CN102117469A - System and method for estimating credit risks - Google Patents
System and method for estimating credit risks Download PDFInfo
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- CN102117469A CN102117469A CN2011100210754A CN201110021075A CN102117469A CN 102117469 A CN102117469 A CN 102117469A CN 2011100210754 A CN2011100210754 A CN 2011100210754A CN 201110021075 A CN201110021075 A CN 201110021075A CN 102117469 A CN102117469 A CN 102117469A
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Abstract
The invention discloses a system and a method for estimating credit risks in extreme cases. The system comprises a source data acquisition device, a user input device, a credit risk pressure measurement device, a data storage device and a data output device, wherein the source data acquisition device is connected with the data storage device; the user input device, the credit risk pressure measurement device, the data storage device and the data output device are connected sequentially; and the credit risk pressure measurement device reads source data collected by the source data acquisition device from the data storage device, and conducts simulation and prediction processing on the source data and user input information input by the user input device so as to realize the estimation of the credit risks in the extreme cases. The system and the method overcome the problem that objective, accurate and efficient estimation cannot be realized since the quantification and the estimation of the credit risks in the extreme cases of a bank are mainly dependent on the experience of personnel, and greatly improve the accuracy and safety of the data.
Description
Technical field
The present invention relates to credit risk administrative skill field, particularly relate to a kind of system and method that credit risk is assessed.
Background technology
Credit risk is meant that debtor or counterparty change because of fail to act contractual obligation or credit quality, influence the execution of contract, thereby bring the risk of loss for obligee or financial instrument possessor.From risk expected angle whether, the credit risk loss that bank faces is divided into expected loss, unexpected loss and extreme loss.For expected loss, can digest by bank's price and Reserve Fund; It is unexpected that loss then can only resist absorption by economic capital; And extreme loss is the loss that is taken place under abnormal conditions, and probability of happening is extremely low but lose huge.Bank has carried out the research of a lot of aspects to Credit Risk Evaluation in recent years, has obtained many achievements, but also rests on the starting stage for the loss of assets under the assessment extreme case, can not estimate the loss that takes place under abnormal conditions effectively.At present, the assessment that bank extremely loses assets mainly depends on the experience of evaluator, and mainly by spreadsheet program instruments such as (as Micrsoft Excel and VBA macrocodes).There is following defective in this processing mode:
(1) data acquisition relies on manually fully, and workload is huge, and data-handling efficiency is very low, limit by the spreadsheet program ability, is difficult to carry out the processing of big data quantity;
(2) data are generally managed by the individual, and are destroyed easily, and safety of data does not ensure;
(3) under the extreme case during assessing credit risks subjective judgement occupy very big composition, to the metering neither one of its risk unified standard and model;
(4) data are difficult to realize sharing, and evaluation result and experience are difficult to offer other and use use.
Summary of the invention
(1) technical matters that will solve
In order to overcome present bank under extreme case, credit risk is quantized and the experience of estimating the main people of dependence, can not realize difficulty objective, accurate, efficient evaluations, the invention provides a kind of objective, accurate, system and method for under extreme case, credit risk being assessed that treatment effeciency is high, to improve data-handling efficiency and security.
(2) technical scheme
For achieving the above object, the invention provides a kind of system that under extreme case, credit risk is assessed, this system comprises Source Data Acquisition device 1, user input apparatus 2, credit risk pressure measurement device 3, data storage device 4 and data output device 5, wherein: Source Data Acquisition device 1 is connected in data storage device 4, user input apparatus 2, credit risk pressure measurement device 3, data storage device 4 is connected successively with data output device 5, from data storage device 4, read the source data that Source Data Acquisition device 1 is gathered by credit risk pressure measurement device 3, and the user's input information of this source data and user input apparatus 2 inputs simulated and prediction processing, be implemented under the extreme case assessment to credit risk.
In the such scheme, described Source Data Acquisition device 1 is used for gathering the source data of macroeconomy data plane in the mode of data-interface from banking system or user interface, and stores the source data of gathering into data storage device 4.Described source data comprises client's assets information, Transaction Information and economic target at least.
