of PPNR and comprises the following steps: configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list; according to the prediction unit list, configuring a corresponding prediction model for each prediction unit according to the calculation mode of the prediction unit; and, determining interest rates for each prediction unit under each stress scenario; and predicting income and expenditure conditions under different stress scenes by the prediction unit based on the prediction model and the interest rate to complete PPNR prediction. The flexible and feasible pressure testing method, device, equipment and storage medium provided by the embodiment of the invention solve the two problems of insufficient flexibility and poor adaptability of the existing pressure testing.
Description
Technical Field
The invention relates to the technical field of network information security, in particular to a pressure testing method, a pressure testing device, pressure testing equipment and a storage medium.
Background
As an important component of an international financial system, a bank requires to establish a group pressure test implementation and control platform according to international highest standards (American Union storage EPS pressure test and CCAR supervision standards) so as to realize a full-flow pressure test. The objective of the capital pressure testing task of the group is to measure the capital abundance ratio index under different pressure scenes to reflect the degree of the loss of the own capital adopted by the bank so as to meet the requirements of supervision.
The overall process of the group basic pressure test is as follows: firstly, designing three pressure scenarios of benchmark, adverse and serious adverse according to the supervision requirement; secondly, measuring various risk parameters including Default Probability (PD), Default Loss rate (LGD), Default risk Exposure (EAD) and the like through the macroscopic index predicted value in the pressure scene; thirdly, pressure conduction is realized on the bank asset liability statement, the profit statement and the cash flow statement so as to finish net profit and loss prediction before preparation; and finally, combining the prediction result of the risk weighted assets to realize the prediction of the capital abundance.
The prediction of the Pre-provisioning Net profit (PPNR) is a difficult point and a bright point of the whole process, and has the characteristics of high data accuracy requirement, complex model establishment, complex prediction items and parameter items and the like, so that a monitoring department puts forward a lot of new severe requirements on the bank pressure test, for example, the united states storage adopts the DFAST rule in 2012, the EPS rule is officially released in 2014 (the chinese bank and the industrial bank meet the third-level standard and need to regularly develop the pressure test and submit a report), the base committee updates the pressure test in 2018 in 10 months, and the bank protection supervision and supervision can put forward the requirements on the pressure test in various supervision and guidance principles. As an important tool for risk prevention of the prospective risk assessment of banks, the rainless silk and muir has increased importance of pressure testing and is already used as the daily risk management work of banks.
However, the existing pressure testing scheme lacks the prediction index change at the bank demand level, and is difficult to meet the demand that some bank indexes need special treatment in a model, and the flexibility is poor.
Disclosure of Invention
The invention provides a pressure detection method, a pressure detection device, pressure detection equipment and a storage medium, aims to provide a flexible and feasible pressure detection method, device, pressure detection equipment and storage medium, and solves the two problems of insufficient flexibility and poor adaptability of the existing pressure detection.
One aspect of the present invention provides a stress testing method applied to prediction of a net profit PPNR before provisioning, including:
configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list;
according to the prediction unit list, configuring a corresponding prediction model for each prediction unit according to the calculation mode of the prediction unit; and the number of the first and second groups,
determining interest rate of each prediction unit under each pressure scene;
and predicting income and expenditure conditions under different stress scenes by the prediction unit based on the prediction model and the interest rate to complete PPNR prediction.
In an embodiment, before configuring the corresponding prediction units for different pressure scenarios, the method further includes:
acquiring source data of the prediction unit, wherein the source data comprises one or more of account and balance data thereof, business data which occurs and does not relate to fund increase and decrease change and financial statement data;
according to the prediction requirement, constructing a mapping relation between the prediction unit and the source data; and
and constructing a mapping relation between the prediction unit and the market risk parameter.
In an embodiment, the constructing a mapping relationship between the prediction unit and the source data according to the prediction requirement includes:
configuring PPNR parameter names, parameter classification, PPNR historical version loading, prediction unit import and prediction unit list information of the existing prediction units;
when a prediction unit is newly added, configuring the name of the prediction unit, the description of the prediction unit, the gate class of the prediction unit, the large class of interest units, the detailed class of interest units, the project of the prediction unit, the calculation mode, the data source and the self-defined classification information of the newly added prediction unit;
and configuring a prediction unit and a subject mapping relation so that the prediction unit processes the source data.
