RU2396598C1 - Automated information-analytical system of financial risk management - Google Patents

Abstract

FIELD: information technologies.

SUBSTANCE: system comprises facility for data gathering on financial obligations, facility for generation of financial obligations portfolio structure, facility for processing data on alternative management options, facility for generation of multidimensional presentation of data, facility for ranging of alternative management options and facility for data visualisation. And also serially connected facility for gathering and processing of historical data on risk factors and facility for generation of risk factors forecasting, facility for gathering data on basis rates, facility for discounting/ranging of time series, and facility for analysis of data on financial obligations.

EFFECT: improved efficiency of public debt portfolio management by reduction of risk level when controlling liabilities and assets.

13 cl, 1 dwg, 11 tbl

Description

The invention relates to computing tools designed to solve special problems, namely, to conduct predictive assessments of financial risks, modeling scenarios of managing financial instruments and choosing the best management solutions to reduce these risks. The invention can be used to manage the assets and liabilities of banking and other financial institutions, financial structures of public authorities, as well as in the implementation of financial activities to raise funds.

The well-known computer system for assessing investment risks Risk Metrics (Introduction to Risk Metrics, JPMorgan, New York, 1995, p. 3), which contains a data input module, a module for receiving data on certain assets, in particular financial instruments, as well as data on the main characteristics of financial instruments, operations on instruments, data on historical values of risk factors, a module for forming the portfolio structure, a module for storing data, a module for calculating statistical values, a module for calculating scenarios, a module for full analysis, a module full risk calculation and decision module.

The Risk Metrics system works as follows. Information on the composition and number of financial instruments that are in this investment portfolio, the specifications of all financial instruments (the main parameters — maturity date, dates and amounts of income payments, payment currency, etc.) are entered into the module for forming the portfolio structure. After that, the structure of this investment portfolio and the schedule of payments coming to be received by the investor are formed. The generated data is sent to the data storage module, where the data is supplemented with information about historical and current prices of financial instruments and historical values of risk factors, and then, in the module for calculating statistical values, the current value of the standard deviation for each risk factor and the correlation between the different risk factors are calculated .

The received data is sent to the scenario calculation module and simultaneously returned to the data storage module. In the calculation module, risk scenarios are calculated based on the calculated values of the statistical values. The results are sent to the full analysis module, in which the probable cash flows for this portfolio are calculated for a given portfolio. After that, the calculated data is sent to the module for the full calculation of risks, in which they evaluate the total risk of the investment portfolio in question. The risks assessed in monetary terms are transferred to the decision-making module, where, based on a comparison of risk data for other investment portfolios or a comparison of data on the limits (limit values) of possible risks, the user decides to perform operations on the market.

The disadvantages of the known system include the fact that it allows you to simulate scenarios of risk factor behavior for cases of assuming a normal distribution of random variables characterizing the behavior of factors, which allows you to calculate the risk of this portfolio of financial instruments and does not provide for the formation of various portfolio management options, i.e. the system can only be used to assess the market risk of the investor.

Closest to the claimed one is an automated information and analytical system for assessing financial risks according to the patent of the Russian Federation No. 2226134, IPC G06F 17/60, G07F 19/00, publ. 05/20/2004, containing the workplace of the operator-analyst connected via communication lines to the server, including a tool for generating a set of data on the current status of the portfolio and payments for portfolio tools, a tool for generating a set of options for managing a portfolio of financial instruments, a tool for creating a data bank and calculating statistical characteristics on the historical values of risk factors, a tool for forecasting risk factors, a tool for calculating matrices of debt indicators, a tool for calculating risks and medium reporting process.

The work of this system consists in the fact that data on operations with the investment portfolio are entered into the tool for generating a set of data on the current state of the portfolio and payments on portfolio instruments, many models of investment portfolio structure options are formed, a risk factor behavior matrix is formed, and cash flow matrices for servicing are calculated portfolio for a certain date, cash flows for repayment of a portfolio for a specific date, cash flows for raising funds for a specific date, form matrices of cash flows for servicing a portfolio of one option for a full period of risk calculation, cash flows for repaying a portfolio within one option for a full period of calculating risk and cash flows for attracting funds of one option for a full period of calculating risk, form a risk matrix and choose the best option for the structure investment portfolio.

The well-known system, like the previous one, is characterized by the possibility of using it only to assess the market risks of the investor and does not allow to form various options for managing a portfolio of financial instruments taking into account these risks, which significantly reduces the efficiency of using this system.

The invention solves the problem of increasing the efficiency of managing a portfolio of public debt by reducing the level of risk in managing liabilities and assets obtained by improving the technology for preparing management decisions based on a multifaceted and multivariate analysis of debt portfolio management strategies, as well as calculating various types and assessing risks of urban debt.

The technical result from the use of the claimed system, which coincides with the task, is achieved due to the fact that the automated information-analytical system of financial risk management, which contains sequentially included means for collecting data on financial obligations, means for forming the structure of the portfolio of financial obligations, means for processing data on alternative options controls, means of forming a multidimensional representation of data, means of ranking alternative var management controls and a data visualization tool, as well as sequentially included means for collecting and processing historical data on risk factors and a tool for generating forecasts of risk factors, the output of which is connected to the second input of the tool for forming the structure of a portfolio of financial obligations, is additionally equipped with a tool for collecting data on basic rates, output which is connected to the third input of the means of forming the structure of the portfolio of financial obligations, a means of discounting / aging time series the output of which is connected to the fourth input of the means for forming the structure of the portfolio of financial obligations, and the means for analyzing data on financial obligations, the inputs of which are connected to the output of the means for collecting data on financial obligations, the information inputs of the means for collecting and processing historical data on risk factors, the means for generating forecasts risk factors, means of collecting data on basic rates, means of collecting data on financial obligations and means of analyzing data on financial obligations you are connected to ODBC sources and / or external systems, the second output of the risk factor collection and processing tool is connected to the fifth input of the financial liability portfolio structure tool, and all system outputs except the financial liability data tool and structure tool a portfolio of financial obligations are connected to the corresponding inputs of the data visualization tool.

In addition, the automated system is additionally equipped with a template management tool, the input of which is connected to external systems, and the outputs are connected by two-way communication with the means of forming the portfolio of financial obligations, a means of forming a multidimensional presentation of data, a means of ranking alternative management options, a means of discounting / aging time series and data visualization tool.

Moreover, in the claimed automated system, the financial liabilities data collection tool for implementing the urban debt subsystem includes a data loading unit from external systems, an ODBC data loading unit and a manual data input unit connected in parallel.

In addition, in the claimed automated system, the base rate data collection tool for implementing the urban debt subsystem includes a manual data entry unit at the base rates and an ODBC base rate data download unit included in parallel.

In addition, in the claimed automated system, the financial liabilities data analysis tool for implementing the urban debt subsystem includes a data loading unit for debt indicators from external systems and a debt indicator calculation unit included in parallel, and a unit for calculating analytical indicators and summary analytical indicators, whose input is connected to the outputs of the first two blocks.

In addition, in the inventive automated system, a means of collecting and processing historical data on risk factors, designed to implement a subsystem of risk factors, comprises a data loading unit from ODBC and a manual data input unit included in parallel, and a historical data analysis unit based on calculation of statistical characteristics, whose input is connected to the outputs of the first two blocks.

In addition, in the claimed automated system, the tool for generating forecasts of risk factors designed to implement the subsystem of risk factors includes a parallel data loading unit from ODBC and a manual data input unit, an automated forecasting unit based on historical data, the input of which is connected to the outputs of the first two blocks, and the output is to the first input of the forecast conversion and conjugation block, the second input of which is connected to the output of the manual data input block.

In addition, in the claimed automated system, the means of discounting / aging time series intended for the implementation of the subsystem of debt portfolios contains sequentially included a unit for generating parameters for discounting / aging a portfolio and a unit for generating discounting / aging functions.

In addition, in the claimed automated system, the means of forming the structure of the portfolio of financial obligations, designed to implement the subsystem of debt portfolios, contains sequentially included means of determining the parameters of the portfolio and a unit for generating a passive option for managing financial obligations, the outputs of which are connected to the inputs of the unit for forming alternative options for managing financial obligations, including a unit for the formation of transactions with financial obligations and additionally equipped with a unit for the formation of transactions for the exchange of financial obligations and cash flows included in parallel.