In the such scheme, described user input apparatus 2 is used to realize mutual between user and the credit risk pressure measurement device 3, the user imports user's input informations by this user input apparatus 2, and obtains the macroscopical all kinds of modelings and the prediction result data of 3 meterings of credit risk pressure measurement device from this user input apparatus 2.Described user's input information comprises explanatory variable, explained variable and analogy model, wherein explanatory variable comprises that GDP and market rate, explained variable comprise Default Probability PD and promise breaking loss percentage LGD, and analogy model comprises linear regression model (LRM), time series models and panel regression model.
In the such scheme, described credit risk pressure measurement device 3 is used for reading the source data that Source Data Acquisition device 1 is gathered from data storage device 4, and the variable information that the needs of this source data and user input apparatus 2 inputs are simulated is simulated and prediction processing.
In the such scheme, described data storage device 4 is used to store the source data of the macroeconomy data plane that Source Data Acquisition device 1 collects, and the macroscopical all kinds of modelings and the prediction result data of 3 meterings of credit risk pressure measurement device.
In the such scheme, described data output device 5 is used for exporting the data of storing with demonstrating data memory storage 4.
For achieving the above object, the present invention also provides a kind of method of under extreme case credit risk being assessed, and is applied to the described system that credit risk is assessed, and this method comprises:
Source Data Acquisition device 1 is gathered source data, and stores the source data that collects into data storage device 4;
User input apparatus 2 is imported credit risk pressure measurement device 3 with user's input information;
Credit risk pressure measurement device 3 reads the source data that Source Data Acquisition device 1 is gathered from data storage device 4, the user's input information of this source data and user input apparatus 2 inputs is simulated and prediction processing;
Credit risk pressure measurement device 3 will be simulated and the result data of prediction processing is stored in the data storage device 4; And
Data output device 5 represents the result data of data storage device 4 to the user.
In the such scheme, described Source Data Acquisition device 1 is gathered in the step of source data, and source data comprises client's assets information, Transaction Information and economic target at least.
In the such scheme, described user input apparatus 2 is imported user's input information in the step of credit risk pressure measurement device 3, user's input information comprises explanatory variable, explained variable and analogy model, wherein explanatory variable comprises that GDP and market rate, explained variable comprise Default Probability PD and promise breaking loss percentage LGD, and analogy model comprises linear regression model (LRM), time series models and panel regression model.
In the such scheme, described credit risk pressure measurement device 3 reads the source data that Source Data Acquisition device 1 is gathered from data storage device 4, user's input information to this source data and user input apparatus 2 inputs is simulated and prediction processing, specifically comprise: credit risk pressure measurement device 3 obtains the match analog variable from data storage device 4, the internal relation of metering variable, and use a model and return simulation process, the input pressure data are simulated and prediction processing according to analog result then.
In the such scheme, the match analog variable that described credit risk pressure measurement device 3 obtains from data storage device 4, comprise explanatory variable and explained variable, explanatory variable comprises that GDP and market rate, explained variable comprise Default Probability PD and promise breaking loss percentage LGD.
In the such scheme, the internal relation of described credit risk pressure measurement device 3 metering variablees, comprising: 3 pairs of these match analog variables of credit risk pressure measurement device carry out initial analysis, in order to screen suitable variable and proper model; This initial analysis adopts the variable to selecting to carry out correlation calculations, the match of variable curve map and the match of standardized variable curve map as the analysis and evaluation means, and result is stored in data storage device 4.
In the such scheme, described correlation calculations is that the variable that will select carries out the calculating of correlativity, in order to judge the internal relation between selecteed variable; Match of variable curve map and the match of standardized variable curve map then are that the distribution situation by the selected variable that draws reflects intuitively whether selected variable meets the requirement that returns simulation.
In the such scheme, described credit risk pressure measurement device 3 uses a model and returns simulation, handles with following three kinds of models respectively:
Model one: linear regression model (LRM);
Y＝β
0+β
1X
1+......+β
kX
k+ε
β wherein
0Be constant, β
1β
2... be coefficient, X
1X
2... be explanatory variable, Y is an explained variable, and ε is a Disturbance, obtain explanatory variable and explained variable by data storage device 4, the use linear regression model (LRM) simulates the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant β that tries to achieve
0And factor beta
1β
2... deposit data storage device 4 in;
Model two: time series models;
Wherein c is a constant, α
ib
jBe coefficient, X
J (t-k)Be explanatory variable, y
tBe explained variable, ε is a Disturbance, obtains explanatory variable and explained variable by data storage device 4, and service time, series model simulated the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant c and the factor alpha of trying to achieve
ib
jDeposit data storage device 4 in;
Model three: panel regression model;
U wherein
ItBe constant, β
kBe coefficient, X
ItkBe explanatory variable, y
ItBe explained variable, ε is a Disturbance, obtains explanatory variable and explained variable by data storage device 4, and use panel regression model simulates the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant u that tries to achieve
ItAnd factor beta
kDeposit data storage device 4 in.