In an embodiment, the constructing a mapping relationship between the prediction unit and the market risk parameter includes:
determining a pricing curve which can be configured for a predicted item to obtain a pricing curve list;
and configuring the reference category, the item name and the corresponding pricing curve name of the pricing curve according to the predicted item.
In an embodiment, configuring corresponding prediction units for different pressure scenarios to obtain a prediction unit list includes:
and configuring one or more types of prediction units for different pressure scenes according to the prediction demands and the classification of the prediction units, wherein the prediction units comprise asset prediction, liability prediction, commission charge prediction, profit and profit loss prediction and out-of-list credit scale prediction, and obtaining a prediction unit list.
In an embodiment, the determining interest rate of each prediction unit in each stress scenario includes:
and under each stress scene, determining the interest rate of each prediction unit as the sum of the floating interest rate of the current prediction unit and the scene value of the pricing curve corresponding to the prediction unit.
In one embodiment, the predicting the income expenditure situation under different stress scenarios completes the PPNR prediction, including:
and processing the net interest income, non-interest income, interest expenditure, non-interest expenditure and net commission charge income predicted by each prediction unit according to a preset algorithm to obtain the PPNR by prediction.
The embodiment of the invention also provides a pressure testing device, which is applied to prediction of net profit PPNR before preparation, and comprises the following steps: the system comprises a first configuration unit, a second configuration unit, a interest rate determination unit and a prediction unit; wherein,
the first configuration unit is used for configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list;
the second configuration unit is used for configuring a corresponding prediction model for each prediction unit according to the prediction unit list and the calculation mode of the prediction unit; and the number of the first and second groups,
the interest rate determining unit is used for determining the interest rate of each prediction unit under each pressure scene;
and the prediction unit is used for predicting income and expenditure conditions under different pressure scenes based on the prediction model and the interest rate to complete PPNR prediction.
In one embodiment, the apparatus further comprises: an acquisition unit, a first construction unit, a second construction unit, wherein,
the acquiring unit is used for acquiring source data of the prediction unit before the corresponding prediction units are configured for different pressure scenes, wherein the source data comprises one or more of account and balance data thereof, business data which occurs and does not involve fund increase and decrease change, and financial statement data;
the first construction unit is used for constructing a mapping relation between the prediction unit and the source data according to prediction requirements; and
the second construction unit is used for constructing the mapping relation between the prediction unit and the market risk parameter.
In an embodiment, the first constructing unit constructs, according to a prediction requirement, a mapping relationship between the prediction unit and the source data, and is specifically configured to:
configuring PPNR parameter names, parameter classification, PPNR historical version loading, prediction unit import and prediction unit list information of the existing prediction units;
when a prediction unit is newly added, configuring the name of the prediction unit, the description of the prediction unit, the gate class of the prediction unit, the large class of interest units, the detailed class of interest units, the project of the prediction unit, the calculation mode, the data source and the self-defined classification information of the newly added prediction unit;
and configuring a prediction unit and a subject mapping relation so that the prediction unit processes the source data.
In an embodiment, the second constructing unit constructs a mapping relationship between the prediction unit and the market risk parameter, and is specifically configured to:
determining a pricing curve which can be configured for a predicted item to obtain a pricing curve list;
and configuring the reference category, the item name and the corresponding pricing curve name of the pricing curve according to the predicted item.
In an embodiment, the first configuration unit configures corresponding prediction units for different pressure scenarios to obtain a prediction unit list, which is specifically configured to:
and configuring one or more types of prediction units for different pressure scenes according to the prediction demands and the classification of the prediction units, wherein the prediction units comprise asset prediction, liability prediction, commission charge prediction, profit and profit loss prediction and out-of-list credit scale prediction, and obtaining a prediction unit list.
In an embodiment, the interest rate determining unit determines the interest rate of each prediction unit in each stress scenario, specifically for:
and under each stress scene, determining the interest rate of each prediction unit as the sum of the floating interest rate of the current prediction unit and the scene value of the pricing curve corresponding to the prediction unit.
In an embodiment, the prediction unit predicts the income and expense condition under different stress scenarios to complete PPNR prediction, specifically:
and processing the net interest income, non-interest income, interest expenditure, non-interest expenditure and net commission charge income predicted by each prediction unit according to a preset algorithm to obtain the PPNR by prediction.
