CN113269373A

CN113269373A - Risk assessment method and risk assessment device

Info

Publication number: CN113269373A
Application number: CN202110710079.7A
Authority: CN
Inventors: 苏晶晶
Original assignee: Beijing Anjiu Information Technology Co ltd
Current assignee: Beijing Anjiu Information Technology Co ltd
Priority date: 2021-06-25
Filing date: 2021-06-25
Publication date: 2021-08-17

Abstract

The invention discloses a risk assessment method and a risk assessment device, relating to the technical field of computers; the risk assessment method comprises the following steps: acquiring K evaluation regions comprising P evaluation types; acquiring historical related data of each evaluation type, wherein the historical related data comprises a plurality of subdata indexes; building a first data analysis base, a second data analysis base and a third data analysis base; setting at least one time period M, respectively calculating corresponding historical quantiles of the designated data indexes in the time period M based on the first data analysis base, the second data analysis base and the third data analysis base, and acquiring corresponding historical reference intervals; generating a first risk early warning value; generating a second risk early warning value; generating a third risk early warning value; returning the corresponding first average value, second average value and third average value corresponding to week +1 week and/or month +1 month; and generating a risk assessment report. The system is used for comprehensively, regularly and intelligently evaluating the risk of the financial market.

Description

Risk assessment method and risk assessment device

Technical Field

The present invention relates to the field of computer technologies, and in particular, to a risk assessment method and a risk assessment apparatus.

Background

The world today is an information and quantification era, with countless data being generated each day. Currently, many business activities of financial institutions rely more and more on the analysis of a large amount of historical data in order to realize scientific management decisions. Financial risk is an intrinsic property of financial activity that is widely present as an important feature in modern financial markets. Since the 70 s of the 20 th century, the volatility of the financial market has been enhanced and the stability of the financial system has been reduced due to the influence of factors such as the relaxation of regulations and financial liberalization, information technology and financial innovation activities, and the like. This puts increasingly stringent requirements on the innovation of techniques, methods of risk management. In the prior art, the market risk assessment and calculation mostly takes financial institutions and clients as research subjects, and focuses on the assessment and monitoring of risk exposure (position); the comprehensive, regular and intelligent monitoring and evaluation of the overall risk and the cross-market risk of the market are insufficient. Therefore, it is necessary to provide an advanced risk assessment method and risk assessment device that can be used in single market/cross-border and cross-market.

Disclosure of Invention

In view of the above, the present invention provides a risk assessment method and a risk assessment apparatus for performing comprehensive, periodic and intelligent assessment on the risk of the financial market.

In a first aspect, the present application provides a risk assessment method, comprising:

acquiring K evaluation regions and P evaluation types in at least one evaluation region, wherein K is more than 0, P is more than 0, and K, P are positive integers;

acquiring at least part of historical related data of each evaluation type in each evaluation area, wherein the historical related data corresponding to any evaluation type in any evaluation area comprises a plurality of subdata indexes;

building a first data analysis base, a second data analysis base and a third data analysis base based on the historical related data;

setting at least one time period M, calculating a first historical quantile corresponding to a first specified data index in the time period M by taking day/week/month as a period based on the first data analysis database, and acquiring a first historical reference interval corresponding to the first specified data index;

calculating a second historical quantile corresponding to the second specified data index in the time period M by taking day/week/month as a period based on the second data analysis base, and acquiring a second historical reference interval corresponding to the second specified data index;

calculating a third history quantile corresponding to the third specified data index in the time period M by taking day/week/month as a period based on the third data analysis library, and acquiring a third history reference interval corresponding to the third specified data index;

obtaining the number B1 of the historical quantiles of which the sub data indexes are less than or equal to A1% in each evaluation type in any evaluation area, comparing the number B1 with the total number of the sub data indexes in each evaluation type to obtain a ratio D1, and comparing the ratio D1 with a preset ratio to generate a first risk early warning value; returning N1 first history reference intervals corresponding to the first designated data indexes in each evaluation type in any evaluation region, wherein N1 is greater than 0, and N1 is a positive integer; obtaining the number B2 of the historical quantiles of which the sub data indexes are less than or equal to A2% in at least two evaluation types in any evaluation area, comparing the number B2 with the total number of the corresponding sub data indexes to obtain a ratio D2, and comparing the ratio D2 with a preset ratio to generate a second risk early warning value; and returning N2 second historical reference intervals corresponding to the second specified data indexes in at least two evaluation types in any evaluation region, wherein N2 is greater than 0, and N2 is a positive integer; obtaining the number B3 of the historical quantiles of which the sub data indexes are less than or equal to A3% in at least two evaluation types in at least two evaluation areas, comparing the number B3 with the total number of the corresponding sub data indexes to obtain a ratio D3, and comparing the ratio D3 with a preset ratio to generate a third risk early warning value; returning N3 third history reference intervals corresponding to the third designated data indexes in at least two evaluation types in at least two evaluation areas, wherein N3 is greater than 0, and N3 is a positive integer; b1 is more than B2 and less than or equal to B3, A1 is A2 is A3;

returning a first average value of the sub-data indexes corresponding to the week +1 week and/or the month +1 month corresponding to the N1 first historical reference intervals; returning second average values of the sub-data indexes corresponding to the week +1 week and/or the month +1 month corresponding to the N2 second historical reference intervals respectively; returning a third average value of the subdata indexes corresponding to the N3 third history reference intervals respectively in week +1 week and/or month +1 month;

generating a risk assessment report by at least one of the first risk pre-warning value, the second risk pre-warning value, the third risk pre-warning value, the first historical quantile, the second historical quantile, the third historical quantile, the N1 first historical reference intervals, the N2 second historical reference intervals, the N3 third historical reference intervals, the first average value, the second average value, and the third average value.

Optionally, wherein:

the building of the first data analysis base, the second data analysis base and the third data analysis base based on the historical related data specifically comprises the following steps:

setting at least one time period M, and respectively returning a third history quantile of the day/week/month data corresponding to at least one sub data index in the time period M;

calculating a first evaluation index of day/week/month data corresponding to at least any two sub-data indexes X and Y in any evaluation type in any evaluation area in the time period M, wherein the first evaluation index comprises a first correlation coefficient r1_x,y；

Returning fourth historical quantiles of the first evaluation indexes in the corresponding time period M respectively;

calculating at least any one of any two evaluation types in any evaluation regionA second evaluation index of the day/week/month data corresponding to the sub-data indices α and β within the time period M, the second evaluation index including a second correlation coefficient r2_α,β；

Returning fifth historical quantiles of the second evaluation indexes in the corresponding time period M respectively;

calculating a third evaluation index of the data of day/week/month corresponding to the sub-data index g and the sub-data index h in the time period M in any two evaluation types in at least two evaluation areas of the at least two evaluation areas, wherein the third evaluation index includes a third correlation coefficient r3_g,h；

And returning sixth historical quantiles of the third evaluation indexes in the corresponding time period M respectively.

Optionally, wherein:

the obtaining of the first history reference interval corresponding to the first specified data index specifically includes:

comparing the first designated data index with corresponding day/week/month data in the first data analysis base to obtain a first historical quantile interval [ C1, C2] corresponding to the first designated data index, and acquiring N3 historical times in the first historical quantile interval [ C1, C2] from the first data analysis base as a first historical reference interval; n3 > 0, and N3 is a positive integer.