In the such scheme, described credit risk pressure measurement device 3 input pressure data, predict according to analog result, comprise: credit risk pressure measurement device 3 obtains constant and the coefficient that regression model is obtained by reading of data memory storage 4, obtain variable that the user selects as explanatory variable by user input apparatus 2, try to achieve explained variable, reach by model of fit conducting pressure data according to formula, obtain based on prediction result, will predict the outcome deposits data storage device 4 in.
(3) beneficial effect
From technique scheme as can be seen, the present invention has following beneficial effect:
1, this system and method for under extreme case, credit risk being assessed provided by the invention, overcome present bank under extreme case, credit risk is quantized and the experience of estimating the main people of dependence, can not realize difficulty objective, accurate, efficient evaluations, the accuracy and the security that have improved data greatly.
2, this system and method for under extreme case, credit risk being assessed provided by the invention, for bank provides a data processing platform (DPP) of measuring efficiently under extreme case, compare with the semi-hand processing mode of former electronic watch form, improved data processing efficiency greatly, can carry out the metering of macro-performance indicator, credit index etc. timely with the big quantitative analysis data of the high efficiency processing of higher frequency (in real time);
3, this system and method for under extreme case, credit risk being assessed provided by the invention, whenever realize can be according to the long-term tendency of current macroeconomy digital simulation bank portfolio, contingent variation under the real-time quantization extreme case, the risk of control bank portfolio, and improve income.According to the real asset data of client, prediction customer default risk is taken precautions against top-tier customer and is run off, improves the customer risk managerial ability, supports for the market competitiveness that further strengthens commercial bank's products ﹠ services provides stable.
Description of drawings
Fig. 1 is the synoptic diagram of the system that under extreme case credit risk is assessed provided by the invention;
Fig. 2 is the process flow diagram that the invention provides the method that credit risk is assessed;
Fig. 3 is the processing flow chart of credit risk pressure measurement device among the present invention.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.
This system and method that credit risk is assessed provided by the invention, " from top to bottom " the macromodel mode (Top-Down) of employing, automatically gather the source data of each operation system, and these source datas are carried out the metering and the evaluation of credit risk under the extreme case with means such as logistic regression, panel recurrence, time series analyses, generate the evaluation result data, and with the evaluation result data storage that generates in data storage device.Final evaluation result is the credit risk analysis data (as the Default Probability PD after exerting pressure, the economic capital EC after exerting pressure, the non-performing loan rate NPLR after exerting pressure etc.) under the extreme case.The contrast accompanying drawing specifically describes below:
Fig. 1 is the synoptic diagram of the system that under extreme case credit risk is assessed provided by the invention, and this system comprises Source Data Acquisition device 1, user input apparatus 2, credit risk pressure measurement device 3, data storage device 4 and data output device 5.
Source Data Acquisition device 1, be used for gathering from banking system or user interface source data (assets information, the Transaction Information that comprise the client of macroeconomy data plane in the mode of data-interface, economic target etc.), store the source data that collects into data storage device 4 by internal network again.
User input apparatus 2, be used for the mutual of user and credit risk pressure measurement device 3, the user wants the variable information of simulating by the input of this user input apparatus, be user's input information, and obtain the credit risk pressure measurement device 3 the macroscopic view metering of exerting pressure from this user input apparatus and handle the result of real-time operation.
Credit risk pressure measurement device 3 is used for reading the source data that Source Data Acquisition device 1 is gathered from data storage device 4, and the user's input information of this source data and user input apparatus 2 inputs is simulated and prediction processing.
Data storage device 4 is used to store the source data of the macroeconomy data plane that Source Data Acquisition device 1 collects and the macroscopical all kinds of modelings and the prediction result data of 3 meterings of credit risk pressure measurement device.