Another aspect of the present invention also provides a pressure detecting apparatus, including:
a processor;
a memory storing a computer program which, when executed by the processor, implements the pressure detection method as described above.
Yet another aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by at least one computing device, cause the at least one computing device to perform the method as described above.
The beneficial effect of the invention is that,
the embodiment of the invention provides a pressure testing method, a pressure testing device and pressure testing equipment, which are applied to the prediction of PPNR and comprise the following steps: configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list; according to the prediction unit list, configuring a corresponding prediction model for each prediction unit according to the calculation mode of the prediction unit; and, determining interest rates for each prediction unit under each stress scenario; and predicting income and expenditure conditions under different stress scenes by the prediction unit based on the prediction model and the interest rate to complete PPNR prediction. According to the embodiment of the invention, different modeling modes are configured for different prediction units, so that the matching degree of the prediction model and the prediction units is improved, and the prediction accuracy is improved; the source data of the prediction unit is selected to select the data with low cohesiveness and low coupling under different pressure situations as much as possible, so that the effectiveness of the independent indexes of the prediction model can be improved, the modeling accuracy is improved, and the prediction accuracy is ensured; the pressure detection of the bank can be completely covered, each prediction unit can be combined according to user definition, various test requirements are met, the degree of freedom is high, the application is flexible, and the applicability is strong.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a pressure detection method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a pressure detection method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a pressure detecting device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a pressure detection apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a pressure detection method according to an embodiment of the present invention. As shown in fig. 1, the pressure detection method applied to prediction of PPNR mainly includes the following steps:
102, configuring a corresponding prediction model for each prediction unit according to the prediction unit list and the calculation mode of the prediction unit;
here, each prediction unit in the prediction unit list may be classified into a modeling method and an expert judgment method according to a prediction method. According to the embodiment of the invention, the model can be configured with a similar risk parameter prediction model aiming at the prediction model suitable for the configuration of each prediction unit according to task requirements. The expert judgment method modeling is to predict the future value of the prediction unit by using the processed historical value, and specifically comprises the following steps: specifying a time period for a prediction unit in the modeling; indicating relevant macro variables and forms of prediction units in the modeling class; and obtaining a model result by an expert judgment method.
103, determining interest rate of each prediction unit under each pressure scene;
and step 104, predicting income and expenditure conditions under different pressure scenes through the prediction unit on the basis of the prediction model and the interest rate, and completing PPNR prediction.
Optionally, before configuring corresponding prediction units for different pressure scenarios in step 101, the method further includes:
specifically, the tables collected by the source data may include trial balance tables, G4B02 quarterly reports (1104 reports), and collective year report data. Here, the trial balance table (trial balance) refers to a list of various accounts and their balances at a certain point in time, and the balances of the accounts of the group are reflected in the debit or credit column corresponding to the trial balance table. The quarterly report of G4B02 is called "out-of-stock credit risk weighted assets calculation table (weightage law)" for recording the business that has occurred but does not involve or does not involve the change of capital increase and decrease, and the balance of assets before conversion is mainly obtained from the business that is not counted in the balance table of assets according to the accounting criteria but may cause the change of profit and loss, wherein the balance of assets before conversion is 11 items in total, such as "credit transaction business equivalent to loan", "transaction or project related", "short term or project related to trade", and the like. The module mainly obtains information such as interest income, interest expenditure and credit commitment commission in the profit list and is used for estimating the earning rate of the group assets, the payment rate of liabilities, the out-of-list credit commitment fee rate and the like.
firstly, due to the fact that properties of various accounting subjects of the finance are greatly different, accurate results are difficult to obtain by directly predicting the total assets or the total liabilities of the group, and the prediction effect is influenced; and directly taking each subject in the trial balance table for prediction relates to a large amount of model modeling work, and influences the prediction efficiency. Therefore, in order to more reasonably, accurately and accurately predict the scale of the assets and liabilities of the group under each pressure situation, a reasonable and feasible 'prediction unit set' needs to be constructed;
after the definition of the prediction units is completed, the data processing of the prediction units needs to be realized, and in the step, the prediction units need to be mapped with respective source data, and the data to be processed by each prediction unit is determined; the mapping can be specifically completed through the prediction unit and the subjects, and the subjects refer to the subjects to which the data belong. The main mapping content comprises a subject list, a prediction unit subject mapping list and prediction unit subject mapping details.
And step 203, constructing a mapping relation between the prediction unit and the market risk parameter.