In addition, in the claimed automated system, the data processing tool for alternative management options, designed to implement the debt portfolio subsystem, contains a unit for calculating debt indicators and a unit for calculating analytical indicators and summary analytical indicators, and is additionally equipped with a unit for generating recommendations for creating alternative management options ( solution search), the inputs of which are connected to the outputs of the first two blocks, and the output is connected to the sixth input An ode to the formation of a portfolio of financial liabilities.

In addition, in the inventive automated system, a means for generating a multidimensional data presentation intended for implementing a subsystem of debt portfolios comprises sequentially included unit for creating custom data representations and a unit for loading debt indicators into a multidimensional data representation.

In addition, in the claimed automated system, the ranking tool for alternative management options, designed to implement the subsystem of debt portfolios, contains sequentially included unit for generating ranking tables (risk tables) and a unit for calculating the values of ranking tables.

In addition, in the inventive automated system, the data visualization tool designed to implement the debt portfolio subsystem includes parallel tabular data presentation unit and graphical data presentation unit, the outputs of which are connected to the inputs of the export device in the exchange format, and the second output of the table data presentation unit is connected with the input of the reporting forms block.

The essence of the invention consists in the implementation of the principle of analysis and comparison of a set of analytical indicators characterizing the borrowing risks of the analyzed entity, for various options for managing the debt portfolio under possible scenarios for the development of market conditions both in the short and long term. Moreover, the claimed system provides the functions of analysis and ranking of management options associated with a subset of the respective scenarios for the development of risk factors.

The invention is illustrated by the drawing, which shows a General block diagram of a computer system for a preferred embodiment of the claimed invention.

The automated information and analytical financial risk management system of the present invention can be implemented on the basis of various computer systems, however, it is preferable to use a system in the form of a known client / server computer network (not shown in the drawing), including a server connected to workstations (client computers) analytic operators and data sources using various types of communication lines, such as fiber optic, telephone, radio, satellite and other them.

At the same time, a standard IBM-compatible personal computer or laptop computer using the well-known Microsoft Windows operating system or its other equivalent can be used as the operator’s analytic workstation. The workplace of an analytic operator, as a rule, contains a data input device (keyboard, mouse), a data reception device (memory on hard disks, a CD-ROM drive or a floppy disk drive), intermediate data storage and processing devices, made in the form RAM of the client computer, microprocessor, as well as data output devices (eg, display, printer).

As information arrays of initial data processed using the declared automated system, for example, data on certain assets, in particular financial instruments, such as bonds, loans, guarantees, denominated in rubles and other currencies, and also data on the main characteristics of financial instruments, such as the amount of debt (for bonds - the number of bonds traded on the market, for loans and sureties - the amount of outstanding debt on the instrument) , operations on instruments (on bonds - dates and amounts of coupon payments, on loans - dates and sizes of interest payments), etc., and / or data on historical values of risk factors. Risk factor is understood as a parameter, on which the characteristics of cash flows arising in the process of borrowing and servicing debt depend on the instruments included in the portfolio, while the values of this parameter are not defined in the future, but are predicted with a certain probability. For example, the risk factor for bonds denominated in dollars may be the ruble to dollar exchange rate, since the maturity of the bond in rubles depends on it.

The automated financial risk management system, as claimed in accordance with the present invention, shown in the block diagram, is installed on a computer network server and contains sequentially included financial liabilities data collection means 1, financial liabilities portfolio structure structure 2, alternative data processing means 3 control options, means 4 for forming a multidimensional data representation, means 5 for ranking alternative control options eniya and data visualization means 6 and means 7 sequentially included collection and processing of historical data on risk factors and means 8 forming projections risk factors, whose output is connected to the second input means 2 forming the structure portfolio of financial obligations.

In addition, the system is additionally equipped with a means 9 of collecting data on basic rates, the output of which is connected to the third input of the means 2 of forming the structure of the portfolio of financial obligations, means 10 of discounting / aging time series, the output of which is connected to the fourth input of the means of 2 forming the structure of the portfolio of financial obligations, and means 11 for analyzing data on financial obligations, the inputs of which are connected to the output of means 1 for collecting data on financial obligations, moreover, information inputs means 7 for collecting and processing historical data on risk factors, means 8 for generating forecasts of risk factors, means 9 for collecting data on base rates, means 1 for collecting data on financial liabilities, and means 11 for analyzing data on financial liabilities connected to 12 ODBC sources (databases with open access) and / or external systems 13, the second output of the means 7 for collecting and processing historical data on risk factors is connected to the fifth input of the means 2 for forming the structure of the portfolio of financial obligations, and the outputs are all x means system 1 except for the means for collecting data on financial obligations and means 2 forming the structure portfolio of financial obligations connected to respective inputs of data visualization means 6.

In addition, in a preferred embodiment, the claimed automated system is additionally equipped with template management means 14, the input of which is connected to external systems 13, and the outputs are connected by two-way communication with means 2 of forming the structure of the portfolio of financial obligations, means 4 of forming multi-dimensional data presentation, means 5 of ranking alternative control options, means 10 discounting / agi time series and means 6 data visualization.

The claimed automated financial risk management system is a hardware-software complex that functionally consists of three main subsystems:

1) subsystem of urban debt,

2) subsystem of risk factors,

3) the subsystem of debt portfolios.

The subsystem "Urban debt" is designed to view historical data on the portfolio of urban debt. Historical data on debt instruments in retrospect are implemented in an automated way using hardware and software and provide export / import of data on debt instruments with daily updating of data between accounting and analytical systems. The urban debt subsystem includes a means 1 of collecting data on financial obligations, means 9 of collecting data on basic rates and means 11 of the analysis of data on financial obligations.

In turn, the financial liabilities data collection means 1 comprises a data download unit 15 from external systems, an ODBC data download unit 16, and a manual data input unit 17 connected in parallel. In this case, the manual data input unit 17 is programmatically connected to the workplace of the operator-analyst (not shown in the block diagram), in particular, to its data input device (for example, keyboard, mouse).

The base rate data collection means 9 comprises a base rate manual input unit 18 and an ODBC base rate data loading unit 19 connected in parallel. At the same time, the block 18 of manual data entry at basic rates is programmatically connected to the data entry device of the analyst’s workstation.

At the input of this software, a set of data on the base rates (name, description, currency) and a data set on the sequence of base rates (identifier of the base rate for which the sequence is formed, the base date, date, value corresponding to the date) is generated. At its output, a set of rates for various dates is formed, which is then used in calculating debt and analytical indicators for a portfolio of financial instruments.

The financial liabilities data analysis tool 11 comprises a data loading unit 20 for debt indicators from external systems and a debt indicator calculation unit 21 included in parallel, as well as an analytical indicator and summary analytical indicator (AAP) calculation unit 22, the input of which is connected to the outputs of the first two blocks.

Another significant part of the declared automated financial risk management system is the subsystem "Risk Factors", designed to generate risk scenarios based on historical data on risk factors using historical modeling methods (extrapolation) and Monte Carlo (simulation), as well as on the basis of expert ratings (scenario modeling). Risk factors include: ruble, dollar, euro, interest rates, inflation, refinancing rate, budget restrictions, interbank rates and their relationship to each other. As already noted, a risk factor is understood as a system parameter representing a random variable depending on time, which can affect the outcome of managing a debt portfolio. The values of the risk factor are not defined in the future, but can be predicted based on available historical data or expert estimates. The subsystem of risk factors includes a means 7 for collecting and processing historical data on risk factors and a means 8 for generating forecasts of risk factors.

The tool 7 for collecting and processing historical data on risk factors includes a block 23 for downloading data from ODBC and a block 24 for manual data input, connected in parallel, as well as a block 25 for analyzing historical data based on the calculation of statistical characteristics, the input of which is connected to the outputs of the first two blocks. In this case, the block 24 of the manual input of historical data is programmatically connected with the data input device of the workplace of the operator-analyst.

The means for generating forecasts of risk factors 8 contains, in parallel, an ODBC data loading unit 26 and a manual data input unit 27, as well as an automated forecast generation unit 28 based on historical data, the input of which is connected to the outputs of the first two blocks, and the output to the first input of the block 29 conversion and conjugation of forecasts, the second input of which is connected to the output of block 27 of the manual data input. In this case, the manual data input unit 27 is programmatically connected to the data entry device of the analytic operator workstation.