Data output device 5 is used for the data that output data memory storage 4 is stored.
Fig. 2 is the process flow diagram that the invention provides the method that credit risk is assessed, and this method may further comprise the steps:
Step 101: Source Data Acquisition device 1 image data.
Source Data Acquisition device 1 is gathered the source data of the macroeconomy data plane in banking system or the external data source, and storing the source data that collects into data storage device 4 by internal network, the source data of this macroeconomy data plane specifically comprises customer information, contract, liability transaction data and economic target data.
Step 102: the explanatory variable that user input apparatus 2 is selected the user is (as GDP, market rate etc.) and explained variable (as Default Probability PD, promise breaking loss percentage LGD) and analogy model (as linear regression model (LRM), time series models, the panel regression model etc.) user's input information of etc.ing import credit risk pressure measurement device 3.
Step 103: credit risk pressure measurement device 3 reads the source data that Source Data Acquisition device 1 is gathered from data storage device 4, and the user's input information of this source data and user input apparatus 2 inputs is simulated and prediction processing.
Step 104: credit risk pressure measurement device 3 will be simulated and the result data of prediction processing is stored in the data storage device 4.
Step 105: data output device 5 represents the result data of data storage device 4 to the user, checks and downloads for the user.
Fig. 3 is the processing flow chart of credit risk pressure measurement device among the present invention.Credit risk pressure measurement device 3 reads the source data that Source Data Acquisition device 1 is gathered from data storage device 4, and the user's input information of this source data and user input apparatus 2 inputs simulated and prediction processing, the flow process of this simulation and prediction processing is as follows:
Step 201: obtain analog variable;
Obtain the model of fit variable from data storage device 4, wherein variable is divided into explanatory variable and explained variable, such as following simple formula:
Y＝aX+b
X in the formula is explanatory variable (variable of exerting pressure), this variable can be that the economic target data are (as GDP, market rate etc.), also can be the risk metering factor (as Default Probability PD, non-performing loan rate LGD etc.), the Y in the formula be explained variable (being subjected to pressure variable amount), this variable is generally the risk metering factor (as Default Probability PD, non-performing loan rate LGD etc.), a in the formula is called coefficient, and b is called constant.
Step 202: the internal relation of metering variable;
Return simulation for the variable that obtains is done, credit risk pressure measurement device 3 carries out initial analysis to these variablees, in order to screen suitable variable and proper model.Initial analysis can choice variable correlation calculations, the match of variable curve map and the match of standardized variable curve map etc. carry out the analysis and evaluation means, and there is data storage device 4 in the result.Correlation calculations is that the variable that will select carries out the calculating of correlativity, in order to judge the internal relation between selecteed variable, if the correlativity such as two groups of variablees is too high, promptly along with the variation of time, these two groups of data present with increasing with the sync status that subtracts, credit risk pressure measurement device can remove one group of data wherein because these two groups of data can be similar to think one group of data.Match of variable curve map and the match of standardized variable curve map then are that the distribution situation by the selected variable that draws reflects intuitively whether selected variable meets the requirement that returns simulation.But all true-time operations of analysis means such as the correlation calculations of variable, the match of variable curve map and the match of standardized variable curve map, credit risk pressure measurement device 3 is real-time is kept at analysis result data storage device 4, and the user can or download the result who analyzes by data output device 5 real time inspections.
Step 203: using a model returns simulation;
Credit risk pressure measurement device 3 returns simulation process, handles with following three kinds of models respectively:
Model one: linear regression model (LRM);
Y＝β
0+β
1X
1+......+β
kX
k+ε
β wherein
1Be constant, β
1β
2... be coefficient, X
1X
2... be explanatory variable (as: GDP, market rate etc.), Y is that explained variable is (as Default Probability PD, non-performing loan rate LGD etc.), ε is a Disturbance, obtain explanatory variable and explained variable by data storage device 4, the use linear regression model (LRM) simulates the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant β that tries to achieve
1And factor beta
1β
2... deposit data storage device 4 in.
Model two: time series models;
Wherein c is a constant, α
ib
jBe coefficient, X
J (t-k)Be explanatory variable (as: GDP, market rate etc.), y
tBe that explained variable is (as Default Probability PD, non-performing loan rate LGD etc.), ε is a Disturbance, obtain explanatory variable and explained variable by data storage device 4, service time, series model simulated the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant c and the factor alpha of trying to achieve
ib
jDeposit data storage device 4 in.