Optionally, wherein:

the obtaining of the second historical reference interval corresponding to the second specified data index specifically includes:

comparing the second specified data index with corresponding day/week/month data in the second data analysis base to obtain a second historical quantile interval [ C3, C4] corresponding to the second specified data index, and acquiring N4 historical times in the second historical quantile interval [ C3, C4] from the second data analysis base as a second historical reference interval; n4 > 0, and N4 is a positive integer.

Optionally, wherein:

the acquiring of the third history reference interval corresponding to the third specified data index specifically includes:

comparing the third designated data index with the corresponding day/week/month data in the third data analysis base to obtain a third history quantile interval [ C5, C6] corresponding to the third designated data index, and acquiring N5 historical times in the third history quantile interval [ C5, C6] from the third data analysis base as a third history reference interval; n5 > 0, and N5 is a positive integer.

Optionally, wherein:

the first risk early warning value comprises a first mild risk early warning state, a first moderate risk early warning state and a first severe risk early warning state; the second risk early warning value comprises a second mild risk early warning state, a second moderate risk early warning state and a second severe risk early warning state; the third risk early warning value comprises a third mild risk early warning state, a third moderate risk early warning state and a third severe risk early warning state.

Optionally, wherein:

the assessment area comprises at least one country or region;

the assessment types include a money market, a bond market, a stock market, a derivative market, and a foreign exchange market.

Optionally, wherein:

the history-related data includes a money market data index, a bond market data index, a stock market data index, a derivative market data index, and a foreign exchange market data index corresponding to the money market, the bond market, the stock market, the derivative market, and the foreign exchange market, respectively.

Optionally, wherein:

the first data analysis repository comprises a plurality of single market data analysis repositories, each of which comprises any of the money market data indicators, the bond market data indicators, the stock market data indicators, the derivatives market data indicators, the fx market data indicators in any of the assessment areas;

the second data analysis repository comprises a plurality of cross-market data analysis repositories, each of the cross-market data analysis repositories including at least two of the money market data indicator, the bond market data indicator, the stock market data indicator, the derivatives market data indicator, the fx market data indicator in any one of the assessment areas;

the third data analysis repository includes a plurality of cross-border and cross-market data analysis repositories, each of the cross-border and cross-market data analysis repositories including at least two of the money market data indicators, the bond market data indicators, the stock market data indicators, the derivatives market data indicators, and the fx market data indicators in at least two of the assessment areas.

In a second aspect, the present application provides a risk assessment apparatus for use in the risk assessment method;

the risk assessment device comprises a data acquisition module, a data analysis base module, a data modeling analysis module and a model report display module; wherein the content of the first and second substances,

the data acquisition module is used for acquiring K evaluation areas and P evaluation types in at least one evaluation area, wherein K is greater than 0, P is greater than 0, and K, P are positive integers; acquiring at least part of historical related data of each evaluation type in each evaluation area, wherein the historical related data corresponding to any evaluation type in any evaluation area comprises a plurality of subdata indexes;

the data analysis library module is used for building a first data analysis library, a second data analysis library and a third data analysis library based on the historical related data;

the data modeling analysis module is used for setting at least one time period M, calculating a first historical quantile corresponding to the first specified data index in the time period M by taking day/week/month as a period based on the first data analysis library, and acquiring a first historical reference interval corresponding to the first specified data index;

the data modeling analysis module is further used for calculating a second historical quantile corresponding to the second specified data index in the time period M by taking day/week/month as a period based on the second data analysis database, and acquiring a second historical reference interval corresponding to the second specified data index;

the data modeling analysis module is further used for calculating a third history quantile corresponding to the third specified data index in the time period M by taking day/week/month as a period based on the third data analysis database, and acquiring a third history reference interval corresponding to the third specified data index;

the data modeling analysis module is further configured to obtain a number B1 of the historical quantile, where the sub-data indexes in each evaluation type in any evaluation region are less than or equal to a 1%, compare the number B1 with the total number of the sub-data indexes in each evaluation type to obtain a ratio D1, and compare the ratio D1 with a preset ratio to generate a first risk early warning value; returning N1 first history reference intervals corresponding to a first designated data index in each evaluation type in any evaluation region, wherein N1 is greater than 0, and N1 is a positive integer; obtaining the number B2 of the historical quantiles of which the sub data indexes are less than or equal to A2% in at least two evaluation types in any evaluation area, comparing the number B2 with the total number of the corresponding sub data indexes to obtain a ratio D2, and comparing the ratio D2 with a preset ratio to generate a second risk early warning value; and returning N2 second historical reference intervals corresponding to second specified data indexes in at least two evaluation types in any evaluation region, wherein N2 is greater than 0, and N2 is a positive integer; obtaining the number B3 of the historical quantiles of which the sub data indexes are less than or equal to A3% in at least two evaluation types in at least two evaluation areas, comparing the number B3 with the total number of the corresponding sub data indexes to obtain a ratio D3, and comparing the ratio D3 with a preset ratio to generate a third risk early warning value; returning N3 third history reference intervals corresponding to third designated data indexes in at least two evaluation types in at least two evaluation areas, wherein N3 is greater than 0, and N3 is a positive integer; b1 is more than B2 and less than or equal to B3, A1 is A2 is A3;

the data modeling analysis module is further used for returning first average values of the sub-data indexes corresponding to N1 first history reference intervals respectively in week +1 week and/or month +1 month; returning second average values of the sub-data indexes corresponding to the week +1 week and/or the month +1 month corresponding to the N2 second historical reference intervals respectively; returning a third average value of the subdata indexes corresponding to the N3 third history reference intervals respectively in week +1 week and/or month +1 month;

the model report display module is configured to generate a risk assessment report according to at least one of the first risk pre-warning value, the second risk pre-warning value, the third risk pre-warning value, the first historical quantile, the second historical quantile, the third historical quantile, the N1 first historical reference intervals, the N2 second historical reference intervals, the N3 third historical reference intervals, the first average value, the second average value, and the third average value.

Compared with the prior art, the risk assessment method and the risk assessment device provided by the invention at least realize the following beneficial effects:

the application provides a risk assessment method and a risk assessment device, which are used for evaluating the risk condition of each assessment type of each assessment area, analyzing the risk relevance among multiple assessment types of each assessment area, identifying the risk degree among multiple assessment types of each assessment area, and predicting the risk trend of each assessment type of each assessment area, so that the risk of a financial market is comprehensively, regularly and intelligently assessed.

Of course, it is not necessary for any product in which the present invention is practiced to achieve all of the above-described technical effects simultaneously.

Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a flowchart illustrating a risk assessment method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of the relationship between two national financial markets provided by an embodiment of the present application;

fig. 3 is a schematic diagram of a risk assessment apparatus according to an embodiment of the present application.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

Fig. 1 is a flowchart illustrating a risk assessment method according to an embodiment of the present application, and referring to fig. 1, the present application provides a risk assessment method, including:

101. acquiring K evaluation regions and P evaluation types in at least one evaluation region, wherein K is more than 0, P is more than 0, and K, P are positive integers;

102. acquiring at least part of historical related data of each evaluation type in each evaluation area, wherein the historical related data corresponding to any evaluation type in any evaluation area comprises a plurality of subdata indexes;

103. building a first data analysis base, a second data analysis base and a third data analysis base based on historical related data;

104. setting at least one time period M, calculating a first historical quantile corresponding to a first designated data index in the time period M by taking day/week/month as a period based on a first data analysis database, and acquiring a first historical reference interval corresponding to the first designated data index;

105. based on a second data analysis base, calculating a second historical quantile corresponding to a second specified data index in a time period M by taking day/week/month as a period, and acquiring a second historical reference interval corresponding to the second specified data index;

106. based on a third data analysis base, calculating a third history quantile corresponding to a third specified data index in a time period M by taking day/week/month as a period, and acquiring a third history reference interval corresponding to the third specified data index;

107. obtaining the number B1 of historical quantiles of which the sub data indexes are less than or equal to A1% in each evaluation type in any evaluation area, comparing the number B1 with the total number of the sub data indexes in each evaluation type to obtain a ratio D1, and comparing the ratio D1 with a preset ratio to generate a first risk early warning value; returning to N1 first history reference intervals corresponding to the first designated data indexes in each evaluation type in any evaluation area, wherein N1 is more than 0, and N1 is a positive integer; obtaining the number B2 of historical quantiles of which the sub data indexes are less than or equal to A2% in at least two evaluation types in any evaluation area, comparing the number B2 with the total number of the corresponding sub data indexes to obtain a ratio D2, and comparing the ratio D2 with a preset ratio to generate a second risk early warning value; returning N2 second historical reference intervals corresponding to second specified data indexes in at least two evaluation types in any evaluation region, wherein N2 is greater than 0, and N2 is a positive integer; obtaining the number B3 of historical quantiles of the subdata indexes which are less than or equal to A3% in at least two evaluation types in at least two evaluation areas, comparing the number B3 with the total number of the corresponding subdata indexes to obtain a ratio D3, and comparing the ratio D3 with a preset ratio to generate a third risk early warning value; returning N3 third history reference intervals corresponding to third designated data indexes in at least two evaluation types in at least two evaluation areas, wherein N3 is more than 0, and N3 is a positive integer; b1 is more than B2 and less than or equal to B3, A1 is A2 is A3;

108. returning a first average value of the subdata indexes corresponding to the week +1 week and/or the month +1 month corresponding to the N1 first historical reference intervals; returning second average values of the sub-data indexes corresponding to the week +1 week and/or the month +1 month corresponding to the N2 second historical reference intervals respectively; returning a third average value of subdata indexes corresponding to the week +1 week and/or month +1 month corresponding to the N3 third history reference intervals;

109. and generating a risk assessment report through at least one of the first risk early warning value, the second risk early warning value, the third risk early warning value, the first historical quantile, the second historical quantile, the third historical quantile, N1 first historical reference intervals, N2 second historical reference intervals, N3 third historical reference intervals, the first average value, the second average value and the third average value.

Specifically, the present application provides a risk assessment method, which at least comprises the following steps 101-109. Step 101 is to obtain a plurality of areas to be evaluated, wherein the areas to be evaluated are specifically countries or regions to be evaluated/monitored, and the like; for example, the evaluation area is a country or region where coverage is evaluated/monitored in china, the united states, the united kingdom, japan, singapore, or the like. And further acquiring a plurality of evaluation types in at least one evaluation area. In the risk assessment of the financial market, the assessment types are specifically a plurality of financial markets needing monitoring and assessment. The method and the device have no specific limitation on the number of the assessment areas and the assessment types (financial markets), and the data can be provided according to the requirements of users in risk assessment of the corresponding markets.

Step 102 is to further obtain historical related data corresponding to the evaluation type of each country or region according to the plurality of evaluation regions selected in step 101 and the plurality of evaluation types corresponding thereto, that is, according to the market range of the selected corresponding country or region, where the historical related data corresponding to any evaluation type in any evaluation region includes a plurality of sub-data indicators, and the sub-data indicators are data for reflecting the quotation, transaction condition, and the like of the corresponding evaluation type. The risk assessment for the financial market is, for example, information about quotations, trades, position data, etc. of the relevant financial market obtained from the financial data service provider.

Step 103 is to build a first data analysis base, a second data analysis base and a third data analysis base based on the historical related data corresponding to each evaluation type in the selected country/region obtained in step 102. The first data analysis library can be, for example, a data analysis library which is constructed in a one-to-one correspondence manner for each evaluation type in any country/region; the second data analysis base can be, for example, a data analysis base which is correspondingly built for at least two evaluation types in any country/region; the third data analysis repository may be, for example, a data analysis repository constructed correspondingly across at least two assessment types in at least two countries/regions.

Step 104 is a risk assessment for a market in a selected country/region, and specifically includes setting at least one time period, for example, the time period is 1 year, calculating a corresponding first historical quantile of at least one first specified data index in a time period of, for example, 1 year based on the first data analysis library set up in step 103, with a small period (frequency) of day/week/month, and obtaining a first historical reference interval corresponding to the first specified data index. Specifically, wherein the "first specified data index" is "update data/latest data"; that is, the risk scale and the history quantile (first history quantile) of the updated data/the latest data are calculated with the frequency of day/week/month, and the corresponding history reference interval (first history reference interval) is identified.

Step 105 is a risk assessment for at least two markets in a selected country/region, specifically including setting at least one time period, for example, the time period is 1 year, calculating a corresponding second historical quantile of at least one second specified data index in a time period of, for example, 1 year based on the second data analysis library set up in step 103, with a small period (frequency) of day/week/month, and obtaining a second historical reference interval corresponding to the second specified data index. Specifically, wherein the "second specified data index" is "update data/latest data"; that is, the risk scale and the history quantile (second history quantile) of the updated data/the latest data are calculated with the frequency of day/week/month, and the corresponding history reference interval (second history reference interval) is identified.

Step 106 is a risk assessment for at least two markets in at least two selected countries/regions, and specifically includes setting at least one time period, for example, the time period is 1 year, calculating a corresponding third history quantile of at least one third specified data index in a time period of, for example, 1 year based on the third data analysis library set up in step 103, with a small period (frequency) of day/week/month, and acquiring a third history reference interval corresponding to the third specified data index. Specifically, wherein the "third specified data index" is "update data/latest data"; that is, the risk scale and the history quantile (third history quantile) corresponding to the updated data/the latest data are calculated with the frequency of day/week/month, and the corresponding history reference interval (third history reference interval) is identified.

