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Journal articles on the topic 'Financial risk management - Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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1

Bohdalová, Mária, and Michal Greguš. "VaR BASED RISK MANAGEMENT." CBU International Conference Proceedings 1 (June 30, 2013): 25–33. http://dx.doi.org/10.12955/cbup.v1.11.

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In this paper we discuss the Value–at–Risk concept and we analyse the market risk by using EWMA approach. EWMA (exponentially weighted moving average) forecasting technique is a popular measure of various risks in financial risk management. We will compare standard EWMA, robust EWMA and skewed EWMA forecast of VaR. JP Morgan standard EWMA is derived from Gaussian distribution. Robust EWMA is based on Laplace distribution and skewed EWMA is a new approach derived from an asymmetric Laplace distribution. Asymmetric Laplace distribution takes into account both skewness and heavy tails in return d
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2

Christoffersen, Peter F., and Francis X. Diebold. "How Relevant is Volatility Forecasting for Financial Risk Management?" Review of Economics and Statistics 82, no. 1 (2000): 12–22. http://dx.doi.org/10.1162/003465300558597.

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3

Tsintsadze, Asie, Vladimer Glonti, Lela Oniani, and Tamar Ghoghoberidze. "Empirical Analysis of Financial and Non-Financial Risks of the Commercial Bank." European Journal of Sustainable Development 8, no. 2 (2019): 101. http://dx.doi.org/10.14207/ejsd.2019.v8n2p101.

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Background: Activities of commercial banks are connected with numerous risks, the source of which is the internal and external processes of the bank. Objectives: Risk management science has been studying the origins of the risks, determining their impact quality and avoiding expected loss models from the 1950s. Method/Approach: Credit risk regressive analysis is based on the selection of effective factors, determination of their influence and prediction of future according to the correlation coefficient. Results/Findings: In the article, it is discussed the regressive analysis of operational r
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4

Chen, Cathy W. S., Richard Gerlach, Edward M H. Lin, and W. C. W. Lee. "Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis." Journal of Forecasting 31, no. 8 (2011): 661–87. http://dx.doi.org/10.1002/for.1237.

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Valipour, Hashem, and Mostafa Sohouli Vahed. "Risk Management and Forecasting Macro-Variables Influences on Bank Risk." International Journal of Business and Management 12, no. 6 (2017): 137. http://dx.doi.org/10.5539/ijbm.v12n6p137.

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Nowadays banks, as the most important component ofmoney market, are playing a very important role in country’s economy. By developing money markets, banking and financial institutes’ activities it is extensively developed and with no doubts economic development is not possible without considering the role of banking and money markets. By virtue of special and sensitive role of banks in Iran economic system, any shock, disturbances and/or ineffectiveness in economic systems directly effect on banks’ and financial institutes’ performance as well as phenomenon such as high inflation and/or price
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Tung, H. K. K., and M. C. S. Wong. "Financial risk forecasting with nonlinear dynamics and support vector regression." Journal of the Operational Research Society 60, no. 5 (2009): 685–95. http://dx.doi.org/10.1057/palgrave.jors.2602594.

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7

Chen, Qian, and Richard H. Gerlach. "The two-sided Weibull distribution and forecasting financial tail risk." International Journal of Forecasting 29, no. 4 (2013): 527–40. http://dx.doi.org/10.1016/j.ijforecast.2013.01.007.

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8

I. Dimitras, Augustinos, Stelios Papadakis, and Alexandros Garefalakis. "Evaluation of empirical attributes for credit risk forecasting from numerical data." Investment Management and Financial Innovations 14, no. 1 (2017): 9–18. http://dx.doi.org/10.21511/imfi.14(1).2017.01.

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In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric mo
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Yarygina, I. Z., V. B. Gisin, and B. A. Putko. "Fractal Asset Pricing Models for Financial Risk Management." Finance: Theory and Practice 23, no. 6 (2019): 117–30. http://dx.doi.org/10.26794/2587-5671-2019-23-6-117-130.

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The article presents the analysis findings of the problems and prospects of using the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The aim of the article is to find out the features of value of bank assets and to develop recommendations for assessing financial risks based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used to achieve the aim. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a vo
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Amri, Adil El, Rachid Boutti, Salah Oulfarsi, Florence Rodhain, and Brahim Bouzahir. "Carbon financial markets underlying climate risk management, pricing and forecasting: Fundamental analysis." Financial Markets, Institutions and Risks 4, no. 4 (2020): 31–44. http://dx.doi.org/10.21272/fmir.4(4).31-44.2020.