Specifically, the duration risk parameter is obtained through a pricing curve, that is, a corresponding pricing curve needs to be configured for each prediction unit, so that mapping between the duration risk parameter and the pricing curve is completed.
It should be understood that step 202 and step 203 are not performed in strict order of succession.
configuring PPNR parameter names, parameter classification, PPNR historical version loading, prediction unit import and prediction unit list information of the existing prediction units;
when a prediction unit is newly added, configuring the name of the prediction unit, the description of the prediction unit, the gate class of the prediction unit, the large class of interest units, the detailed class of interest units, the project of the prediction unit, the calculation mode, the data source and the self-defined classification information of the newly added prediction unit; here, the prediction unit categories can be roughly classified into five categories, namely, an asset category, a liability category, a commission category, a profit category and an out-of-list category, according to the nature and use of the detail units; the interest units are classified according to the interest and the payment attributes of the prediction unit and can be roughly divided into an interest income unit, an interest expenditure unit and a non-interest unit; the interest unit subclass is a further subdivision of the interest unit major class, and the initialization content is shown in the following table 1; the purpose of the prediction unit item is to improve the matching efficiency of the prediction unit and the pricing curve; the calculation mode refers to a mode of processing the source data by the prediction unit, and comprises a modeling class and an expert judgment method, and a user can select the source data according to a prediction implementation mode; the data source is used for marking the data source of the prediction unit; the custom classification is used for merging presentation and merging prediction of prediction units.
TABLE 1
And configuring a prediction unit and a subject mapping relation so that the prediction unit processes the source data. The content required to be configured in the step mainly comprises a subject list, a prediction unit subject mapping list and prediction unit subject mapping details.
In step 203, the constructing a mapping relationship between the prediction unit and the market risk parameter includes:
determining a pricing curve which can be configured for a predicted item to obtain a pricing curve list; the pricing curve list may include: curve type, curve number and historical trend; the curve categories can be roughly divided into deposit interest rate, loan interest rate and the like according to the properties of the pricing curve financial indexes; the curve number indicates the ID of the financial index; the historical trend can be viewed by clicking a preset button, for example, a quarterly trend chart of the index of three or five years can be displayed in a form of a bullet box, and it should be understood that the above is only an exemplary illustration, and the embodiment of the present invention does not limit this.
And configuring the reference category, the item name and the corresponding pricing curve name of the pricing curve according to the predicted item. Here, the base category is divided into an asset class, a liability class, an out-of-list class, and other classes according to the nature of the predicted item, and the division rule can be determined with reference to table 2 below.
TABLE 2
Step 101 configures corresponding prediction units for different pressure scenarios to obtain a prediction unit list, including:
and configuring one or more types of prediction units for different pressure scenes according to the prediction demands and the classification of the prediction units, wherein the prediction units comprise asset prediction, liability prediction, commission charge prediction, profit and profit loss prediction and out-of-list credit scale prediction, and obtaining a prediction unit list. Each prediction unit gate class comprises a detail unit and a custom prediction subunit, for example, the prediction unit comprises the detail units of a public fixed property loan, a personal housing loan and the like, and the custom prediction subunit of the loan and the payment.
and under each stress scene, determining the interest rate of each prediction unit as the sum of the floating interest rate of the current prediction unit and the scene value of the pricing curve corresponding to the prediction unit.
The predicting income and expenditure conditions under different pressure scenes in the step 104 to complete the PPNR prediction comprises the following steps:
and processing the net interest income, non-interest income, interest expenditure, non-interest expenditure and net commission charge income predicted by each prediction unit according to a preset algorithm to obtain the PPNR by prediction.
The embodiment of the invention provides a pressure testing method applied to prediction of PPNR (transient period noise ratio), which comprises the following steps: configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list; according to the prediction unit list, configuring a corresponding prediction model for each prediction unit according to the calculation mode of the prediction unit; and, determining interest rates for each prediction unit under each stress scenario; and predicting income and expenditure conditions under different stress scenes by the prediction unit based on the prediction model and the interest rate to complete PPNR prediction. According to the embodiment of the invention, different modeling modes are configured for different prediction units, so that the matching degree of the prediction model and the prediction units is improved, and the prediction accuracy is improved; the source data of the prediction unit is selected to select the data with low cohesiveness and low coupling under different pressure situations as much as possible, so that the effectiveness of the independent indexes of the prediction model can be improved, the modeling accuracy is improved, and the prediction accuracy is ensured; the pressure detection of the bank can be completely covered, each prediction unit can be combined according to user definition, various test requirements are met, the degree of freedom is high, the application is flexible, and the applicability is strong.