The Debt Portfolios subsystem is designed to form an active debt portfolio from debt instruments in accordance with the assigned analytical tasks, import data into an active debt portfolio from external systems, create and analyze debt portfolio management options, set and edit the laws of changes in discount capital. The use of the statistical series method in the claimed system allows linking the indicators of the debt portfolio instruments and risk factors into a single complex, which enables the user to carry out an in-depth analysis of various options for managing the debt portfolio and make the right decision. Analysis of management options is carried out on the basis of analysis and comparison of a set of analytical and debt indicators characterizing the borrowing risks of the analyzed entity for various options for managing the debt portfolio under possible scenarios for the development of market conditions both in the short and long term.

Based on the forecasts made for the considered portfolio management options, the probability distributions of the debt indicators are calculated and the risk / cost ratios for the portfolio management options are compared, and the legislative budget constraints are checked. When developing the system, OLAP technology (operational analytical data processing) was used. Thus, the calculated debt indicators (time series) are presented in the system in the form of data cubes. The automated system provides the ability to analyze a multidimensional data cube under various “slices”. Due to this structure, the analysis of calculated debt indicators can be carried out at various levels of aggregation.

In addition, the system allows the construction of arbitrary time series as functions of time series and risk factors stored in the database. To do this, create a formula data cube.

As a result of the work of the software block for generating data cubes, the following data cubes are displayed in the system:

The Main Cubes group: debt indicators cube, debt indicators cube in the currency of the instrument, risk probability non-occurrence cube, debt indicators cube for debt portfolio management options.

Group “Discounting”: cube of discount capital, cube of discounted debt indicators in rubles / in the currency of the instrument, cube of the present value of debt indicators in rubles / in the currency of the instrument.

A group of cubes of risk factor values determined by portfolio.

Group “Percentage Relations”: cubes of percentage relations to the total volume / to the total volume for the year.

Group “Formula cubes”: cube “MO debt indicators”, cube “SKO debt indicators”, as well as formula cubes created by users.

Group “Constraints”: cube “Principal debt <0”.

Using debt indicators calculated on the basis of the created debt portfolio, introduced management options and the constructed forecasts of risk factors, the system constructs risk matrices - risk tables. In these tables, for a predefined debt indicator and many management options, quantitative characteristics of borrowing risks are presented, for example, the standard deviation of the debt indicators.

Based on risk tables, management options are ranked according to various criteria: according to risk, according to the implementation of a quantitative restriction on debt parameters.

The debt portfolio subsystem includes a means 2 of forming the structure of a portfolio of financial obligations, a means 3 of processing data on alternative management options, a means 4 of forming a multidimensional presentation of data, a means 5 of ranking alternative management options, a means of visualizing data 6, and a temporary discounting / averaging tool 10 rows and template management tool 14.

Means 2 of forming the structure of the portfolio of financial obligations contains sequentially included block 30 for determining the parameters of the portfolio and block 31 for forming a passive option for managing financial obligations, the outputs of which are connected to the inputs of block 32 for forming alternative options for managing financial obligations, which includes block 33 for generating transactions with financial obligations additionally equipped, unlike the prototype, with a block 34 for forming financial exchange transactions lstvami and cash flow, connected in parallel.

The current state of the urban debt portfolio is characterized by a set of financial instruments and operations on them. The system implements three main ways of forming the current state of urban debt.

Import of the current state of city debt from the accounting systems of the Government Debt Management: PC “State urban short-term bonds GGO”, PC “Medium-term Government bonds“ SGO ”, PC“ Medium-term registered bonds “SIO”, PC “Keeping a book of debt” and additional sources ( ODBC format). The system implements the ability to import into four main types of financial instruments: bonds, loans, guarantees and loans. The formation of a set of each of the listed types of financial instruments in the import process is carried out on the basis of a fixed set of input data. For example, the formation of a set of bonds is carried out on the basis of the following input data set: identification code, name, par value, placement date, maturity date, issue volume, type of circulation. The formation of a set of operations on bonds is carried out on the basis of the following input data set: instrument identification code, date of transaction, number of bonds on sale transactions, amount on sale transactions, number of bonds on purchase transactions, amount on purchase transactions, coupon redeemed, current interest rate, VAT , coupon debt price, revolutions (in rubles), revolutions (in pcs.).

Import current state of urban debt from ODBC sources. The formation of a set of instruments is carried out on the basis of the following input data set: instrument identifier, instrument name, description of instrument, type code of a financial instrument circulation, face value, currency code, date of conclusion of an agreement (placement), date of closing of an agreement (repayment), volume of agreement (issue) ) Formation of a set of operations for imported instruments is carried out on the basis of the following input data set: identifier of the instrument under operation, operation type code, transaction price (transaction volume, interest rate), number of instrument units, and date of operation.

Manual data entry on financial instruments and operations with them. The formation of a set of instruments manually is carried out on the basis of the following input data set: instrument name, description of instrument, type of circulation of a financial instrument, face value, currency, date of conclusion of an agreement (placement), date of closing of an agreement (repayment), volume of agreement (issue), binding to days of the week, circulation period (in days), value of the fiscal year. Manually creating a set of operations for instruments is carried out on the basis of the following input data set: instrument name, type of operation, transaction price (transaction volume, interest rate), number of instrument units, date of operation.

At the output of this program module, two output data sets are formed: data on the current state of financial instruments of the city debt and data on the current state of payments on financial instruments of the city debt (data on operations). The first set includes: display name, description, object name, download source (name of the information system from which the historical instrument was imported), date of conclusion of the contract (placement), date of closing of the contract (redemption), currency of the instrument, type of instrument, day mask weeks, volume of issue. The second set includes: the name of the instrument, type of transaction, transaction price (transaction volume, interest rate), the number of units of the instrument, date of operation.

The tool 3 for processing data on alternative management options contains sequentially a unit 35 for calculating debt indicators and a unit 36 for calculating analytical indicators and summary analytical indicators (SAP) and is additionally equipped with a block 37 for generating recommendations for creating alternative management options (finding a solution), the inputs of which are connected to the outputs of the first two blocks, and the output is connected to the sixth input of the means 2 of forming the structure of the portfolio of financial obligations.

The means 4 for generating a multidimensional data representation comprises sequentially included block 38 for creating custom data representations in the form of formula cubes and a block 39 for loading debt indicators into a multidimensional data representation.

The alternative control options (WU) ranking tool 5 comprises sequentially included block 40 for generating ranking tables (risk tables) and a block 41 for calculating the values of the ranking tables.

The data visualization means 6 comprises a parallel table data presentation unit 42 and a data graphical presentation unit 43, the outputs of which are connected to the inputs of the export device 44 in the exchange format, and the second output of the table data presentation unit 42 is connected to the input of the reporting form unit 45.

The tool 10 discount / agitation time series contains sequentially included unit 46 for forming the parameters of discount / agi portfolio and block 47 for the formation of discount / agitating functions.

The template management means 14, the input of which is connected to external systems 13, is connected by two-way communication with the means 2 of forming the structure of the portfolio of financial obligations, the means 4 of forming a multidimensional data presentation, the means 5 of ranking alternative management options, the means 10 for discounting / ajing time series and means 6 data visualization. The template management tool 14 is intended for generating a plurality of templates, in particular a template for a portfolio of financial obligations, a template for discounting / aging time series, a template for a reporting form, and others. For example, each reporting form is described by certain properties that can be applied to other reporting forms. Saving a template allows you to subsequently select a template and overlay it on the generated report so that the properties of the saved template can be applied to the current report.

All of the above blocks of the claimed system are implemented using hardware and software.

The system operates as follows. Using the financial liabilities data collection tool 1, data on financial liabilities is downloaded by importing from external systems (block 15), ODBC (block 16) or using manual data water (block 17). Data from the output of funds 1 is fed to the information inputs of the financial analysis data analysis tool 11, in which, using block 20, the data on debt indicators (DP) are downloaded and analyzed using block 21, the debt indicators are calculated, and the data from their outputs at the input of block 22, where analytical indicators and summary analytical indicators (SAP) are calculated. In addition, data from the output of funds 1 is also submitted to the first input of funds 2 of the formation of the structure of the portfolio of financial obligations.