Step 107, for each evaluation type in one evaluation area, obtaining a number B1 of historical quantiles of the sub-data indexes in each evaluation type in one evaluation area, which are less than or equal to a 1%, comparing the number B1 with the total number of the sub-data indexes in each evaluation type to obtain a ratio D1, and comparing the ratio D1 with a preset ratio to generate a first risk early warning value. For example, for a certain financial market in a country/region, the sub-data indicator exceeding 1/2 shows a historical quantile interval less than or equal to 10%, which may be a warning state indicating that the financial market is in a certain risk early warning state; the subdata indicator exceeding 1/2 shows a historical quantile interval of less than or equal to 5%, which may be a prompt that the market is in another risk early warning state. It should be noted that, in the present application, the value of a1 is not specifically limited, and a user may correspondingly limit the value according to his own needs to adjust the accuracy of risk prediction. And then returning to N1 first history reference intervals corresponding to the first designated data index in each evaluation type in an evaluation area, wherein the value of N1 is not limited in the application, and a user can adjust the value according to the requirement of the user. It should be noted that, when "returning the N1 first history reference intervals corresponding to the first specified data index in each evaluation type in any evaluation area", specifically, returning the N1 first history reference intervals that are more similar to data/closest to the latest data in each evaluation type in any evaluation area.

For at least two evaluation types in one evaluation area, the number B2 of historical quantiles of the sub-data indexes less than or equal to a 2% in any two evaluation types in the one evaluation area can be obtained, the number B2 is compared with the total number of the corresponding sub-data indexes to obtain a ratio D2, and the ratio D2 is compared with a preset ratio to generate a second risk early warning value. For example, for two financial markets in a country/region, the sub-data indicator exceeding 1/3 shows a historical quantile interval less than or equal to 10%, which may be a warning indication that the two corresponding financial markets are in a certain risk early warning state; the subdata indicator exceeding 1/3 shows a historical quantile interval of less than or equal to 5%, which may be a warning to indicate that two financial markets corresponding to the country/region are in another risk early warning state. It should be noted that, in the present application, the value of a2 is not specifically limited, and a user may correspondingly limit the value according to his own needs to adjust the accuracy of risk prediction. And then returning to N2 second historical reference intervals corresponding to second specified data indexes in any two evaluation types in one country/region, wherein the value of N2 is not limited in the application, and a user can adjust the value according to the requirement of the user. It should be noted that, when "returning N2 second history reference intervals corresponding to the second specified data index in at least two evaluation types in any evaluation area", specifically, returning N2 second history reference intervals with the closest updated data/latest data in at least two evaluation types in any evaluation area.

For at least two evaluation types in the at least two evaluation areas, the number B3 of historical quantiles of the sub-data indexes less than or equal to a 3% in any two evaluation types in the at least two evaluation areas can be obtained, the number B3 is compared with the total number of the corresponding sub-data indexes to obtain a ratio D3, and the ratio D3 is compared with a preset ratio to generate a third risk early warning value. For example, for two financial markets in at least two countries/regions, the sub-data indicator exceeding 1/3 shows a historical quantile interval less than or equal to 10%, which may be a warning indication that the two financial markets corresponding to the at least two countries/regions are in a certain risk early warning state; the subdata indicator exceeding 1/3 shows a historical quantile range of less than or equal to 5%, which may be a warning to indicate that two financial markets corresponding to the at least two countries/regions are in another risk early warning state. It should be noted that, in the present application, the value of a3 is not specifically limited, and a user may correspondingly limit the value according to his own needs to adjust the accuracy of risk prediction. And then returning to N3 third history reference intervals corresponding to third designated data indexes in any two evaluation types in at least two countries/regions, wherein the value of N3 is not limited in the application, and the user can adjust the reference intervals according to the self requirement. It should be noted that, when "returning the N3 third history reference intervals corresponding to the third specified data indexes in the at least two evaluation types in the at least two evaluation regions", specifically, returning the N3 third history reference intervals with the closest updated data/latest data in the at least two evaluation types in the at least two evaluation regions.

A specific embodiment is provided herein, wherein the value of N1, N2, N3 may be 5, for example, and each of a single evaluation type in one evaluation area, at least any two evaluation types in one evaluation area, or at least any two evaluation types in at least two evaluation areas may return their corresponding 5 closest historical reference intervals. It should be noted that, in the present application, the values of N1, N2, and N3 are not specifically limited, and a user may adjust the values according to needs.

In step 108, the first average value of the sub-data indexes corresponding to the N1 first history reference intervals in the future week (week +1 week) and/or the future month (month +1 month) is returned, and the first average value can be used as the predicted value of the future week/month of a certain financial market related index in a certain corresponding country/region. And returning a second average value of the sub-data indexes corresponding to the N2 second historical reference intervals respectively in the future week (week +1 week) and/or the future month (month +1 month), wherein the second average value can be used as a predicted value of the future week/month of at least two financial market related indexes in a corresponding country/region. And returning a third average value of the subdata indexes corresponding to the N3 third history reference intervals respectively corresponding to the future week (week +1 week) and/or the future month (month +1 month), wherein the third average value can be used as a predicted value of the future week/month of at least two financial market related indexes in at least two corresponding countries/regions. "future week" and "future month" are explained herein, such as for example, week 1 of month 3 now, and "future week" refers to week 2 of month 3; for example, now month 5, then "future month" refers to month 6.

Step 109 is to generate a risk assessment report through at least one of the first risk early warning value, the second risk early warning value, the third risk early warning value, the first historical quantile, the second historical quantile, the third historical quantile, N1 first historical reference intervals, N2 second historical reference intervals, N3 third historical reference intervals, the first average value, the second average value, and the third average value obtained in the previous steps, where the risk assessment report may be used to display future risk conditions of the data assessed by the risk assessment report. It should be noted that the first risk early warning value, the second risk early warning value, and the third risk early warning value do not only represent a fixed risk level/state; that is, the value range of the first risk early warning value, the second risk early warning value or the third risk early warning value is not limited to only one point value, but also can be an interval value; for example, an indicator may be at one risk when it falls within a specified interval and at another risk when it exceeds a specified interval.

Fig. 2 is a schematic diagram illustrating a relationship between two national financial markets provided in the present embodiment, please refer to fig. 2, wherein optionally, the evaluation area includes at least one country or region; the assessment types include a money market, a bond market, a stock market, a derivative market, and a foreign exchange market. Specifically, FIG. 2 illustrates an embodiment in which an assessment area includes country A and country B, each of which includes assessment types that include multiple financial markets; the above 5 financial markets are only a few exemplary types of financial markets provided in the present application, and are not intended to limit the types and numbers included in the financial markets, and the users may adjust the types, numbers, and the like included in the financial markets according to the contents to be evaluated actually.

Optionally, the history-related data comprises a currency market data index, a bond market data index, a stock market data index, a derivative market data index, and a foreign exchange market data index corresponding to the currency market, the bond market, the stock market, the derivative market, and the foreign exchange market, respectively.

Specifically, the money market data index includes inter-bank market (deposit institution) pledge repurchase interest rate, inter-bank market disconnected repurchase, inter-bank borrowing rate, repurchase price rate, and exchange repurchase interest rate, among others. The bond market data indexes comprise national bonds, national bond, local government bonds, enterprise bonds, city investment bonds, asset support bond earning rate data, rise and fall amplitude, volume of transaction, number of transaction strokes and amount of transaction. The stock market data indexes include closing price (point), fluctuation range, hand-off rate, volume and amplitude of all domestic listed companies and main stock indexes. The market data indexes of the derivatives comprise closing price, settlement price, fluctuation amplitude, volume of transaction and change of position holding amount of commodity futures, stock-index futures and national bond futures mastery contracts. The market data indexes of foreign exchange include the intermediate price, fluctuation and fluctuation range of main currency exchange rate, and the index, fluctuation and fluctuation range of main currency. It should be noted that, what is included in the data indexes is an alternative embodiment provided in the present application, and is not a limitation to the present application.