Based on the same inventive concept as the pressure detection method shown in fig. 1, the embodiment of the present application further provides a pressure detection apparatus, as described in the following embodiments. Because the principle of the device for solving the problems is similar to the pressure detection method in fig. 1, the implementation of the device can refer to the implementation of the pressure detection method in fig. 1, and repeated details are not repeated. The structure of the device is shown in fig. 3, and the device is applied to the prediction of the net profit PPNR before preparation, and comprises the following steps: a first configuration unit 31, a second configuration unit 32, an interest rate determination unit 33, and a prediction unit 34; wherein,
the first configuration unit 31 is configured to configure corresponding prediction units for different pressure scenes to obtain a prediction unit list;
the second configuration unit 32 is configured to configure, according to the prediction unit list, a corresponding prediction model for each prediction unit in a calculation manner of the prediction unit; and the number of the first and second groups,
the interest rate determining unit 33 is configured to determine an interest rate of each prediction unit in each pressure scenario;
the prediction unit 34 is configured to predict revenue and expenditure situations under different stress scenarios based on the prediction model and the interest rate, and complete PPNR prediction.
Optionally, the apparatus further comprises: an acquisition unit, a first construction unit, a second construction unit, wherein,
the acquiring unit is used for acquiring source data of the prediction unit before the corresponding prediction units are configured for different pressure scenes, wherein the source data comprises one or more of account and balance data thereof, business data which occurs and does not involve fund increase and decrease change, and financial statement data;
the first construction unit is used for constructing a mapping relation between the prediction unit and the source data according to prediction requirements; and
the second construction unit is used for constructing the mapping relation between the prediction unit and the market risk parameter.
The first construction unit constructs a mapping relationship between the prediction unit and the source data according to a prediction demand, and is specifically configured to:
configuring PPNR parameter names, parameter classification, PPNR historical version loading, prediction unit import and prediction unit list information of the existing prediction units;
when a prediction unit is newly added, configuring the name of the prediction unit, the description of the prediction unit, the gate class of the prediction unit, the large class of interest units, the detailed class of interest units, the project of the prediction unit, the calculation mode, the data source and the self-defined classification information of the newly added prediction unit;
and configuring a prediction unit and a subject mapping relation so that the prediction unit processes the source data.
The second construction unit is used for constructing a mapping relation between the prediction unit and the market risk parameter, and is specifically used for:
determining a pricing curve which can be configured for a predicted item to obtain a pricing curve list;
and configuring the reference category, the item name and the corresponding pricing curve name of the pricing curve according to the predicted item.
The first configuration unit 31 configures corresponding prediction units for different pressure scenes to obtain a prediction unit list, which is specifically configured to:
and configuring one or more types of prediction units for different pressure scenes according to the prediction demands and the classification of the prediction units, wherein the prediction units comprise asset prediction, liability prediction, commission charge prediction, profit and profit loss prediction and out-of-list credit scale prediction, and obtaining a prediction unit list.
Wherein, the interest rate determining unit 33 determines the interest rate of each prediction unit in each pressure scenario, and is specifically configured to:
and under each stress scene, determining the interest rate of each prediction unit as the sum of the floating interest rate of the current prediction unit and the scene value of the pricing curve corresponding to the prediction unit.
The prediction unit 34 predicts income and expenditure situations under different stress scenarios, and completes PPNR prediction, specifically configured to:
and processing the net interest income, non-interest income, interest expenditure, non-interest expenditure and net commission charge income predicted by each prediction unit according to a preset algorithm to obtain the PPNR by prediction.
The invention provides a pressure testing method, a device and equipment, which are applied to the prediction of PPNR and comprise the following steps: configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list; according to the prediction unit list, configuring a corresponding prediction model for each prediction unit according to the calculation mode of the prediction unit; and, determining interest rates for each prediction unit under each stress scenario; and predicting income and expenditure conditions under different stress scenes by the prediction unit based on the prediction model and the interest rate to complete PPNR prediction. According to the embodiment of the invention, different modeling modes are configured for different prediction units, so that the matching degree of the prediction model and the prediction units is improved, and the prediction accuracy is improved; the source data of the prediction unit is selected to select the data with low cohesiveness and low coupling under different pressure situations as much as possible, so that the effectiveness of the independent indexes of the prediction model can be improved, the modeling accuracy is improved, and the prediction accuracy is ensured; the pressure detection of the bank can be completely covered, each prediction unit can be combined according to user definition, various test requirements are met, the degree of freedom is high, the application is flexible, and the applicability is strong.