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Climate Change (CC) is a major issue of our century. Controlling the constraints of Greenhouse Gas (GHG) emissions through transformation into opportunities, in an organization to increase industrial production, has become a necessity. The main reason for this adoption was the effectiveness of energy management and responsible linkages that are being developed to determine the issues and opportunities of carbon finance for organizations. Through analysis of the European Union Emissions Trading Scheme (EU ETS) and the Clean Development Mechanism (CDM), this article presents and demonstrates a v
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PORTOVARAS, Tetiana. "METHODOLOGICAL ASPECTS OF FINANCIAL RISK ANALYSIS AS AN INSTRUMENT OF MANAGEMENT." WORLD OF FINANCE, no. 3(56) (2018): 128–40. http://dx.doi.org/10.35774/sf2018.03.128.

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Introduction. The article deal with the risks and its influence on the enterprises activity within the uncertainty. The essence, classification and analysis of risks is generalized, on the basis of which their own understanding is proposed. The question of forecasting the probability of bankruptcy as a widely used method of risk management is focused, its advantages and disadvantages at the present stage of development of the domestic economy are shown. Purpose. The aim of the paper is justifying the methodological approach to the analysis of financial risks in order to increase the efficiency
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12

Ray, Russ. "Currency Futures: Some Implications For International Financial Management." Journal of Applied Business Research (JABR) 3, no. 3 (2011): 62. http://dx.doi.org/10.19030/jabr.v3i3.6516.

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This paper tests the contemporary currency futures market for interest-rate parity, purchasing-power parity, market efficiency, and hedging effectiveness. The study finds that the currency futures markets is a highly efficient, hedging-effective market exhibiting significant degrees of interest-rate parity and (longer-term) purchasing-power parity. Finally, the study infers from such findings some practicable policy tools for international cash management, multi-country capital budgeting, currency forecasting, and the risk management of foreign exchange exposure.
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13

Couce-Vieira, Aitor, David Rios Insua, and Alex Kosgodagan. "Assessing and Forecasting Cybersecurity Impacts." Decision Analysis 17, no. 4 (2020): 356–74. http://dx.doi.org/10.1287/deca.2020.0418.

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Cyberattacks constitute a major threat to most organizations. Beyond financial consequences, they may entail multiple impacts that need to be taken into account when making risk management decisions to allocate the required cybersecurity resources. Experts have traditionally focused on a technical perspective of the problem by considering impacts in relation with the confidentiality, integrity, and availability of information. We adopt a more comprehensive approach identifying a broader set of generic cybersecurity objectives, the corresponding set of attributes, and relevant forecasting and a
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14

Manuylenko, Viktoriya, Denis Ryzin, Natalia Gryzunova, Olga Bigday, and Olga Mandrytsa. "Toolset for financial risk strategic assessment in corporations based on stochastic modeling." Revista Amazonia Investiga 9, no. 28 (2020): 451–64. http://dx.doi.org/10.34069/ai/2020.28.04.50.

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The study substantiates the need to develop and test a model for assessment of strategic financial risk level in corporations. It implies modeling for two indicators: relative (financial leverage) and absolute (external capital of indicators). The model should also take into account influence of emergent environment factors and most stakeholder groups’ interests when building scenarios for their behaviors in the financial markets –Implementation of the model allows establishing financial risk target values considering deviation calculations between the indicators’ modeled and actual values sim
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15

Clements, Michael P., Costas Milas, and Dick van Dijk. "Forecasting returns and risk in financial markets using linear and nonlinear models." International Journal of Forecasting 25, no. 2 (2009): 215–17. http://dx.doi.org/10.1016/j.ijforecast.2009.01.003.

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16

Liu, Jianxu, Quanrui Song, Yang Qi, Sanzidur Rahman, and Songsak Sriboonchitta. "Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions." Sustainability 12, no. 10 (2020): 4000. http://dx.doi.org/10.3390/su12104000.

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The global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct a trade decision model for investors and financial institutions to assist them in forecasting risk and potential returns based on the results of the analysis of systemic risk. The factor copula-generalized autoregressive conditional heteroskedasticity (GARCH) models and component expected shortfall (CES) were combined for the first t
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17

Al Wadi, S. "Improving Volatility Risk Forecasting Accuracy in Industry Sector." International Journal of Mathematics and Mathematical Sciences 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1749106.

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Recently, the volatility of financial markets has contributed a necessary part to risk management. Volatility risk is characterized as the standard deviation of the constantly compound return per day. This paper presents forecasting of volatility for the Jordanian industry sector after the crisis in 2009. ARIMA and ARIMA-Wavelet Transform (WT) have been conducted in order to select the best forecasting model in the content of industry stock market data collected from Amman Stock Exchange (ASE). As a result, the researcher found that ARIMA-WT has more accuracy than ARIMA directly. The results h
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18

Kim, A., Y. Yang, S. Lessmann, T. Ma, M. C. Sung, and J. E. V. Johnson. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting." European Journal of Operational Research 283, no. 1 (2020): 217–34. http://dx.doi.org/10.1016/j.ejor.2019.11.007.