Optionally, the first data analysis repository comprises a plurality of single market data analysis repositories, each single market data analysis repository comprising any of a currency market data index, a bond market data index, a stock market data index, a derivative market data index, a foreign exchange market data index in any one of the assessment areas;

the second data analysis repository comprises a plurality of cross-market data analysis repositories, each of which comprises at least two of currency market data indicators, bond market data indicators, stock market data indicators, derivatives market data indicators, and foreign exchange market data indicators in any one of the evaluation areas;

the third data analysis library comprises a plurality of cross-border and cross-market data analysis libraries, each of which comprises at least two of a currency market data index, a bond market data index, a stock market data index, a derivative market data index, and a foreign exchange market data index in at least two assessment areas.

Optionally, building a first data analysis library, a second data analysis library and a third data analysis library based on the historical related data specifically includes:

step 1031, setting at least one time period M, and respectively returning a third history quantile of day/week/month data corresponding to at least one subdata index in the time period M;

step 1032, calculating a first evaluation index of day/week/month data corresponding to at least any two sub-data indexes X and Y in any evaluation type in any evaluation area in the time period M, where the first evaluation index includes a first correlation coefficient r1_x,y；

Step 1033, returning fourth historical quantiles of the first evaluation index in the corresponding time period M respectively;

step 1034, calculating a second evaluation index of the day/week/month data corresponding to at least any one sub-data index α and β in any two evaluation types in any evaluation area in the time period M, where the second evaluation index includes a second correlation coefficient r2_α,β；

Step 1035, returning fifth historical quantiles of the second evaluation indexes in the corresponding time period M respectively; step 1036, calculating a third evaluation index of the day/week/month data corresponding to at least any sub-data index g and h in the time period M in at least two evaluation types in the at least two evaluation areas, where the third evaluation index includes a third correlation coefficient r3_g,h；

And 1037, returning sixth historical quantiles of the third evaluation indexes in the corresponding time period M respectively.

Specifically, the first data analysis library may be, for example, a data analysis library which is constructed in a one-to-one correspondence manner for each evaluation type in any country/region, and colloquially, a single market data analysis library in any country/region; the second data analysis library can be, for example, a data analysis library which is correspondingly built for at least two evaluation types in any country/region, and is colloquially a cross-market data analysis library of any country/region; the third data analysis library may be, for example, a data analysis library constructed correspondingly across at least two assessment types in at least two countries/regions, colloquially referred to as a cross-border and cross-market data analysis library across at least two countries/regions. For example, a single market, cross-border and cross-market data analysis library is constructed based on the main data of the currency market, bond market, stock market, derivatives market and foreign exchange market in China and the United states since 2000.

Step 103 specifically includes that, in step 1031, at least one time period M is set, for example, M may be selected to be 1 year, and the third historical quantiles of the day/week/month data corresponding to at least one sub data index in the selected country/region within 1 year (time period M) are returned respectively. A specific embodiment is provided, taking 1-year historical quantiles corresponding to Shanghai depth 300 exponential fluctuation range data of 4 and 9 days in 2019 as an example, and the daily fluctuation range X is in the historical quantile Z of the 2019 full-year fluctuation range data_aIs expressed as p (X is less than or equal to Z)_a) α. Further, the method further includes step 10311, and the third history quantiles of the day/week/month data corresponding to at least one sub-data index in the corresponding time period are returned by referring to the calculation mode in step 1031 and taking M as the period of 3 years, 5 years and 10 years, respectively. The above 3 years, 5 years and 10 years are only exemplary, and are not intended to limit the value of M.

Further, step 1032 is to calculate a first evaluation index of the day/week/month data corresponding to at least any two sub-data indexes X and Y in any single market in any selected country/region (evaluation area), where the first evaluation index includes a first correlation coefficient r1_x,y；

Step 1033 is to return to the fourth historical quantiles of the first evaluation indexes in the corresponding time period M in step 1032 respectively, referring to the calculation manner in step 1031.

Step 1034 is to calculate a second evaluation index of day/week/month data corresponding to at least any one of the sub-data indexes α and β in the time period M in any two cross-markets in any selected country/region (evaluation region) with reference to the calculation method of step 1032, where the second evaluation index includes a second correlation coefficient r2_α,β；

Further, in step 1035, fifth historical quantiles of the second evaluation index at corresponding time periods M are returned.

Step 1036 is to calculate a third evaluation index of the day/week/month data corresponding to at least any one sub-data index g and h in the time period M in any two cross-markets in any selected at least two countries/regions (evaluation areas) with reference to the calculation method in step 1032, where the third evaluation index includes a third correlation coefficient r3_g,h；

Further, in step 1037, sixth historical quantiles of the third evaluation index at corresponding time periods M are returned.

The "time period" in steps 1033, 1035, and 1037 may include, for example, M being 1 year, 3 years, 5 years, and 10 years in specific embodiments.

Optionally, the obtaining of the first history reference interval corresponding to the first specified data index specifically includes: comparing the first designated data index with the corresponding day/week/month data in the first data analysis base to obtain a first historical quantile interval [ C1, C2] corresponding to the first designated data index, and acquiring N3 historical times in the first historical quantile interval [ C1, C2] from the first data analysis base as a first historical reference interval; n3 > 0, and N3 is a positive integer.

Specifically, the inclusion of step 104 is described herein in connection with a specific embodiment. Step 104 may include step 1041 of comparing a latest value (updated data/latest data/first designated data index) of a data index with historical data of the data index in a historical database (first data analysis library), comparing a historical quantile interval in which the latest value is located, and returning a plurality of pieces of historical interval data closest to the latest value as a historical reference interval according to the latest value of the index. For example, the date-based frequency of returning the update data/latest data (first designated data index)The historical quantile in the last year (first historical quantile). Using the updated daily data as the end value to push forward for one year, using 8/7/2019 as the latest data X as an example, pushing forward for one year to 8/2018, and returning to the historical quantile Z of the data X of 8/7/2019 in the 8/7/2018_aIs expressed as p (X is less than or equal to Z)_a)＝α。

Step 1042 is included of comparing the updated/updated data (first specified data index) with the daily data of the single market data analytics repository in the selected country/region, and returning the closest 5 historical periods as the historical reference interval (first historical reference interval). The specific calculation method is as follows: assuming that the value of the updated data/latest data is a, and the corresponding first historical quantile is in the interval [ C1, C2], screening the single market data analysis library that the data of the index corresponding to the year is also in the interval [ C1, C2], and taking 5 historical periods with the values of the corresponding index closest to a as historical reference intervals (first historical reference intervals).

Furthermore, step 1043 may further include updating the historical quantiles corresponding to the weekly and monthly data with reference to the calculation method of step 1041; step 1044 is updating the historical reference intervals corresponding to the weekly and monthly data with reference to the calculation method of step 1042; step 1045 is updating the historical quantiles and the historical reference intervals of the day/week/month data of 3 years, 5 years, and 10 years, with reference to the calculation methods of steps 1041 to 1044.