In the tool 9 for collecting data on base rates, information about base rates is entered into the system for use in subsequent calculations. So, information on base rates can be entered into the system by manual input (block 18) and / or by downloading from ODBC sources (block 19), after which the information is transmitted to the third input of the means 2 of forming the structure of the portfolio of financial obligations to determine portfolio parameters (block 30) and for the formation of a passive option for managing financial obligations (block 31). Data from the output of funds 9 is fed to the third input of funds 2 of the formation of the structure of the portfolio of financial obligations.

In the tool 7 for collecting and processing historical data on risk factors, the historical data for the selected risk factors are downloaded by manually entering data (block 24) and / or by downloading from ODBC sources (block 23), after which historical data is analyzed in block 25 based on the calculation of statistical characteristics. At the next stage, the results of the analysis of historical data are transferred to the fifth input of the means 2 of forming the structure of the portfolio of financial obligations for the formation of alternative options for managing financial obligations, which is performed in block 32. Also, the data from the means 7 for collecting and processing historical data on risk factors are transferred to the means 8 for generating forecasts of risk factors for the automated generation of forecasts based on historical data (in block 28) and, if necessary, transformation and forecast forecasts using block 29. Data from the output of funds 8 is fed to the second input of the funds 2 of the formation of the structure of the portfolio of financial obligations.

In the tool 10 discount / agitation of time series, the operator generates the parameters of discount / agitation of the portfolio (using block 46) and determines the discount / aging functions (in block 47). Data from the output of funds 10 is fed to the fourth input of the means 2 of forming the structure of the portfolio of financial obligations for the formation in block 33 of transactions with financial obligations and in block 34 of the exchange of financial obligations and cash flows.

The results of data processing from the output of means 2 of forming the structure of the portfolio of financial obligations are input to means 3 of processing data on alternative management options, where using block 35 for calculating debt indicators and block 36 for calculating analytical indicators and SAP in block 37, recommendations are generated for creating alternative management options VU, i.e. the search for the optimal solution. Data from the output of block 37 is sent to the sixth input of the means 2 of forming the structure of the portfolio of financial obligations.

In addition, the data from the output of the means 3 is fed to the input of the means 4 for forming a multidimensional data representation, in which using block 38 you create custom data representations, the so-called formula cubes, and then using block 39 you load the values of debt indicators (DP) into multidimensional presentation of data.

The results of processing the data from the output of the means 4 are fed to the input of the ranking means 5 of alternative control options, where using the block 40 ranking tables (risk tables) are formed, and in the block 41 the values of the ranking tables are calculated.

The result of the calculation stage is a list of ranked options for managing a portfolio of financial assets, which is stored in the server’s RAM, and is also transferred from the output of funds 5 to the reporting unit 45, along with data on management options from other blocks of the system. Based on the list of ranked management options, a report is generated, focusing on which the user decides on the implementation of the management option. The best option for managing a borrower’s portfolio is the one that takes the first place in the ranked list of management options created by the system. The remaining reports generated in block 45 are auxiliary.

After processing, the data from the outputs of all funds of the claimed system, with the exception of funds 1 for collecting data on financial obligations and funds 2 for forming the structure of the portfolio of financial obligations, go to the corresponding inputs of data visualization means 6, where depending on the visualization method chosen by the operator, reports are generated for various control options portfolio under various slices based on data obtained at various stages of the system and automatically sent to environments GUT 6 after each stage of the system. Means 6 of data visualization allows the conversion of incoming information (data) into a graphical or tabular presentation (reporting) using blocks 42, 43, 45, respectively, outputting received reports to a monitor screen, printer or other output device, and can also export processed data to exchange formats (in block 44).

The system operator is provided with a wide range of options for choosing the method of construction and the set of data used to build reports.

To reflect the results of the system, all available and known means of display can be used. These may include tables, graphs, light and sound signals, software, help, service or other tips, displaying processes in the dynamics of their course on the monitor, etc.

Based on the reports received at the reporting stage, the operator of the automated system selects a management option that is the most appropriate for the restrictions imposed and has the lowest risk values. To make a decision, the system operator uses the following information generated in the form of reporting forms: characteristics calculated for the risk calculation stage for each of the management options and the result of ranking portfolio management options.

As a result of the above actions, the system operator selects the best portfolio management option. After that, data on the selected management option is entered into the portfolio structure subsystem, that is, the initial portfolio structure is changed.

The following example illustrates the process and results of the automated information-analytical system "RISK-1" that implements the present invention.

The options for managing the borrower's portfolio as of December 1, 2008 are considered.

Section I. The borrower's portfolio as of December 1, 2008 consists of the following instruments:

1. Warranty A

Non-Regular Warranty

Volume of guarantee provided: 10,000,000 euros

Currency: Euro

Warranty Date: 01/01/02

Repayment Date: 08/15/10

The instrument has a repayment schedule in installments: 4 equal payments each year.

2. Warranty B

Non-Regular Warranty

Volume of guarantee provided: EUR 1,200,000

Currency: Euro

Warranty Date: 01/01/02

Repayment Date: 08/15/10

The instrument has a repayment schedule in installments: 4 equal payments each year.

3. Bond A

Fixed coupon market discount coupon bond

Currency: Russian Rubles

Denomination: 100 rubles

Post date: 09/11/02

Repayment Date: 09/02/09

Payment dates and coupon rates, starting from 12/01/2008:

Date of payment Interest rate,% of face value 03/05/2008 5.73 09/03/2008 5.48 03/04/2009 5.24 09/02/2009 4.99

4. Bond B

Fixed coupon market discount coupon bond

Currency: Russian Rubles

Denomination: 100 rubles

Post date: 10/09/02

Repayment Date: 09/28/11

Payment dates and coupon rates, starting from 12/01/08:

Date of payment Interest rate,% of face value 04/01/2009 4.74 09/30/2009 4.74 03/31/2010 4.49 Date of payment Interest rate,% of face value 09/29/2010 4.49 03/30/2011 4.24 09/28/2011 4.24

5. Bond C

Fixed coupon market discount coupon bond

Currency: Russian Rubles

Denomination: 100 rubles

Post date: 06/18/03

Repayment Date: 07/14/10

Payment dates and coupon rates, starting from 12/01/08:

Date of payment Interest rate,% of face value 01/14/2009 4.49 07/15/2009 4.49 01/13/2010 4.49 07/14/2010 4.49

6. Bond D

Fixed coupon market discount coupon bond with debt amortization

Currency: Russian Rubles

Denomination: 100 rubles

Post date: 06/18/03

Repayment Date: 07/14/10

Payment dates and coupon rates, starting from 12/01/08:

Date of payment Interest rate,% of face value 01/14/2009 4.49 07/15/2009 4.49 01/13/2010 4.49 07/14/2010 4.49

Dates and rates of debt amortization:

Date of payment Rate,% of face value 02/06/2013 25.0 08/07/2013 25.0 02/05/2014 25.0 08/06/2014 25.0

6. Bond E

Early coupon market discount coupon bond with a fixed coupon

Currency: Russian Rubles

Denomination: 100 rubles

Post date: 04/27/05

Repayment Date: 04/20/11

Payment dates and coupon rates, starting from 12/01/08:

Date of payment Interest rate,% of face value 04/22/2009 4.49 10.21.2009 4.49 04/21/2010 4.49 10/20/2010 4.49

The nominal debt volume on the borrower's portfolio as of December 1, 2008 is:

Tool name Debt volume in instrument currency Instruments with Euro currency, including 6715 182.24 Warranty A 3 352 399.60 Warranty B 3 362 782.64 Instruments with currency Ruble of the Russian Federation, including 91 705 200.00 Bond A 27 098 900.00 Bond B 43,472,700.00 Bond C 15510200.00 Bond D 4 260 300.00 Bond E 1 363 100.00

Section II. The borrower needs to create an active portfolio management option and evaluate the possibility of the debt portfolio going beyond the established limits.