Model three: panel regression model;
U wherein
ItBe constant, β
kBe coefficient, X
ItkBe explanatory variable (as: GDP, market rate etc.), y
ItBe explained variable (as Default Probability PD, non-performing loan rate LGD etc.), ε is a Disturbance, obtain explanatory variable and explained variable by data storage device 4, use panel regression model simulates the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant u that tries to achieve
ItAnd factor beta
kDeposit data storage device 4 in.
Step 204: the input pressure data, predict according to analog result
Credit risk pressure measurement device 3 obtains constant and the coefficient that regression model is obtained by reading of data memory storage 4, obtain (exerting pressure) variable of user's selection as the explanatory variable in the formula by user input apparatus 2, try to achieve the Y on the equation left side according to formula, explained variable just, reach by model of fit conducting pressure data, obtain based on prediction result, to predict the outcome deposits data storage device 4 in, and the user can or download the result who analyzes by data output device 5 real time inspections.
Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (17)
1. system that under extreme case, credit risk is assessed, it is characterized in that, this system comprises Source Data Acquisition device (1), user input apparatus (2), credit risk pressure measurement device (3), data storage device (4) and data output device (5), wherein: Source Data Acquisition device (1) is connected in data storage device (4), user input apparatus (2), credit risk pressure measurement device (3), data storage device (4) is connected successively with data output device (5), from data storage device (4), read the source data that Source Data Acquisition device (1) is gathered by credit risk pressure measurement device (3), and the user's input information of this source data and user input apparatus (2) input simulated and prediction processing, be implemented under the extreme case assessment to credit risk.
2. the system that under extreme case, credit risk is assessed according to claim 1, it is characterized in that, described Source Data Acquisition device (1) is used for gathering the source data of macroeconomy data plane in the mode of data-interface from banking system or user interface, and stores the source data of gathering into data storage device (4).
3. the system that under extreme case credit risk is assessed according to claim 2 is characterized in that, described source data comprises client's assets information, Transaction Information and economic target at least.
4. the system that under extreme case, credit risk is assessed according to claim 1, it is characterized in that, described user input apparatus (2) is used to realize mutual between user and the credit risk pressure measurement device (3), the user imports user's input information by this user input apparatus (2), and obtains the macroscopical all kinds of modelings and the prediction result data of credit risk pressure measurement device (3) metering from this user input apparatus (2).
5. the system that under extreme case, credit risk is assessed according to claim 4, it is characterized in that, described user's input information comprises explanatory variable, explained variable and analogy model, wherein explanatory variable comprises that GDP and market rate, explained variable comprise Default Probability PD and promise breaking loss percentage LGD, and analogy model comprises linear regression model (LRM), time series models and panel regression model.
6. the system that under extreme case, credit risk is assessed according to claim 1, it is characterized in that, described credit risk pressure measurement device (3) is used for reading the source data that Source Data Acquisition device (1) is gathered from data storage device (4), and the variable information that the needs of this source data and user input apparatus (2) input are simulated is simulated and prediction processing.
7. the system that under extreme case, credit risk is assessed according to claim 1, it is characterized in that, described data storage device (4) is used to store the source data of the macroeconomy data plane that Source Data Acquisition device (1) collects, and the macroscopical all kinds of modelings and the prediction result data of credit risk pressure measurement device (3) metering.
8. the system that under extreme case credit risk is assessed according to claim 1 is characterized in that, described data output device (5) is used for the data of output and demonstrating data memory storage (4) storage.
9. a method of under extreme case credit risk being assessed is applied to the described system that credit risk is assessed of claim 1, it is characterized in that this method comprises:
Source Data Acquisition device (1) is gathered source data, and stores the source data that collects into data storage device (4);
User input apparatus (2) is imported credit risk pressure measurement device (3) with user's input information;
Credit risk pressure measurement device (3) reads the source data that Source Data Acquisition device (1) is gathered from data storage device (4), the user's input information of this source data and user input apparatus (2) input is simulated and prediction processing;
Credit risk pressure measurement device (3) will be simulated and the result data of prediction processing is stored in the data storage device (4); And
Data output device (5) represents the result data of data storage device (4) to the user.