Optionally, the obtaining a second historical reference interval corresponding to the second specified data index specifically includes:

Specifically, the latest value (updated data/latest data/second specified data index) of a data index is compared with the historical data of the data index in a historical database (second data analysis base), the historical quantile interval where the latest value is located is compared, and according to the latest value of the index, a plurality of pieces of historical interval data which are closest to the latest value are returned to serve as the historical reference interval.

The content included in step 105 may specifically include, for example, the following step 1051, that is, comparing a latest value (updated data/latest data/second specified data index) of one data index with historical data of the data index in a historical database (second data analysis base), comparing a historical quantile interval where the latest value is located, and returning a plurality of pieces of historical interval data closest to the latest value as a historical reference interval according to the latest value of the index. Calculating a fourth evaluation index of any two cross-market daily indexes X and Y of the selected country/region, wherein the fourth evaluation index comprises a fourth correlation number r4_x,y。

Step 1052 is returning the historical quantile of the last year in which the updated/newest data was located, with the date as the frequency. Pushing forward for one year by taking the updated daily data as the end value, and taking 8, 7 and 2019 as the latest data

For example, the data is pushed forward by one year to 2018, 8 and 8 months and 8 and 7 days in 2019

Historical quantile Z between 8 months and 8 months of 2018 and 7 months of 2019_aIs expressed by formula as

Step 1053 is to compare the updated/newest data with the day data of the cross-market data analysis base, and return the closest 5 historical periods as the historical reference interval. The algorithm of step 1042 in reference to step 104 finds the corresponding historical reference interval. Step 1054 is to update the historical quantiles corresponding to the weekly and monthly data with reference to the calculation method of step 1051. Step 1055 is to update the history reference section corresponding to the weekly and monthly data with reference to the calculation method of step 1052. Step 1056 is to update the historical quantiles of the day/week/month data and the historical reference interval of the 3, 5, and 10 year old, with reference to the calculation methods of steps 1052-1055.

Optionally, the obtaining a third history reference interval corresponding to a third specified data index specifically includes:

Specifically, the latest value (updated data/latest data/third specified data index) of a data index is compared with the historical data of the data index in a historical database (third data analysis database), the historical quantile interval in which the latest value is located is compared, and according to the latest value of the index, a plurality of pieces of historical interval data which are closest to the latest value are returned to serve as the historical reference interval.

The content included in step 106 may specifically include, for example, the following step 1061, that is, comparing a latest value (updated data/latest data/third specified data index) of one data index with historical data of the data index in a historical database (third data analysis base), comparing a historical quantile interval where the latest value is located, and returning a plurality of pieces of historical interval data closest to the latest value as a historical reference interval according to the latest value of the index. Calculating a fifth evaluation index of any two cross-market daily indexes X and Y of the selected at least two countries/regions, wherein the fifth evaluation index comprises a fifth correlation coefficient r5_x,y。

Step 1062 is to return the historical quantile of the last year in which the updated/newest data was located with the date as the frequency. Pushing forward for one year by taking the updated daily data as the end value, and taking 8, 7 and 2019 as the latest data

Step 1063 is comparing the updated/updated data with the daily data of the cross-border and cross-market data analysis libraries, and returning the closest 5 historical periods as the historical reference interval. The algorithm of step 1042 in reference to step 104 finds the corresponding historical reference interval. Step 1064 is to update the historical quantiles corresponding to the weekly and monthly data with reference to the calculation method of step 1061. Step 1065 is to update the history reference section corresponding to the weekly and monthly data with reference to the calculation method of step 1062. Step 1066 is to update the historical daily/weekly/monthly data quantiles and the historical reference interval for 3, 5, and 10 years, with reference to the calculation methods of steps 1062-1065.

Note that the calculation formula of the evaluation index (first evaluation index/second evaluation index/third evaluation index/fourth evaluation index/fifth evaluation index) provided above exemplifies Pearson simple correlation coefficients (first correlation coefficient/second correlation coefficient/third correlation coefficient/fourth correlation coefficient/fifth correlation coefficient):

however, the present application is not limited thereto, and the following formula may be used for calculating the evaluation index, for example:

spearman rank correlation coefficient:

wherein, C (u)₁,u₂) Is a random variable X₁And X₂Copula function of (a).

Kendall rank correlation coefficient:

gini coefficient:

blomqvist median correlation coefficient:

wherein the content of the first and second substances,

and

are each X₁And X₂The median of (3). Beta (X)₁,X₂)＝4C(1/2,1/2)-1。

CoP：

Suppose X_iAnd X_jRepresenting profitability, X, of markets i and j_iAnd X_jIs subject to F_i,jMarginal distribution obeys F_iAnd F_jDefinition of

Representing the probability that, at a given probability level alpha, market j is in a crisis state, and market i is also in a crisis state. The value of alpha is usually 95% or 99%.

Wherein, the binary extreme value structure function C_i,j(u, v) can be expressed as:

C_i,j(u,v)＝exp{-V(-1/logu,-1/logv)}

wherein A (t) ═ t^δ+(1-t)^δ)^1/δ，V(x,y)＝(x^-δ+y^-δ)^1/δ。

CoV：

Suppose X_iAnd X_jRepresenting the loss (negative in profitability) of markets i and j,

representing the maximum potential loss for market i at a given probability level alpha when market j is in a crisis state. Is expressed by formula as

MiS:

representing the marginal expected loss for market i when market j is in a crisis state at a given probability level alpha. The value of alpha is usually 95% or 99%.

Wherein M is_i＝g(Y_i)。

The estimate of (c) is closely related to the binary distribution assumption, which, if based on the conventional normal distribution assumption,

when M is_i＝Y_iAnd j represents the total number of the markets,

when M is_i＝CS_iAnd j represents the total number of the markets,

when M is_i＝ω_iY_iAnd j represents the total number of the markets,

optionally, the first risk early warning value includes a first mild risk early warning state, a first moderate risk early warning state, and a first severe risk early warning state; the second risk early warning value comprises a second mild risk early warning state, a second moderate risk early warning state and a second severe risk early warning state; the third risk early warning value comprises a third mild risk early warning state, a third moderate risk early warning state and a third severe risk early warning state.

Specifically, in the embodiment illustrated in step 107, only two risk early warning values are included, for example, for a certain financial market (single market), if the sub-data indicator exceeding 1/2 shows a historical quantile interval of less than or equal to 10%, it indicates that the financial market is in the first mild risk early warning state; and if the sub-data index exceeding 1/2 shows a historical quantile interval less than or equal to 5%, prompting that the market is in a first moderate risk early warning state.

For example, for a certain two financial markets (cross-markets), if the sub-data index exceeding 1/3 shows a historical quantile interval less than or equal to 10%, the financial market is prompted to be in a second light risk early warning state; and if the sub-data index exceeding 1/3 shows a historical quantile interval less than or equal to 5%, prompting that the market is in a second moderate risk early warning state.

For example, for a certain two cross-border financial markets (cross-border cross-markets), if the sub-data index exceeding 1/3 shows a historical quantile interval less than or equal to 10%, the financial market is prompted to be in a third light risk early warning state; and if the sub-data index exceeding 1/3 shows a historical quantile interval less than or equal to 5%, prompting that the market is in a third moderate risk early warning state.