The active management option includes the following tools and operations in 2009-2011:

1. Short-term loans

Currency: Russian Rubles

Amount of attraction: 1,000,000,000 rubles

Loan term: 3 months

Loan rate and frequency of coupon payment: on the maturity date at a rate corresponding to the inflation rate plus a margin of 1.5% per annum.

Fundraising is carried out once a year no later than June 1 of the corresponding year.

2. Secondary market transactions and repurchase agreements

Currency: Russian Rubles

Amount of attraction: 15000000000 rubles

Operation period: within each year, no more than 12 months

Loan rate and frequency of coupon payment: at maturity at a rate of 2.0% per annum

3. Bonds

Fixed coupon market discount coupon bond

Amount of placement: 100 rubles

The placement price: 95 rubles in 2009, 97.2 rubles in 2010, 99.9 rubles in 2011.

Currency: Russian Rubles

Denomination: 100 rubles

Date of placement: no later than 01.06 of the corresponding year

Maturity date: after 6 years from the date of placement.

Frequency of payments and coupon rates: twice a year at a rate corresponding to the inflation rate plus 0.5% per annum.

Partial repayment schedule: four equal annual installments. The last payment is due at maturity.

Established restrictions:

1) Operating flow> 0

2) Debt servicing costs / Current income of own budget <7.5

3) Debt expenses / Current income of own budget <17.5

4) Net current balance> 0

5) Nominal debt / Current income of own budget <= 20

6) Nominal external debt / Nominal debt <= 50

7) Nominal conditional debt / Nominal debt <= 15

8) Nominal debt / Own budget revenues <= 50

9) Nominal debt <= Marginal debt at the end of the year

Section III. Requirements for the active management option: The balance of attraction for the portfolio should correspond to:

- 7,000,000,000 rubles in 2009

- 1.5 billion rubles in 2010

- 8,000,000,000 rubles in 2011

Section IV. System operation:

The system requests data on the initial state of the portfolio of financial obligations. At the same time, databases are manually entered in block 17 for manual data entry, from external data sources in block 15 for loading data from external systems, from ODBC in block 16 for loading data from ODBC, the following data on the current state of the portfolio and payments on portfolio obligations :

Under Warranty A:

Date of operation Type of transaction Volume 02/15/2009 Issuance of a guarantee (scheduled operation) 375851.00 05/15/2009 Issuance of a guarantee (scheduled operation) 362 974.00 08/15/2009 Issuance of a guarantee (scheduled operation) 353,709.00 11/15/2009 Issuance of a guarantee (scheduled operation) 342 638.00 02/15/2010 Issuance of a guarantee (scheduled operation) 331 568.00 Date of operation Type of transaction Volume 05/15/2010 Issuance of a guarantee (scheduled operation) 320518.00 08/15/2010 Redemption (scheduled operation) 08/15/2010 Issuance of a guarantee (scheduled operation) 319244.23

Warranty B:

Date of operation Type of transaction Volume 02/15/2009 Issuance of a guarantee (scheduled operation) 389 693.00 05/15/2009 Issuance of a guarantee (scheduled operation) 378791.00 08/15/2009 Issuance of a guarantee (scheduled operation) 370 946.00 11/15/2009 Issuance of a guarantee (scheduled operation) 364 129.00 02/15/2010 Issuance of a guarantee (scheduled operation) 353 903.00 05/15/2010 Issuance of a guarantee (scheduled operation) 343 343.00 08/15/2010 Redemption (scheduled operation) 08/15/2010 Issuance of a guarantee (scheduled operation) 341 723.29

On Bond A:

Date of operation Type of transaction Price Interest rate 03/04/2009 Interest payment (scheduled operation) 5.24 09/02/2009 Interest payment (scheduled operation) 4.99 09/02/2009 Redemption (scheduled operation) 100.00

By Bond:

Date of operation Type of transaction Price Interest rate 04/01/2009 Interest payment (scheduled operation) 4.74 09/30/2009 Interest payment (scheduled operation) 4.74 Date of operation Type of transaction Price Interest rate 03/31/2010 Interest payment (scheduled operation) 4.49 09/29/2010 Interest payment (scheduled operation) 4.49 03/30/2011 Interest payment (scheduled operation) 4.24 09/28/2011 Interest payment (scheduled operation) 4.24 09/28/2011 Redemption (scheduled operation) 100.00

For Bonds C:

Date of operation Type of transaction Price Interest rate 01/14/2009 Interest payment (scheduled operation) 4.49 07/15/2009 Interest payment (scheduled operation) 4.49 01/13/2010 Interest payment (scheduled operation) 4.49 07/14/2010 Interest payment (scheduled operation) 4.49 07/14/2010 Redemption (scheduled operation) 100.00

For Bonds D:

Date of operation Type of transaction Price Interest rate 02/11/2009 Interest payment (scheduled operation) 4.99 08/12/2009 Interest payment (scheduled operation) 4.99 02/10/2010 Interest payment (scheduled operation) 4.74 08/11/2010 Interest payment (scheduled operation) 4.74 02/09/2011 Interest payment (scheduled operation) 4.49 08/10/2011 Interest payment (scheduled operation) 4.49 02/08/2012 Interest payment (scheduled operation) 4.24 08/08/2012 Interest payment (scheduled operation) 4.24 Date of operation Type of transaction Price Interest rate 02/06/2013 Interest payment (scheduled operation) 3.99 02/06/2013 Depreciation (planned operation) 25.00 08/07/2013 Interest payment (scheduled operation) 3.99 08/07/2013 Depreciation (planned operation) 25.00 02/05/2014 Interest payment (scheduled operation) 3.74 02/05/2014 Depreciation (planned operation) 25.00 08/06/2014 Interest payment (scheduled operation) 3.74 08/06/2014 Depreciation (planned operation) 25.00 08/06/2014 Redemption (scheduled operation) 100.00

For Bonds E:

Date of operation Type of transaction Price Interest rate 04/22/2009 Interest payment (scheduled operation) 4.49 10.21.2009 Interest payment (scheduled operation) 4.49 04/21/2010 Interest payment (scheduled operation) 4.49 10/20/2010 Interest payment (scheduled operation) 4.49 04/20/2011 Interest payment (scheduled operation) 4.49 04/20/2011 Redemption (scheduled operation) 100.00

These data are downloaded to the financial analysis data analysis tool 11. From external systems, block 20 contains data on debt indicators calculated before the date of the working session, on loaded financial obligations. Based on data on the current state of the portfolio and payments on portfolio obligations in block 21, debt indicators for future dates are calculated. In block 22, the calculation of analytical indicators and summary analytical indicators taking into account data on upcoming payments for financial obligations is carried out.

Further, the action of the system continues using means 2 of the formation of the structure of the portfolio of financial obligations. In the means 30 for determining portfolio parameters, the following parameters are set:

- the period of calculating debt indicators and the start date of portfolio management,

- used historical instruments,

- from the list of debt indicators, a list of calculated debt indicators is selected,

- forecasts and historical data on risk factors.

Based on the information specified in the portfolio parameter determination tool 30, a control option is created in block 31 for generating a passive option for managing financial obligations, including financial obligations from the list of used historical instruments. For these financial liabilities, the calculated debt indicators are downloaded from the start date of calculating the debt indicators to the start date of the portfolio management, operations with dates after the start date of the portfolio management, as well as historical and forecast values of risk factors at the dates of operations on obligations.

Historical and forecast values of risk factors for the dates of operations on obligations come from the means 7 for collecting and processing historical data on risk factors for the fifth input of the means 2 for forming the portfolio of financial obligations and from the means 8 for generating forecasts of risk factors for the second input for funds 2, respectively.

In block 32 of the formation of alternative options for managing financial obligations, an active option for managing the borrower's portfolio is created, which is described in Section P.

In addition to ordinary operations, the system provides the ability to create transactions for the exchange of obligations and flows of obligations. For example, bond E provides a fixed rate on interest payments. Using block 34 of the formation of transactions for the exchange of financial obligations and cash flows in the system, you can create a group of operations that will provide for the payment of interest on bond E at a floating rate instead of a fixed number of bonds specified by the properties of bond E and operations with it in the management option. The system calculates the delta, defined as the gain or loss from such a cash flow transaction. If the fixed rate is lower than the interest payment floating on the transaction date, the borrower will incur losses and the delta will be negative; if the fixed rate is higher than the interest payment floating on the transaction date, the borrower will benefit and the delta will be positive.