Fig. 4 is a schematic diagram of a computer device for pressure detection provided in accordance with an embodiment of the present disclosure. As shown in fig. 4, the apparatus may include a processor 401 and a memory 402 storing computer program instructions.
Specifically, the processor 401 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
Memory 402 may include mass storage for data or instructions. By way of example, and not limitation, memory 402 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 402 may include removable or non-removable (or fixed) media, where appropriate. The memory 402 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 402 is a non-volatile solid-state memory. In a particular embodiment, the memory 402 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.
The processor 401 may implement any of the above embodiments for the pressure detection method by reading and executing computer program instructions stored in the memory 402.
In one example, the computer devices described above may also include a communication interface 403 and a bus 404. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 404 to complete communication therebetween.
The communication interface 403 is mainly used for implementing communication between modules, devices, units and/or devices in the embodiment of the present invention.
The bus 404 may comprise hardware, software, or both for coupling the above-described components to one another. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry 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 X10 may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.
The computer device can execute the method for pressure detection in the embodiment of the invention, thereby realizing the method and the device for pressure detection described in conjunction with fig. 1, fig. 2 and fig. 3.
Further, the method for pressure detection described with reference to fig. 1 may be implemented by a program (or instructions) recorded on a computer-readable storage medium. For example, according to an exemplary embodiment of the present invention, a computer-readable storage medium storing instructions may be provided, wherein the instructions, when executed by at least one computing device, cause the at least one computing device to perform a method for pressure detection.
The computer program in the computer-readable storage medium may be executed in an environment deployed in a computer device such as a client, a host, a proxy device, a server, and the like, and it should be noted that the computer program may also be used to perform additional steps other than the above steps or perform more specific processing when the above steps are performed, and the content of the additional steps and the further processing are already mentioned in the description of the related method with reference to fig. 1, and therefore will not be described again here to avoid repetition.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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, embedded processor, 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (16)
1. A stress testing method is applied to prediction of net profit (PPNR) before preparation, and comprises the following steps:
configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list;
according to the prediction unit list, configuring a corresponding prediction model for each prediction unit according to the calculation mode of the prediction unit; and the number of the first and second groups,
determining interest rate of each prediction unit under each pressure scene;
and predicting income and expenditure conditions under different stress scenes by the prediction unit based on the prediction model and the interest rate to complete PPNR prediction.
2. The method of claim 1, further comprising, prior to configuring the corresponding prediction units for the different pressure scenarios:
acquiring source data of the prediction unit, wherein the source data comprises one or more of account and balance data thereof, business data which occurs and does not relate to fund increase and decrease change and financial statement data;
according to the prediction requirement, constructing a mapping relation between the prediction unit and the source data; and
and constructing a mapping relation between the prediction unit and the market risk parameter.
3. The method of claim 2, wherein the constructing the mapping relationship between the prediction unit and the source data according to the prediction requirement comprises:
configuring PPNR parameter names, parameter classification, PPNR historical version loading, prediction unit import and prediction unit list information of the existing prediction units;
when a prediction unit is newly added, configuring the name of the prediction unit, the description of the prediction unit, the gate class of the prediction unit, the large class of interest units, the detailed class of interest units, the project of the prediction unit, the calculation mode, the data source and the self-defined classification information of the newly added prediction unit;
and configuring a prediction unit and a subject mapping relation so that the prediction unit processes the source data.
4. The method of claim 2, wherein the constructing the mapping of the prediction unit to the market risk parameter comprises:
determining a pricing curve which can be configured for a predicted item to obtain a pricing curve list;
and configuring the reference category, the item name and the corresponding pricing curve name of the pricing curve according to the predicted item.
5. The method according to claim 1, wherein configuring the corresponding prediction units for different pressure scenarios to obtain a prediction unit list comprises:
and configuring one or more types of prediction units for different pressure scenes according to the prediction demands and the classification of the prediction units, wherein the prediction units comprise asset prediction, liability prediction, commission charge prediction, profit and profit loss prediction and out-of-list credit scale prediction, and obtaining a prediction unit list.