The above mentioned early warning states including mild and moderate risk states are only an optional way provided by the present application, and on this basis, corresponding severe risk early warning states can be further set. For example, for a financial market (single market) in a selected country/region, if the sub-data indicator exceeding 1/2 shows a history quantile range of 2% or less, it may be selected and set to indicate that the market is in the first severe risk early warning state. Optionally, for two financial markets (cross-market) in the selected country/region, the sub-data indicator exceeding 1/3 shows a historical quantile interval of 2% or less, indicating that the market is in the second heavy risk early warning state. Optionally, for two financial markets (cross-border and cross-market) of at least two selected countries/regions, if the sub-data indicator exceeding 1/3 shows a historical quantile interval less than or equal to 2%, the market is prompted to be in a third serious risk early warning state.

It should be added that, while the risk pre-warning is performed on each type of market in each area through the above step 107, the identification of the crisis type faced by the market can also be performed. The infection models of risk cross-border and cross-market mainly include three types: firstly, a risk event occurs in the currency market, namely a simple liquidity crisis; secondly, risk events occur simultaneously in any combination of the currency market, the bond market, the stock market and the derivative market, namely, a liquidity crisis and a financial institution credit crisis occur simultaneously; thirdly, the currency market, the bond market, the stock market and the derivative market are combined randomly, and risk events occur simultaneously with the foreign exchange market, namely, a liquidity crisis, a financial institution credit crisis and a ownership credit crisis occur simultaneously.

Fig. 3 is a schematic view of a risk assessment apparatus according to an embodiment of the present application, please refer to fig. 3 in combination with fig. 1, and based on the same inventive concept, the present application further provides a risk assessment apparatus 20 for use in the risk assessment method;

the risk assessment device 20 comprises a data acquisition module 21, a data analysis library module 22, a data modeling analysis module 23 and a model report display module 24; wherein the content of the first and second substances,

the data acquisition module 21 is configured to acquire K evaluation regions and P evaluation types in at least one evaluation region, where K is greater than 0, P is greater than 0, and K, P are positive integers; acquiring at least part of historical related data of each evaluation type in each evaluation area, wherein the historical related data corresponding to any evaluation type in any evaluation area comprises a plurality of subdata indexes;

the data analysis base module 22 is used for building a first data analysis base, a second data analysis base and a third data analysis base based on historical related data;

the data modeling analysis module 23 is configured to set at least one time period M, calculate, based on the first data analysis library, a first historical quantile corresponding to the first specified data index in the time period M by taking day/week/month as a period, and acquire a first historical reference interval corresponding to the first specified data index;

the data modeling analysis module 23 is further configured to calculate, based on the second data analysis library, a second historical quantile corresponding to a second specified data index in the time period M by taking day/week/month as a period, and obtain a second historical reference interval corresponding to the second specified data index;

the data modeling analysis module 23 is further configured to calculate, based on the third data analysis library, a third history quantile corresponding to a third specified data index in the time period M by taking day/week/month as a period, and acquire a third history reference interval corresponding to the third specified data index;

the data modeling analysis module 23 is further configured to obtain a number B1 of historical quantiles of sub-data indexes less than or equal to a 1% in each evaluation type in any evaluation area, compare the number B1 with the total number of the sub-data indexes in each evaluation type to obtain a ratio D1, compare the ratio D1 with a preset ratio, and generate a first risk early warning value; returning to N1 first history reference intervals corresponding to the first designated data indexes in each evaluation type in any evaluation area, wherein N1 is more than 0, and N1 is a positive integer; obtaining the number B2 of historical quantiles of which the sub data indexes are less than or equal to A2% in at least two evaluation types in any evaluation area, comparing the number B2 with the total number of the corresponding sub data indexes to obtain a ratio D2, and comparing the ratio D2 with a preset ratio to generate a second risk early warning value; returning N2 second historical reference intervals corresponding to second specified data indexes in at least two evaluation types in any evaluation region, wherein N2 is greater than 0, and N2 is a positive integer; obtaining the number B3 of historical quantiles of the subdata indexes which are less than or equal to A3% in at least two evaluation types in at least two evaluation areas, comparing the number B3 with the total number of the corresponding subdata indexes to obtain a ratio D3, and comparing the ratio D3 with a preset ratio to generate a third risk early warning value; returning N3 third history reference intervals corresponding to third designated data indexes in at least two evaluation types in at least two evaluation areas, wherein N3 is more than 0, and N3 is a positive integer; b1 is more than B2 and less than or equal to B3, A1 is A2 is A3;

the data modeling analysis module 23 is further configured to return first average values of the sub-data indexes corresponding to N1 first history reference intervals, respectively corresponding to week +1 week and/or month +1 month; returning second average values of the sub-data indexes corresponding to the week +1 week and/or the month +1 month corresponding to the N2 second historical reference intervals respectively; returning a third average value of subdata indexes corresponding to the week +1 week and/or month +1 month corresponding to the N3 third history reference intervals;

the model report display module 24 is configured to generate a risk assessment report according to at least one of a first risk early warning value, a second risk early warning value, a third risk early warning value, a first historical quantile, a second historical quantile, a third historical quantile, N1 first historical reference intervals, N2 second historical reference intervals, N3 third historical reference intervals, a first average value, a second average value, and a third average value.

Specifically, risk assessment device 20 includes a data acquisition module 21, a data analysis library module 22, a data modeling analysis module 23, and a model report presentation module 24.

The data acquisition module 21 is used in the foregoing

steps

101 and 102 to acquire a plurality of evaluation areas and a plurality of types to be evaluated in at least one evaluation area; and further acquiring some historical related data corresponding to each evaluation type in each evaluation area, wherein the historical related data corresponding to any evaluation type in any evaluation area comprises a plurality of sub-data indexes, and the sub-data indexes are data used for reflecting quotations, transaction conditions and the like of the corresponding evaluation type.

The data analysis library module 22 is used for the aforementioned step 103, and builds a first data analysis library, a second data analysis library and a third data analysis library based on the historical relevant data of each evaluation type in the selected country/region, which is acquired by the data acquisition module 21.

The data modeling analysis module 23 is used for the above step 104, risk assessment for a market in a selected country/region; specifically, at least one time period is set, for example, the time period is 1 year, based on the first data analysis library set up in step 103, a corresponding first historical quantile of at least one first specified data index in the time period of, for example, 1 year is calculated with day/week/month as a small period (frequency), and a first historical reference interval corresponding to the first specified data index is obtained. Specifically, wherein the "first specified data index" is "update data/latest data"; that is, the risk scale and the history quantile (first history quantile) of the updated data/the latest data are calculated with the frequency of day/week/month, and the corresponding history reference interval (first history reference interval) is identified.

The data modeling analysis module 23 is also used for the above step 105, risk assessment for at least two markets in a selected country/region; specifically, at least one time period is set, for example, the time period is 1 year, based on the second data analysis library set up in step 103, a corresponding second historical quantile of at least one second specified data index in the time period of, for example, 1 year is calculated with day/week/month as a small period (frequency), and a second historical reference interval corresponding to the second specified data index is obtained. Specifically, wherein the "second specified data index" is "update data/latest data"; that is, the risk scale and the history quantile (second history quantile) of the updated data/the latest data are calculated with the frequency of day/week/month, and the corresponding history reference interval (second history reference interval) is identified.