Along with passing through the stages of forming the portfolio structure and portfolio management options, a stage of calculating statistical characteristics is carried out, which is implemented in the means 7 for collecting and processing historical data on risk factors. From external ODBC data sources in block 23 for loading data from ODBC and by manually entering data in block 24 for manual data entry, historical data on the mentioned risk factors are imported:

1. Euro.

2. Inflation.

3. Current own budget revenues.

4. Current own budget expenditures.

5. Capital own budget revenues.

6. Capital own budget expenditures.

7. The maximum amount of debt at the end of the year.

In block 25 of the analysis of historical data based on the calculation of statistical characteristics is carried out:

- calculation of the mathematical average, which is performed by the formula:

- математическое среднее по фактору риска,Where

- mathematical average of the risk factor,

x _i - i-th historical observation of the risk factor,

n is the number of observations in the historical series of the risk factor.

According to the risk factor "US dollar", the mathematical average is 27.98;

- calculation of standard deviation (RMS) - according to the formula:

where δ _x is the standard deviation of the risk factor,

- математическое среднее по фактору риска,

- mathematical average of the risk factor,

x _i - i-th historical observation of the risk factor,

n is the number of observations in the historical series of the risk factor.

The standard deviation for the risk factor "US dollar" is 2.40;

- calculation of correlation characteristics (correlation of two risk factors, auto-covariance). The formula for the correlation of two risk factors:

where corr (x, y) is the correlation coefficient of two risk factors,

δ _x , δ _y - standard deviations for risk factors,

,

- математические средние по факторам риска,

,

- mathematical averages for risk factors,

x _i , y _i - i-th historical observations on risk factors,

n is the number of observations in the historical series of the risk factor,

- other.

δ_х, δ_y, соrr(х, y) направляются в средство 8 формирования прогнозов факторов риска для продолжения функционирования системы, где для каждого вектора на основе исторических данных и статистических характеристик производится расчет значений факторов риска, на основе которых формируются матрицы-прогнозы факторов риска.Then calculated statistical characteristics

δ _x , δ _y , corr (x, y) are sent to the means 8 for generating forecasts of risk factors to continue the functioning of the system, where for each vector, based on historical data and statistical characteristics, the values of risk factors are calculated, based on which matrix-forecasts of factors are formed risk.

Short-term forecasts are based on historical data in block 28 of the automated formation of forecasts based on historical data. Long-term forecasts are formed on the basis of expert opinions downloaded manually in block 27 for manual data entry and from external systems in block 26 for loading data from ODBC.

Based on the risk factor “US Dollar”, a short-term forecast is based on historical data, as well as a long-term forecast based on expert opinion. An example of the numerical values of the expert forecast for the risk factor "US dollar" is presented in the table:

Name Trajectory probability 12/19/2008 12/31/2009 12/31/2010 12/31/2011 TP1 0.20 27.61 31.81 33.00 34.00 TP2 0.60 27.61 31.31 32.50 33.50 TP3 0.20 27.61 30.81 32.00 33.00

An example of the numerical values of the short-term forecast for the risk factor "US dollar" is presented in the table:

Name Trajectory probability 12/19/2008 01/08/2009 01/28/2009 02/08/2009 TP1 0.20 27.61 28.64 29.40 04/30 TP2 0.20 27.61 27.17 12/27 27.18 TP3 0.60 27.61 27.91 28.26 28.61

Further, short-term and long-term forecasts are mated.

Pairing forecasts is carried out in block 29 conversion and pairing forecasts.

Pairing matrix-forecasts of risk factors is carried out by one of the following methods:

- Embedding a short tree in the initial step;

- Embedding with projection;

- Embedding with branching;

- Extrapolation / Interpolation by Black-Scholes method.

As an example, we can cite the results of calculation using the conjugation of the US Dollar risk factor forecast matrix by embedding this matrix in the first layer of a long tree (US Dollar risk factor forecast matrix constructed on the basis of expert estimates).

An example of the numerical values of the conjugate forecast for the risk factor “US dollar” is presented in the table:

Name Trajectory probability 12/19/2008 02/08/2009 12/31/2009 12/31/2010 12/31/2011 TP1 0.50 27.61 29.33 31.81 33.00 34.00 TP2 0.50 27.61 27.18 30.81 32.00 33.00

The algorithm laid down in the method of embedding a short tree in the first layer of a long one provides for combining several scenarios of the matrix of behavior of risk factors into one. This procedure depends on the choice of intervals for partitioning the risk factor values on the terminal layer of the short tree (on the last date of the risk factor behavior matrix) and on the second layer of the long tree (on the second date of the risk factor behavior matrix, based on expert estimates).

The partition is as follows.

Let v ₁ <... <v _n be the values (atoms) of the risk factor on the terminal layer of the short tree, c ₁ <... <c _k be the values (atoms) of the risk factor on the second layer of the long tree.

The corresponding partition intervals [a _i , b _i ] are chosen so that each of them contains exactly one corresponding to _i , and the extreme points (a and b in the previous notation) lie so that the partition intervals cover the atoms v ₁ <... <v _n. If each partition interval contains at least one v • atom, then the probability of none of the branches of the long-term tree after changing the conjugation will not be reset. If the interval [a _i , b _i ] does not contain v • atoms, then as a result of conjugation, all trajectories leaving the node with _i on the first layer are “killed” by zeroing the probabilities.

As a standard variant of partitioning into a system of pairwise disjoint intervals, it is proposed to take the ends of the partition intervals in the midpoints of the segments [c _i , c _{i + 1} ]; the extreme points a and b are set to coincide with the atoms v ₁ and v _n .

Based on the conjugated forecast for the risk factor “US dollar”, a joint forecast is built, including a forecast for two risk factors “US dollar” and “Euro”. An example of the numerical values of the joint forecast for risk factors “US Dollar” and “Euro” is presented in the table:

Name Trajectory probability Risk factor 12/19/2008 02/08/2009 12/31/2009 12/31/2010 12/31/2011 TP1 0.50 U.S. dollar 27.61 29.33 31.81 33.00 34.00 Euro 39.78 39.18 06/37 35.59 34.26 TP2 0.5 U.S. dollar 27.61 27.18 30.81 32.00 33.00 Euro 39.78 39.88 01.01 35.54 34.21

The algorithm laid down in the method of constructing a joint forecast provides for a forecast for one of the risk factors and historical data for two risk factors, based on which the covariance coefficient is used to estimate the angle of inclination and, accordingly, the increment of the risk factor in a certain node.

The calculation is as follows.

We fix an already constructed tree of development of one risk factor up to some terminal date T. Let there be N trajectories on the terminal layer of the tree. The probabilities of the passage of the risk factor along these trajectories are considered already determined and are equal respectively to p ₁ , ..., p _N.

In each node of the constructed tree, the value of the second risk factor is calculated according to the principle "main axis of the dispersion ellipse". One of the factors is the leader, and the other is the follower.

According to historical data, the statistical characteristics of the increments of historical data are estimated at each of the necessary time intervals corresponding to the distance between the layers of the tree.

Let X≡ (X ₁ , ..., X _N ) and Y = (Y ₁ , ..., Y _N ) be two vectors composed of increments of risk factors X and Y for some time lag corresponding to a fixed node and the time distance to the next layer .

и

и соответствующие выборочные стандартные отклонения

и

To calculate them, first, using well-known formulas, sample mean values are calculated, as well as sample variances

and

and corresponding sample standard deviations

and

Then, the covariance cov (X, Y) is calculated (covariance estimation, practical value of the covariance, Pearson covariance) and the sample correlation coefficient Corr (X, Y).

To build a tree, the angle of inclination of the main axis of the ellipse of constant density values of a two-dimensional normal vector is also calculated by the formula:

Further, let the increment ΔX be given in some node of the tree already constructed for risk factor X. It is required to calculate the corresponding increment ΔY. The calculations are performed according to the formula:

According to the risk factor "Inflation", a forecast is based on expert opinion.

For the risk factors "Inflation", "Current own budget revenues", "Current own budget expenditures", "Capital own budget revenues", "Capital own budget expenditures", "Maximum debt at the end of the year" forecasts are made based on expert opinion.