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19

Marcek, Dusan, and Lukas Falat. "Volatility Forecasting in Financial Risk Management with Statistical Models and ARCH-RBF Neural Networks." Journal of Risk Analysis and Crisis Response 4, no. 2 (2014): 77. http://dx.doi.org/10.2991/jrarc.2014.4.2.4.

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20

Dickason, Zandri, and Sune J. Ferreira. "The effect of age and gender on financial risk tolerance of South African investors." Investment Management and Financial Innovations 15, no. 2 (2018): 96–103. http://dx.doi.org/10.21511/imfi.15(2).2018.09.

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Financial risk tolerance refers to the amount of risk a person is willing to take when making financial decisions. Previous researchers have found that demographic factors when used as independent variables to have an effect on the risk tolerance behavior of investors. Within this study, emphasis was given to gender and age within a sample of South African investors. Not much research on risk tolerance and demographics has been done in South Africa. Hence, an opportunity for further research within this field emerged. This study aimed to contribute towards the accurate risk profiling of South
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21

Singh, Devi. "Managing Currency Risk." Vision: The Journal of Business Perspective 2, no. 2 (1998): 6–10. http://dx.doi.org/10.1177/09722629x98002002002.

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With market frontiers expanding and financial price volatility and risk going up, management of foreign exchange risk assumes importance. The implications of increased involvement of Indian companies in international trade and finance require managers to measure the foreign exchange exposure and manage it to maximise profitability, net cash flow and the market value of the firm. An analysis of the foreign risk management practices of some Indian companies reveals that they neither have a model for forecasting foreign exchange rate movement nor a detailed mechanism to evaluate the effectiveness
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22

Peel, Michael, and Nick Wilson. "NEURAL NETWORK SIMULATION: A NEW APPROACH TO RISK ASSESSMENT AND BUSINESS FORECASTING." Management Research News 19, no. 6 (1996): 50–54. http://dx.doi.org/10.1108/eb028477.

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Neural Networks (NN's), one of the latest developments in computer software artificial intelligence, are an innovative method of simulating and analysing complex and changing systems of relationships. Originally developed to mimic the neural architecture and functioning of the human brain, NN techniques have recently been applied successfully in a wide variety of complex business and financial applications (Trippi and Turban, 1994).
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23

Shen, Ze, Qing Wan, and David J. Leatham. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN." Journal of Risk and Financial Management 14, no. 7 (2021): 337. http://dx.doi.org/10.3390/jrfm14070337.

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One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample perf
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24

Antoni Haber, Józef, Alina Bukhtiarova, Svitlana Chorna, Olesia Iastremska, and Tetiana Bolgar. "Forecasting the level of financial security of the country (on the example of Ukraine)." Investment Management and Financial Innovations 15, no. 3 (2018): 304–17. http://dx.doi.org/10.21511/imfi.15(3).2018.25.

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In the conditions of functioning of economic relations, which arise between subjects of the financial system of Ukraine, the question of creating safe conditions for their activity is increasingly being raised. Attention is paid to the investigation of the state of financial security of the country as a component of economic security, in terms of its key elements, which allows attention to the most important indicators and to develop measures to prevent existing threats.The purpose of the paper is to forecast the level of financial security of the country based on regression analysis of impact
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25

Kekytė, Ieva, and Viktorija Stasytytė. "Comparative Analysis of Investment Decision Models." Mokslas - Lietuvos ateitis 9, no. 2 (2017): 197–208. http://dx.doi.org/10.3846/mla.2017.1023.

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Rapid development of financial markets resulted new challenges for both investors and investment issues. This increased demand for innovative, modern investment and portfolio management decisions adequate for market conditions. Financial market receives special attention, creating new models, includes financial risk management and investment decision support systems.Researchers recognize the need to deal with financial problems using models consistent with the reality and based on sophisticated quantitative analysis technique. Thus, role mathematical modeling in finance becomes important. This
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26

Валиева, Гульнара, Gulnara Valieva, Ленар Хазеев, and Lenar Khazeev. "THE RISK MANAGEMENT SYSTEM AS THE BASIS OF THE FINANCIAL STABILITY OF THE ENTERPRISE (ON THE EXAMPLE OF AGRICULTURAL ENTERPRISE OF THE REPUBLIC OF TATARSTAN)." Vestnik of Kazan State Agrarian University 12, no. 2 (2017): 93–97. http://dx.doi.org/10.12737/article_59ad09682cabd2.51620825.