10. the method for under extreme case, credit risk being assessed according to claim 9, it is characterized in that, described Source Data Acquisition device (1) is gathered in the step of source data, and source data comprises client's assets information, Transaction Information and economic target at least.
11. the method for under extreme case, credit risk being assessed according to claim 9, it is characterized in that, described user input apparatus (2) is imported user's input information in the step of credit risk pressure measurement device (3), user's input information comprises explanatory variable, explained variable and analogy model, wherein explanatory variable comprises that GDP and market rate, explained variable comprise Default Probability PD and promise breaking loss percentage LGD, and analogy model comprises linear regression model (LRM), time series models and panel regression model.
12. the method for under extreme case, credit risk being assessed according to claim 9, it is characterized in that, described credit risk pressure measurement device (3) reads the source data that Source Data Acquisition device (1) is gathered from data storage device (4), user's input information to this source data and user input apparatus (2) input is simulated and prediction processing, specifically comprises:
Credit risk pressure measurement device (3) obtains the match analog variable from data storage device (4), the internal relation of metering variable, and use a model and return simulation process, the input pressure data are simulated and prediction processing according to analog result then.
13. the method for under extreme case, credit risk being assessed according to claim 12, it is characterized in that, the match analog variable that described credit risk pressure measurement device (3) obtains from data storage device (4), comprise explanatory variable and explained variable, explanatory variable comprises that GDP and market rate, explained variable comprise Default Probability PD and promise breaking loss percentage LGD.
14. the method for under extreme case credit risk being assessed according to claim 12 is characterized in that, the internal relation of described credit risk pressure measurement device (3) metering variable comprises:
Credit risk pressure measurement device (3) carries out initial analysis to this match analog variable, in order to screen suitable variable and proper model; This initial analysis adopts the variable to selecting to carry out correlation calculations, the match of variable curve map and the match of standardized variable curve map as the analysis and evaluation means, and result is stored in data storage device (4).
15. the method for under extreme case credit risk being assessed according to claim 14 is characterized in that, described correlation calculations is that the variable that will select carries out the calculating of correlativity, in order to judge the internal relation between selecteed variable; Match of variable curve map and the match of standardized variable curve map then are that the distribution situation by the selected variable that draws reflects intuitively whether selected variable meets the requirement that returns simulation.
16. the method for under extreme case credit risk being assessed according to claim 12 is characterized in that, described credit risk pressure measurement device (3) uses a model and returns simulation, handles with following three kinds of models respectively:
Model one: linear regression model (LRM);
Y＝β
0+β
1X
1+......+β
kX
k+ε
β wherein
0Be constant, β
1β
2... be coefficient, X
1X
2... be explanatory variable, Y is an explained variable, and ε is a Disturbance, obtain explanatory variable and explained variable by data storage device (4), the use linear regression model (LRM) simulates the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant β that tries to achieve
0And factor beta
1β
2... deposit data storage device (4) in;
Model two: time series models;
Wherein C is a constant, α
ib
jBe coefficient, X
J (t-k)Be explanatory variable, y
tBe explained variable, ε is a Disturbance, obtains explanatory variable and explained variable by data storage device (4), and service time, series model simulated the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant C and the factor alpha of trying to achieve
ib
jDeposit data storage device (4) in;
Model three: panel regression model;
U wherein
ItBe constant, β
kBe coefficient, X
ItkBe explanatory variable, y
ItBe explained variable, ε is a Disturbance, obtains explanatory variable and explained variable by data storage device (4), and use panel regression model simulates the internal relation between variable, with constant and the coefficient of obtaining in the formula, with the constant u that tries to achieve
ItAnd factor beta
kDeposit data storage device (4) in.
17. the method for under extreme case credit risk being assessed according to claim 12 is characterized in that, described credit risk pressure measurement device (3) input pressure data are predicted according to analog result, comprising:
Credit risk pressure measurement device (3) obtains constant and the coefficient that regression model is obtained by reading of data memory storage (4), obtain the variable of user's selection as explanatory variable by user input apparatus (2), try to achieve explained variable according to formula, reach by model of fit conducting pressure data, obtain based on prediction result, will predict the outcome deposits data storage device (4) in.
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