Matrices-forecasts of risk factors “Euro” (conjugate), “Inflation”, “Current own budget revenues”, “Current own budget expenditures”, “Capital own budget revenues”, “Capital own budget expenditures”, “Maximum amount of debt at the end years ”are transferred to the main memory, and then to means 2 of forming the structure of the portfolio of financial obligations, in which the values of the matrix of forecasts of risk factors for the dates of operations on obligations in the portfolio are calculated.

Next, the forecast matrices and preset control options are sent to the data processing tool 3 about alternative management options, where on the basis of the information received in block 35, debt matrices are calculated for each date for the entire period of risk calculation. The calculated matrices of debt indicators are stored in RAM and automatically transferred also to the means 4 of forming a multidimensional representation of the data. Since operations according to management options depend on the risk factor behavior matrices, which are presented in the form of behavior scenarios with probability of implementation, for each management variant several scenarios of behavior of debt indicators with probability of implementation will be calculated. So, for example, in the passive version, several scenarios of behavior of such a debt indicator as nominal debt (million rubles) will be presented:

Name of the trajectories 2008 year 2009 year 2010 year 2011 year 2012 year 2013 year 2014 year TP1 330173.86 205145.19 49096.10 4260.30 4260.30 2130.15 TP2 330183.86 205157.44 49096.10 4260.30 4260.30 2130.15

Further, the data enters the block 37 of the formation of recommendations, which sets the target values of the debt indicator presented in Section III, and the main parameters of the operations on the instruments "Bonds" described in Section II.

Block 37 formation of recommendations calculates one of the desired parameters of operations. Creating operations with the recommended parameters of operations will allow you to obtain target values of the debt indicator.

For example, consider the creation of a bond placement transaction to be placed in 2009. In the search window for operation parameters, the desired operation parameter “Volume” is set from a list including:

- Volume

- Price

- Amount

Set the target indicator "Balance of attraction" for all instruments for the active management option in 2009 and its target value of 7 billion rubles. The list of targets includes:

- debt service costs

- debt repayment expenses

- involved funds

- balance of attraction

- the balance of attraction minus services

- nominal debt

Select the instrument "Bonds_2009", the date of the operation is 06/01/2009 and the transaction price is 95.0 rubles.

Taking into account the specified parameters of the operation, the debt indicators for the active management option in 2009, as well as the properties of the Bonds_2009 tool, the recommendation generation unit 37 calculates the volume of the operation corresponding to the amount (in million rubles):

Type of operation / Name of the trajectory of the risk factor TP1 TP2 Accommodation 7139 7140

In block 32 of the formation of alternative management options, an operation for placement of Bonds_2009 is created on the date of June 1, 2009 in the amount of 7,140 million rubles at a price of 95 rubles.

The same actions to search for operation parameters and create operations in the active control option are performed on the instruments “Bonds_2010” and “Bonds_2011”.

As a result, the indicator “Balance of attraction" along the paths of risk factors takes the following values:

The name of the trajectory 2009 year 2010 year 2011 year TP1 7001.11 14 999.80 8 000.00 TP2 7 000.00 15000.00 8 000.00

The obtained matrices of debt indicators enter the means 4 of forming a multidimensional data presentation, in which fixed data cubes are created, calculated on the basis of debt indicator matrices, and formula data cubes. The values of formula data cubes can be calculated based on matrices of debt indicators and matrix-forecasts of risk factors.

Fixed data cubes include: debt metrics cube; cube of debt indicators in the currency of the instrument; mathematical expectation of debt indicators; standard deviation of debt indicators, etc.

In this example, the following formula cubes are created using the formulas:

1) Operating stream = Current own budget revenues - Current own budget expenditures

2) Net current balance = Operating stream - Debt servicing expenses (debt indicator) - Debt repayment expenses (debt indicator)

3) Own budget revenues = Current own budget revenues + Capital own budget revenues

Further, matrices of debt indicators and formula data cubes enter the means 5 ranking alternative management options.

In block 40 of the formation of the ranking tables, risk tables are created that include the ratio of restrictions on the debt portfolio, indicators of going beyond the established restrictions, as well as characteristics such as:

- the expected value of the debt indicator, which is calculated by the formula:

where Ex is the mathematical expectation, the expected value of the debt indicator,

p _j - the probability of the implementation of the j-th scenario of the debt indicator,

x _j - the value of the debt indicator for the j-th scenario,

m is the number of scenarios.

- the standard deviation of the debt indicator, which is calculated by the formula:

where δ _x is the standard deviation of the debt indicator,

Ex is the mathematical expectation, the expected value of the debt indicator,

p _j - the probability of the implementation of the j-th scenario of the debt indicator,

x _j - the value of the debt indicator for the j-th scenario,

m is the number of scenarios.

Then, in block 41 for calculating the values of the ranking tables, the values are calculated according to the created risk tables.

According to the risk table, including the ratio of restrictions on the debt portfolio, the following values were obtained:

Characteristics 2008 year 2009 year 2010 year 2011 year 2012 year 2013 year 2014 year 2015 year 2016 year 2017 year Mat. expectation (Debt servicing expenses / Current income of own budget),% 0.33 0.23 0.53 0.58 0.66 0.70 0.69 0.64 0.61 0.58 Mat. expectation (Debt expenses / Current income of own budget),% 3.03 5.43 4.78 4.11 3.47 5.44 5.88 2.41 2.29 1.85 Mat. expectation (Nominal debt / Current income of own budget),% 0.13 2.49 6.09 6.86 6.84 6.79 6.75 6.71 6.68 6.68 Mat. expectation (Nominal external debt / Nominal debt),% 72.23 1.82 Mat. expectation (Nominal conditional debt / Nominal debt),% 72.23 1.82 Mat. expectation (Nominal debt / Own budget revenues),% 0.13 2.41 5.86 6.68 6.66 6.61 6.57 6.53 6.51 6.51

According to the risk table, including indicators of exceeding the established limits, the following results were obtained:

Characteristics 2008 year 2009 year 2010 year 2011 year 2012 year 2013 year 2014 year 2015 year 2016 year 2017 year Operating flow> 0 + + + + + + + + + + Mat. expectation (Debt servicing expenses / Current income of own budget) <7.5 + + + + + + + + + + Mat. expectation (Debt expenses / Current income of own budget) <17.5 + + + + + + + + + + Mat.Waiting (Net current balance)> 0 + + + + + + + + + + Mat. expectation (Nominal debt / Current income of own budget) <= 20 + + + + + + + + + + Mat expectation (Nominal external debt / Nominal debt) <= 50 - + Mat expectation (Nominal conditional debt / Nominal debt) <= 15 - + Mat expectation (Nominal debt / Own budget revenues) <= 50 + + + + + + + + + + Mat expectation (Nominal debt) <= Marginal debt at the end of the year + - + + + + + + + +

According to the table, it is obvious that the restriction “Mat. expectation (Nominal debt) <= Maximum amount of debt at the end of the year” is not fulfilled.

According to the risk table, which includes the characteristics of the mathematical expectation and standard deviation for the active control option, the following values are obtained:

Characteristics 2008 year 2009 year 2010 year 2011 year 2012 year 2013 year 2014 year 2015 year 2016 year 2017 year Mat. expectation (Debt servicing expenses), mln rubles 820.96 702.12 2,024.18 2654.14 3 004.26 3 161.65 3 136.21 2 929.02 2 787.09 2 648.43 SKO (Debt servicing expenses), million rubles - 50.60 200.65 347.66 381.03 332.25 247.00 148.89 62.67 12.70 Coefficient of variation (Cost of
debt service) 0.00 0.07 0.10 0.13 0.13 0.11 0.08 0.05 0.02 0.00

According to the risk table, which includes the characteristics of the mathematical expectation and standard deviation for the active control option, the following values are obtained:

Characteristics 2008 year 2009 year 2010 year 2011 year 2012 year 2013 year 2014 year 2015 year 2016 year 2017 year Mat. expectation (Debt servicing expenses), mln rubles 820.96 8.93 5.84 5.68 0.36 0.30 0.11 SKO (Debt servicing expenses), million rubles 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Coefficient of variation (Debt servicing costs) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

In addition to calculating risk characteristics, in block 41 for calculating the values of the ranking tables, the debt ratios are calculated. Consider an example of calculating the given debt indicators.