6. The method of claim 1, wherein determining interest rate for each prediction unit in each stress scenario comprises:
and under each stress scene, determining the interest rate of each prediction unit as the sum of the floating interest rate of the current prediction unit and the scene value of the pricing curve corresponding to the prediction unit.
7. The method of claim 1, wherein predicting revenue expenditure scenarios under different stress scenarios, completing PPNR prediction, comprises:
and processing the net interest income, non-interest income, interest expenditure, non-interest expenditure and net commission charge income predicted by each prediction unit according to a preset algorithm to obtain the PPNR by prediction.
8. A stress testing apparatus, wherein the apparatus is used for predicting a pre-preparation net profit PPNR, comprising: the system comprises a first configuration unit, a second configuration unit, a interest rate determination unit and a prediction unit; wherein,
the first configuration unit is used for configuring corresponding prediction units for different pressure scenes to obtain a prediction unit list;
the second configuration unit is used for configuring a corresponding prediction model for each prediction unit according to the prediction unit list and the calculation mode of the prediction unit; and the number of the first and second groups,
the interest rate determining unit is used for determining the interest rate of each prediction unit under each pressure scene;
and the prediction unit is used for predicting income and expenditure conditions under different pressure scenes based on the prediction model and the interest rate to complete PPNR prediction.
9. The apparatus of claim 8, further comprising: an acquisition unit, a first construction unit, a second construction unit, wherein,
the acquiring unit is used for acquiring source data of the prediction unit before the corresponding prediction units are configured for different pressure scenes, wherein the source data comprises one or more of account and balance data thereof, business data which occurs and does not involve fund increase and decrease change, and financial statement data;
the first construction unit is used for constructing a mapping relation between the prediction unit and the source data according to prediction requirements; and
the second construction unit is used for constructing the mapping relation between the prediction unit and the market risk parameter.
10. The apparatus according to claim 9, wherein the first constructing unit is configured to construct, according to a prediction requirement, a mapping relationship between the prediction unit and the source data, and is specifically configured to:
configuring PPNR parameter names, parameter classification, PPNR historical version loading, prediction unit import and prediction unit list information of the existing prediction units;
when a prediction unit is newly added, configuring the name of the prediction unit, the description of the prediction unit, the gate class of the prediction unit, the large class of interest units, the detailed class of interest units, the project of the prediction unit, the calculation mode, the data source and the self-defined classification information of the newly added prediction unit;
and configuring a prediction unit and a subject mapping relation so that the prediction unit processes the source data.
11. The apparatus according to claim 9, wherein the second construction unit is configured to construct a mapping relationship between the prediction unit and a market risk parameter, and is specifically configured to:
determining a pricing curve which can be configured for a predicted item to obtain a pricing curve list;
and configuring the reference category, the item name and the corresponding pricing curve name of the pricing curve according to the predicted item.
12. The apparatus according to claim 8, wherein the first configuration unit configures corresponding prediction units for different pressure scenarios to obtain a prediction unit list, and is specifically configured to:
and configuring one or more types of prediction units for different pressure scenes according to the prediction demands and the classification of the prediction units, wherein the prediction units comprise asset prediction, liability prediction, commission charge prediction, profit and profit loss prediction and out-of-list credit scale prediction, and obtaining a prediction unit list.
13. The apparatus according to claim 8, wherein the interest rate determining unit determines the interest rate of each prediction unit in each stress scenario, in particular for:
and under each stress scene, determining the interest rate of each prediction unit as the sum of the floating interest rate of the current prediction unit and the scene value of the pricing curve corresponding to the prediction unit.
14. The apparatus according to claim 8, wherein the prediction unit is configured to predict revenue and expenditure situations under different stress scenarios and to perform PPNR prediction, in particular to:
and processing the net interest income, non-interest income, interest expenditure, non-interest expenditure and net commission charge income predicted by each prediction unit according to a preset algorithm to obtain the PPNR by prediction.
15. A pressure testing apparatus, characterized in that the apparatus comprises:
a processor;
memory storing a computer program which, when executed by the processor, implements the method of any one of claims 1-7.
16. A computer-readable storage medium storing instructions that, when executed by at least one computing device, cause the at least one computing device to perform the method of any of claims 1 to 7.
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