Russian Banks Credit Risk Stress-Testing Based on the Publicly Available Data

Bidzhoyan, Davit S.; Bogdanova, T. V.

doi:10.1007/978-3-030-02351-5_31

Cited by 3 publications

(7 citation statements)

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“…Organizations that possess the necessary data use non-performing loans (NPL) [15], loans of quality categories 4-5 (according to the Bank of Russia Regulation No 590-P dated 28 June 2017 "On the Procedure for Making Loss Provisions by Credit Institutions for Loans and Similar Debts"), as well as their share in the portfolio. However, paper [1] presents an algorithm for credit risk stress testing based on publicly available financial reports; it proposes to use the volume of LLP, which can be calculated relying on changings accounts balances, as a measure of credit risk. In this paper, we also use LLP to measure credit risk.…”

Section: Methods and Models For Assessing The Banks' Financial Positionmentioning

confidence: 99%

“…The large number of macroeconomic factors that directly or indirectly affect the financial statements of banks should be taken into account when conducting stress testing. For example, the Bank of England when stress testing the seven largest banks uses 58 macroeconomic indicators 1 and work with them is extremely difficult. Many studies have proposed methods for reducing the dimensionality: in particular, the principal component method [20] and other methods that reduce the dimensionality [21] are used.…”

Section: Methods and Models For Assessing The Banks' Financial Positionmentioning

confidence: 99%

“…Banks whose capital adequacy ratios are above the minimum values at the end of a stressful period are considered to be reliable. This paper proposes an algorithm for conducting credit risk stress testing for a group of banks which is an extension of the algorithm presented in [1] real GDP growth rate (compared to the same quarter of previous year), in %; the consumer price index growth rate (compared to the same quarter of previous year), in %; the population real income growth rate (compared to the same quarter of previous year), in %;…”

Section: An Algorithm Of Stress Testing For a Cluster Of Banksmentioning

confidence: 99%

“…The results of stress testing conducted by the Bank of Russia are not published, but nevertheless, they may be of interest to the bank's lenders and depositors. The paper [1] presents the stress testing algorithm for a bank's credit risk on an individual basis based on official, publicly available financial reports. However, conducting stress testing of the credit risk of a large number of banks is laborious.…”

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confidence: 99%

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Credit risk stress testing in a cluster of Russian commercial banks

Bidzhoyan¹,

Bogdanova²,

Neklyudov³

2019

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Stress testing as an instrument of risk evaluation is actively used in many international organizations, as well as by central banks in many countries. Some organizations (including the Bank of Russia) when conducting stress testing do not publish results of the tests, though they are interesting for the business community. They are reticent so to avoid causing panic on markets which could lead to a massive outflow of deposits from the banking sector as a whole or from some individual banks in particular. As a rule, stress testing is conducted relying on huge number of unpublished reporting forms, but the business community has no access to them. Only four reporting forms are presented on the Bank of Russia's website. In this paper we propose a simplified algorithm of credit risk stress testing of a banking cluster based on the four officially published reporting forms. The algorithm provides modelling of median values of banking variables depending on macroeconomic indicators, and subsequent retranslation of the received values for assessing the financial position of each bank included in the cluster. It is assumed that growth rates of banking indicators obtained from the econometrics models relying on median values are the same for each bank in the cluster. As of 1 January 2018, credit risk stress testing was conducted for 26 banks, nine of which are system-significant credit institutions. Within the stress testing, eight econometric time series models were developed. As a result, it was discovered that 11 out of 26 banks in the cluster will face certain difficulties regarding statutory requirements related to capital ratios or buffers.

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Section: Methods and Models For Assessing The Banks' Financial Positionmentioning

confidence: 99%

Section: Methods and Models For Assessing The Banks' Financial Positionmentioning

confidence: 99%

Section: An Algorithm Of Stress Testing For a Cluster Of Banksmentioning

confidence: 99%

mentioning

confidence: 99%

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Credit risk stress testing in a cluster of Russian commercial banks

Bidzhoyan¹,

Bogdanova²,

Neklyudov³

2019

Bus. Inform.