The data modeling analysis module 23 is also used for risk assessment for at least two markets in at least two selected countries/regions, step 106, above; specifically, at least one time period is set, for example, the time period is 1 year, based on the third data analysis library set up in step 103, a corresponding third history quantile of at least one third specified data index in the time period of, for example, 1 year is calculated with day/week/month as a small period (frequency), and a third history reference interval corresponding to the third specified data index is obtained. Specifically, wherein the "third specified data index" is "update data/latest data"; that is, the risk scale and the history quantile (third history quantile) corresponding to the updated data/the latest data are calculated with the frequency of day/week/month, and the corresponding history reference interval (third history reference interval) is identified.

The data modeling analysis module 23 is further configured to, in step 107, obtain, for each evaluation type in one evaluation area, a number B1 of historical quantiles of the sub-data indicators of each evaluation type in one evaluation area, which are less than or equal to a 1%, compare the number B1 with the total number of the sub-data indicators in each evaluation type, obtain a ratio D1, and compare the ratio D1 with a preset ratio to generate a first risk early warning value. For example, for a certain financial market in a country/region, the sub-data indicator exceeding 1/2 shows a historical quantile interval less than or equal to 10%, which may be a warning state indicating that the financial market is in a certain risk early warning state; the subdata indicator exceeding 1/2 shows a historical quantile interval of less than or equal to 5%, which may be a prompt that the market is in another risk early warning state. It should be noted that, in the present application, the value of a1 is not specifically limited, and a user may correspondingly limit the value according to his own needs to adjust the accuracy of risk prediction. And then returning to N1 first history reference intervals corresponding to the first designated data index in each evaluation type in an evaluation area, wherein the value of N1 is not limited in the application, and a user can adjust the value according to the requirement of the user. It should be noted that, when "returning the N1 first history reference intervals corresponding to the first specified data index in each evaluation type in any evaluation area", specifically, returning the N1 first history reference intervals with the closest updated data/latest data in each evaluation type in any evaluation area.

For at least two evaluation types in the at least two evaluation areas, the number B3 of historical quantiles of the sub-data indexes less than or equal to a 3% in any two evaluation types in the at least two evaluation areas can be obtained, the number B3 is compared with the total number of the corresponding sub-data indexes to obtain a ratio D3, and the ratio D3 is compared with a preset ratio to generate a third risk early warning value. For example, for two financial markets in at least two countries/regions, the sub-data indicator exceeding 1/3 shows a historical quantile interval less than or equal to 10%, which may be a warning indication that the two financial markets corresponding to the at least two countries/regions are in a certain risk early warning state; the subdata indicator exceeding 1/3 shows a historical quantile range of less than or equal to 5%, which may be a warning to indicate that two financial markets corresponding to the at least two countries/regions are in another risk early warning state. It should be noted that, in the present application, the value of a3 is not specifically limited, and a user may correspondingly limit the value according to his own needs to adjust the accuracy of risk prediction. And then returning to N3 third history reference intervals corresponding to third designated data indexes in any two evaluation types in at least two countries/regions, wherein the value of N3 is not limited in the application, and the user can adjust the reference intervals according to the self requirement. It should be noted that, when "returning the N3 third history reference intervals corresponding to the third specified data index in the at least two evaluation types in the at least two evaluation regions", specifically, returning the N3 third history reference intervals closest to the third specified data index in the at least two evaluation types in the at least two evaluation regions.

A specific embodiment is provided herein, wherein the value of N1, N2, N3 may be 5, for example, and each of a single evaluation type in one evaluation area, at least any two evaluation types in one evaluation area, or at least any two evaluation types in at least two evaluation areas may return their corresponding 5 closest historical reference intervals.

The data modeling and analyzing module 23 is further configured to return, in the step 108, the first average value of the sub-data indicators corresponding to the N1 first historical reference intervals, which is used as a predicted value of one future week/month of one financial market-related indicator in one corresponding country/region, for one future week (week +1 week) and/or one future month (month +1 month). And returning a second average value of the sub-data indexes corresponding to the N2 second historical reference intervals respectively in the future week (week +1 week) and/or the future month (month +1 month), wherein the second average value can be used as a predicted value of the future week/month of at least two financial market related indexes in a corresponding country/region. And returning a third average value of the subdata indexes corresponding to the N3 third history reference intervals respectively corresponding to the future week (week +1 week) and/or the future month (month +1 month), wherein the third average value can be used as a predicted value of the future week/month of at least two financial market related indexes in at least two corresponding countries/regions. "future week" and "future month" are explained herein, such as for example, week 1 of month 3 now, and "future week" refers to week 2 of month 3; for example, now month 5, then "future month" refers to month 6.

The model report display module 24 is configured to, in step 109, generate a risk assessment report through at least one of the first risk early warning value, the second risk early warning value, the third risk early warning value, the first historical quantile, the second historical quantile, the third historical quantile, N1 first historical reference intervals, N2 second historical reference intervals, N3 third historical reference intervals, the first average value, the second average value, and the third average value obtained in the foregoing step, where the risk assessment report may be used to display future risk conditions of data assessed by the risk assessment report.

Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustrative purposes only and are not intended to limit the scope of the present invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A method of risk assessment, comprising:

2. The risk assessment method according to claim 1, wherein the building of the first data analysis library, the second data analysis library and the third data analysis library based on the historical related data specifically comprises:

calculating a second evaluation index of day/week/month data corresponding to at least any one of the sub-data indexes alpha and beta in any two evaluation types in any evaluation area in the time period M, wherein the second evaluation index comprises a second correlation coefficient r2_α,β；

3. The risk assessment method according to claim 1, wherein the obtaining of the first historical reference interval corresponding to the first specified data index specifically includes:

4. The risk assessment method according to claim 1, wherein the obtaining of the second historical reference interval corresponding to the second specified data index specifically includes:

5. The risk assessment method according to claim 1, wherein the obtaining of the third history reference interval corresponding to the third specified data index specifically includes:

6. The risk assessment method of claim 1, wherein the first risk pre-warning value comprises a first mild risk pre-warning state, a first moderate risk pre-warning state, a first severe risk pre-warning state; the second risk early warning value comprises a second mild risk early warning state, a second moderate risk early warning state and a second severe risk early warning state; the third risk early warning value comprises a third mild risk early warning state, a third moderate risk early warning state and a third severe risk early warning state.

7. The risk assessment method according to claim 1,

the assessment area comprises at least one country or region;

8. The risk assessment method of claim 7, wherein the historical related data comprises a currency market data index, a bond market data index, a stock market data index, a derivative market data index and a foreign exchange market data index corresponding to the currency market, the bond market, the stock market, the derivative market and the foreign exchange market, respectively.

9. The risk assessment method according to claim 8, wherein the first data analysis repository comprises a plurality of single market data analysis repositories, each of which comprises any one of the money market data indicators, the bond market data indicators, the stock market data indicators, the derivatives market data indicators, the foreign exchange market data indicators in any one of the assessment areas;

10. A risk assessment device for use in a risk assessment method according to any one of claims 1 to 9;