In the tool 10 discount / agitation of time series, the parameters of the discount / agitation of flows for a portfolio of financial obligations are set. In particular, in block 46 of the formation of the parameters of discounting / aging of the portfolio, the date of discounting and the period within which payments are discounted / aggated are set. In block 47 of the formation of discounting / aging functions, a discount method and a discount rate are set. These data are received at the fourth input of the portfolio structure formation tool 2, which determines the dates on which the discount function values should be calculated. In the tool 3 for processing data on alternative control options, the values of the discount functions for previously determined dates are calculated. The calculated values of the discount / aging functions enter the means 4 of forming a multidimensional data representation, in which a data cube is formed with discounted / projected values of debt indicators. After the formation of the ranking tables in block 40 of the formation of the ranking tables, the calculated discounted / projected values of the debt indicators are displayed in the risk table, including such characteristics as the present value of payments for the portfolio of financial obligations, in the block 41 for calculating the values of the ranking tables.

Characteristics Debt expenses Repayment of principal Depreciation expense on principal Debt service costs Present value of debt payments 102477.86 87784.84 2.74 14690.28

According to the results obtained in block 41 for calculating the values of the ranking tables, it follows that the developed active management option when fulfilling the requirements for the balance of attraction does not meet the specified restrictions, in particular, "Mat. expectation (Nominal debt) <= Maximum amount of debt at the end of the year".

Thus, the balance of attracting the debt portfolio in 2009 should be reduced to volumes that allow the volume of nominal debt not to exceed the maximum amount of debt.

In the data visualization means 6, tabular (in block 42) and graphical (in block 43) data representations are formed, which are then combined into the reporting form in the reporting form generating block 45 and exported to the exchange format in the export device 44.

Since the results revealed that the active control option does not meet the specified restrictions, it is necessary to provide recommendations to bring the management option in line with the restriction. In the template management tool 14, a template is created for the created portfolio, which includes all management options created during the operation, data cubes, risk tables, etc. The specified template is loaded when creating a new portfolio. Downloading the template allows you to create a duplicate portfolio without re-performing the previously performed actions, work on which can continue in any block of the system. In this case, in block 37 of the formation of recommendations based on clearly defined criteria, in particular, the equality of the target debt indicator “Nominal Debt” to the limit value, the parameters of the Bonds_2009 transaction will be calculated. As a result of adjusting the parameters of the operation for Bonds_2009 according to the recommendations received, the active control option will meet all the specified restrictions. In accordance with the results, the attraction balance in 2009 was recommended to be reduced to 6884.5-6885.6 million rubles.

Thus, in accordance with the present invention and taking into account the above example, it can be argued that the introduction of additional blocks, in particular the block for the formation of transactions for the exchange of financial obligations and cash flows in the means of forming the structure of the portfolio of financial obligations, the block for generating recommendations for creating alternative management options in the tool processing data on alternative control options, as well as a template control unit into the declared automated information analytic financial risk management system, can significantly increase the public debt portfolio management efficiency by reducing the level of risk in conducting forward-looking assessments of financial risks and the management of assets and liabilities obtained by improving the technology of preparation of administrative decisions on the basis of a multidimensional and multivariate analysis of the debt portfolio management strategies.

Claims (13)

1. An automated information and analytical system for financial risk management, which includes sequentially included financial liabilities data collection tool, financial liabilities portfolio structure tool, data processing tools for alternative management options, multi-dimensional data presentation tools, alternative management options ranking tool and visualization tool data, as well as sequentially included means of collecting and processing historically their data on risk factors and a means of generating forecasts of risk factors, the output of which is connected to the second input of the means for forming the structure of the portfolio of financial obligations, characterized in that the system is additionally equipped with a means of collecting data on basic rates, the output of which is connected to the third input of the means of forming the structure of a portfolio of financial obligations, a means of discounting / aging time series, the output of which is connected to the fourth input of the means of forming the structure of the portfolio of finance new obligations, and a means of analyzing data on financial obligations, the inputs of which are connected to the output of a means of collecting data on financial obligations, moreover, information inputs of a means of collecting and processing historical data on risk factors, means of generating forecasts of risk factors, means of collecting data on basic rates, means financial liabilities data collection and financial liabilities data analysis tools connected to ODBC sources and / or external systems, the second output of the collection and processing means The historical data on risk factors is connected to the fifth input of the means for forming the structure of the portfolio of financial liabilities, and the outputs of all means of the system, except for the means of collecting data on financial obligations and the means of forming the structure of the portfolio of financial obligations, are connected to the corresponding inputs of the data visualization tool. 2. The automated system according to claim 1, characterized in that it is additionally equipped with a template management tool, the input of which is connected to external systems, and the outputs are connected by two-way communication with a means of forming a portfolio of financial obligations, a means of forming a multidimensional presentation of data, a means of ranking alternative options management, a means of discounting / aging time series and a means of visualizing data. 3. The automated system according to claim 1, characterized in that the financial obligation data collection tool is designed to implement the urban debt subsystem and comprises a data loading unit from external systems, an ODBC data loading unit and a manual data input unit connected in parallel. 4. The automated system according to claim 1, characterized in that the base rate data collection tool is designed to implement the urban debt subsystem and contains a manual data entry unit at basic rates and an ODBC base rate data loading unit included in parallel. 5. The automated system according to claim 1, characterized in that the financial liabilities data analysis tool is designed to implement the urban debt subsystem and comprises a data loading unit for debt indicators from external systems and a debt indicator calculation unit included in parallel and an analytical indicator calculation unit and summary analytical indicators, the input of which is connected to the outputs of the first two blocks. 6. The automated system according to claim 1, characterized in that the means of collecting and processing historical data on risk factors is designed to implement a subsystem of risk factors and comprises a data loading unit from ODBC and a manual data input unit connected in parallel, and a historical data analysis unit for based on the calculation of statistical characteristics, the input of which is connected to the outputs of the first two blocks. 7. The automated system according to claim 1, characterized in that the means of generating forecasts of risk factors is designed to implement a subsystem of risk factors and comprises a parallel data loading unit from ODBC and a manual data input unit, an automated forecast generation unit based on historical data, the input of which connected to the outputs of the first two blocks, and the output to the first input of the conversion and forecast conjugation block, the second input of which is connected to the output of the manual data input block. 8. The automated system according to claim 1, characterized in that the means of discounting / aging the time series is intended for the implementation of the subsystem of debt portfolios and comprises sequentially included a unit for generating parameters for discounting / aging a portfolio and a unit for generating discounting / averaging functions. 9. The automated system according to claim 1, characterized in that the means for forming the structure of the portfolio of financial obligations is designed to implement the subsystem of debt portfolios and contains sequentially included means for determining the parameters of the portfolio and a unit for generating a passive option for managing financial obligations, the outputs of which are connected to the inputs of the unit for forming alternative financial liabilities management options, including a unit for the formation of transactions with financial liabilities and additionally equipped with a block for the formation of transactions for the exchange of financial obligations and cash flows included in parallel. 10. The automated system according to claim 1, characterized in that the data processing tool for alternative management options is designed to implement the subsystem of debt portfolios, contains sequentially a unit for calculating debt indicators and a unit for calculating analytical indicators and summary analytical indicators and is additionally equipped with a unit for generating recommendations on the creation of alternative control options (search for solutions), the inputs of which are connected to the outputs of the first two blocks, and the output is connected to the sixth input means of forming the structure of the portfolio of financial obligations. 11. The automated system according to claim 1, characterized in that the means for generating a multidimensional data presentation is designed to implement the subsystem of debt portfolios and contains sequentially included unit for creating custom data representations and a unit for loading debt indicators into a multidimensional data representation. 12. The automated system according to claim 1, characterized in that the alternative management options ranking tool is designed to implement the debt portfolio subsystem and contains sequentially included block for generating ranking tables (risk tables) and a block for calculating the values of the ranking tables. 13. The automated system according to claim 1, characterized in that the data visualization tool is designed to implement the subsystem of debt portfolios and contains parallel block table presentation of data and a graphical representation of data, the outputs of which are connected to the inputs of the export device to the exchange format, and the second output block tabular presentation of data is connected to the input of the block reporting forms.