Self Cite

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“…The proposed concept of forming a universal complex indicator (UCI) of express valuation of the state of the сontrol object makes it possible to assess not only the current, but also to predict the future state of such a сontrol object [19][20][21]. to take into account the external in relation to the object of management characteristics of its activity (the presence of actual mini-hotels on the coast, the tone of the news about the director of the bank, the comfort of the infrastructure of the educational institution, the activity of the use of social networks by the respondent, etc.)…”

Section: The Concept Of Constructing a Universal Complex Indicator For Express Valuation Of The State Of The сOntrol Objectmentioning

confidence: 99%

Valuating the position of the control object based on a universal complex indicator using structured and unstructured data

Bogdanova¹,

Zhukova²

2021

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Valuating the position of a controlled object using indicators which are management and control tools is widely used in many areas of the economy. Usually such indicators are based on internal data, however, as the volume of available open information grows, algorithms for valuation of the position of certain control objects and on open structured data are appearing. The disadvantage of these models is their narrow specialization and binding only to structured, and sometimes strictly official data, which, as a rule, have a rare publication frequency. This does not allow you to track the change in the position of the object at different times. The authors have proposed a concept for constructing a universal complex indicator (UCI) for express valuation of the position of a controlled object in various types of activity: banking, educational, industrial, etc. Another difference in the construction of the UCI is that the concept presented in the article assumes, as a reference point, to take into account the requirements of regulatory authorities, while in most Russian and foreign studies, indicators are built for the needs of investors. It is also proposed to use, along with structured and unstructured data, tracking the dynamics of changes in the position of the control object. To determine the UCI values on the basis of various econometric models and methods, the components that characterize the requirements of the control bodies to the control object are calculated; using them the UCI value is determined from the truth table. The concept proposed was tested to build an express valuation of the financial position of 108 banks for the period from 1 January 2018 to 1 February 2020. In accordance with the requirements of the Central Bank of the Russian Federation, the values of the three UCI components were obtained, and the value was calculated for each bank. The predictive ability of the constructed model, tested on three banks of the test sample, was confirmed by the consistency of the express valuation with their actual position in March 2020.

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Stress Testing as a Banking Risk Assessment Tool: A Review of International Practice, Methods and Methodology

Bidzhoyan

2020

Ekonomicheskaya nauka sovremennoy Rossii

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Stress testing is a broad research area, at the interference of many disciplines (finance, banking, econometrics, macroeconomics, microeconomics, mathematical analysis etc.), and is of interest to both theoretical scientists and practitioners. The usefulness of this approach became evident after the financial crisis of 2007–2009, which prompted many researchers to develop and constantly improve stress-testing methodologies, using which it is possible to accurately forecast the behavior of banks and the financial sector in crisis periods. It allows banks to assess the scale of losses and timely take the necessary measures to strengthen the financial condition. Today, economic science has the biggest arsenal of stress testing methods that allow us to assess potential losses in crisis periods that correspond to extreme but plausible events. The stress testing methodologies cover all-important types of risks (credit, interest rate risk, liquidity risk etc.), as well as specific risks. The presence of a huge number of stress testing methods guarantees its versatility and depth, which could be explained by the attempt using this methods to create a behavior model of banks, which are quite complex in structure and functionality. The purpose of this study is to provide a concise, but at the same time comprehensive classification of stress testing methods, as well as a review of the current approaches to stress testing or to solving its various aspects (for example, developing stress scenarios) presented by scientists, international organizations, central banks and other interested parties. This paper is an introduction to the vast field of analytics – stress testing, and is oriented to banking and financial analysts, macroeconomists who want either to familiarize themselves with stress testing as a tool for assessing banking risks, or to systematize all the accumulated knowledge in this area in order to better understand economic processes.

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Russian Banks Credit Risk Stress-Testing Based on the Publicly Available Data

Cited by 3 publications

References 9 publications

Credit risk stress testing in a cluster of Russian commercial banks

Credit risk stress testing in a cluster of Russian commercial banks

Valuating the position of the control object based on a universal complex indicator using structured and unstructured data

Stress Testing as a Banking Risk Assessment Tool: A Review of International Practice, Methods and Methodology

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