Financial Risk Estimation in Conditions of Stochastic Uncertainties

2019 ◽

Vol 2 ◽

pp. 70

Author(s):

Vasily Karasev ◽

Ekaterina Karaseva

Keyword(s):

Financial Risk ◽

Business Processes ◽

Process Mining ◽

Quantitative Estimation ◽

Risk Estimation ◽

Main Idea ◽

Operational Risk ◽

Economic Capital ◽

Advantages And Disadvantages ◽

Process Event

Authors propose a new process-event approach for quantitative estimation of operational risk in a bank and calculation the amount of economic capital in dynamics. The proposed approach, according to the Basel II Capital Accord, belongs to the category of “advanced methods”. Operational risk is not financial risk and appeared in unfavorable events mainly. A number of business processes are performed in banking activity. Every business process contains a set of routines (operations), which can be interrupted by operational risk events with certain losses. The main idea of the approach is to describe processes as chains of casual events instead of a traditional graphic description as diagrams. Authors introduce new concepts: an elementary process event, a chain of process events, a time diagram of enterprise’s event flow, and build logical and probabilistic risk models. Methods and formulas for calculation the current and integrated operational risk in dynamics, the amount of economic capital, upper and lower limits of reservation, are given. The value of integrated operational risk can be used as a rating of the current reliability of the bank. The paper outlines the advantages and disadvantages of proposed process-event approach. Research results can be implemented as analytical tool in risk management technology, “process mining” technology and in bank intelligent management systems.

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Financial Risk Estimation with KNN Classification Algorithm on Determined Financial Ratios

European Journal of Science and Technology ◽

10.31590/ejosat.1001663 ◽

2021 ◽

Author(s):

Oğuzcan ULUDAĞ ◽

Arif GÜRSOY

Keyword(s):

Financial Risk ◽

Risk Estimation ◽

Classification Algorithm ◽

Financial Ratios ◽

Knn Classification

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Risk estimation for shares on the Johannesburg Stock Exchange using transfer function modeling

South African Journal of Business Management ◽

10.4102/sajbm.v29i1.765 ◽

1998 ◽

Vol 29 (1) ◽

pp. 1-13

Author(s):

Ian K. Craig ◽

Mike T. Bendixen

Keyword(s):

Transfer Function ◽

Financial Risk ◽

Stock Exchange ◽

Risk Estimation ◽

Systematic Risk ◽

Ols Regression ◽

Regression Methods ◽

Johannesburg Stock Exchange ◽

University Of Cape Town ◽

The University

This study investigates whether the estimation of the systematic risk component or the beta of shares on the Johannesburg Stock Exchange (JSE) can be improved using transfer function or MARIMA modeling. Two propositions are tested. Transfer function modeling will result in estimates of systematic risk which are different from those obtained using conventional OLS regression methods. Transfer function models will provide forecasting results which are better than those provided by betas estimated in the conventional way. Proposition I cannot be tested using conventional inferential tests as the standard errors of estimate of the betas estimated from MARIMA modeling cannot, in general, be measured. It is found however that 16.9% of the MARIMA beta estimates fall outside the 95% confidence intervals of the respective OLS regression beta estimates. Similar results are obtained when the OLS regression betas are compared to the University of Cape Town (UCT) Financial Risk Service and BFA-NET beta estimates. Proposition 2 can in general not be supported as the MARIMA and OLS regression forecasts are found not to be statistically significantly different. Cross correlations between index and share returns are in many cases found not to be statistically significant. In such cases one is probably better off using OLS regression. Resulting beta estimates should be used with caution.

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Adaptive forecasting and financial risk estimation

System research and information technologies ◽

10.20535/srit.2308-8893.2020.1.04 ◽

2020 ◽

Vol 0 (1) ◽

pp. 34-53

Author(s):

Valery Ya. Danilov ◽

O. P. Gozhyj ◽

I. O. Kalinina ◽

Andrii O. Belas ◽

Petro I. Bidyuk ◽

...

Keyword(s):

Financial Risk ◽

Risk Estimation

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Design of international financial risk estimation model based on improved genetic algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189936 ◽

2021 ◽

pp. 1-10

Author(s):

Tianyu Mo

Keyword(s):

Genetic Algorithm ◽

Risk Management ◽

Financial Institutions ◽

Financial Risk ◽

Risk Estimation ◽

Accurate Estimation ◽

Financial Risks ◽

Improved Genetic Algorithm ◽

Estimation Model ◽

Model Based

The financial industry is developing rapidly, and risk management is an important part of the internal management of financial institutions. In order to accurately estimate international financial risks, improve the risk management performance of financial institutions, and ensure the sustainable development of the international financial market, an international financial risk estimation model based on improved genetic algorithms was designed, the value-at-risk model VAR model was selected to estimate the international financial risk by measuring the degree of economic loss, and the improved genetic algorithm was adopted to the seven parts of immature convergence to quickly obtain the VAR value of international financial risks, including initialize the population, real number coding, determine fitness function, selection operator, crossover operator, mutation operator and predict and process. Results show that the rapid estimation of international financial risks was realized, the designed model can achieve accurate estimation of international financial risks, and the time cost of financial risk estimation under different sample sizes is less than 500 ms.

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Extreme Value Analysis for Financial Risk Management

Annual Review of Statistics and Its Application ◽

10.1146/annurev-statistics-042720-015705 ◽

2021 ◽

Vol 8 (1) ◽

pp. 217-240

Author(s):

Natalia Nolde ◽

Chen Zhou

Keyword(s):

Risk Management ◽

Financial Risk ◽

Risk Estimation ◽

Extreme Value ◽

Dependent Data ◽

Extreme Value Analysis ◽

Distributed Data ◽

Cross Sectional ◽

Value Analysis ◽

Financial Risk Management

This article reviews methods from extreme value analysis with applications to risk assessment in finance. It covers three main methodological paradigms: the classical framework for independent and identically distributed data with application to risk estimation for market and operational loss data, the multivariate framework for cross-sectional dependent data with application to systemic risk, and the methods for stationary serially dependent data applied to dynamic risk management. The article is addressed to statisticians with interest and possibly experience in financial risk management who are not familiar with extreme value analysis.

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