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The process of assessing the financial stability of an agricultural enterprise and the possible introduction of elements of a risk management system is illustrated by the example of the agricultural organization JSC “Alabuga Sote” (the Republic of Tatarstan). In order to consider the financial stability of the enterprise, the following indicators were calculated: absolute liquidity ratio, current liquidity ratio, profitability on equity and profitability of sales. Based on these calculations, the company’s financial stability was assessed and possible measures were proposed to improve it. In a
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27

Plakandaras, Vasilios, Periklis Gogas, and Theophilos Papadimitriou. "The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach." Algorithms 12, no. 1 (2018): 1. http://dx.doi.org/10.3390/a12010001.

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An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several
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Kuzmynchuk, N., T. Kutsenko, L. Strygul, O. Terovanesova, and S. Klepikova. "INTELLECTUAL INSTRUMENTAL ANALYSIS IN ECONOMIC SECURITY MANAGEMENT OF THE ENTERPRISES FOR COUNTERING RAIDING." Financial and credit activity: problems of theory and practice 2, no. 37 (2021): 231–43. http://dx.doi.org/10.18371/fcaptp.v2i37.230242.

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Abstract. Modern views on the economic nature of countering raiding are a permanently important prerequisite for the stable development of the enterprises and the formation of a system of their effective management as the basis for ensuring economic security through the development and implementation of scenarios for countering raiders.
 The presented article is aimed at the use of the analytical and methodological tools regarding the introduction into the activities of the enterprise to ensure economic security in terms of countering raiding.
 Using the methods of forecasting and mo
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29

Liu, Hung‐Chun, and Jui‐Cheng Hung. "Forecasting volatility and capturing downside risk of the Taiwanese futures markets under the financial tsunami." Managerial Finance 36, no. 10 (2010): 860–75. http://dx.doi.org/10.1108/03074351011070233.

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30

Afreen, Maria. "Review Paper on Composite Leading Index Creation for Forecasting the Bangladeshi Financial Sector." International Journal of Finance & Banking Studies (2147-4486) 9, no. 4 (2020): 23–32. http://dx.doi.org/10.20525/ijfbs.v9i4.791.

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In perspective of the economic vulnerability faced by banks in financial sector, this study mirrors the methodology used by Shumway (2001) – the dynamic hazard model that is able to forecast systemic risk in financial market arena. Here, the terminology followed is based on the CAMELS framework variables: capital adequacy, asset, management, earnings, liquidity and sensitivity to market risk. The objective of this study is to construct a macroprudential indicator (MPI) for the case of Bangladeshi financial market. The result will then be tested for robustness with macro-stress test. Lagged ind
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31

Ettredge, Michael, Ying Huang, and Weining Zhang. "Restatement Disclosures and Management Earnings Forecasts." Accounting Horizons 27, no. 2 (2013): 347–69. http://dx.doi.org/10.2308/acch-50414.

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SYNOPSIS We examine the impact of financial restatements on managers' subsequent earnings forecasts. We argue that restatements create conflicting incentives. One incentive is to repair manager reputations as information providers by providing more and better guidance via earnings forecasts. The opposing incentive is to avoid risk by reducing the information in forecasts. We find that compared to control firms, restatement companies exhibit a decreased propensity to issue quarterly earnings forecasts following restatements. Those that do make forecasts issue fewer forecasts in post-restatement
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32

Kudryavtseva, N. N., Y. V. Pakhomova, and Y. N. Duvanova. "Economic model of forecasting financial condition of the enterprise." Proceedings of the Voronezh State University of Engineering Technologies 82, no. 1 (2020): 356–59. http://dx.doi.org/10.20914/2310-1202-2020-1-356-359.

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The calculation indicator of the planning model, which allows you to determine the study period in order to obtain uniform input data. This economic and mathematical model is an object that also uses a system of mathematical tools that maximizes the creation of value, taking into account additional restrictions that take into account various types of innovations, which as a result make it possible to create an innovative portfolio that most closely matches the strategy of the enterprise. , price range and the necessary resource of the innovation plan. An innovative portfolio is expressed in ca
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33

Teoh Yeong Kin, Akmal Haziq Ahmad Aizam, Suzanawati Abu Hasan, Anas Fathul Ariffin, and Norpah Mahat. "Bankruptcy Prediction Model with Risk Factors using Fuzzy Logic Approach." Journal of Computing Research and Innovation 6, no. 2 (2021): 102–10. http://dx.doi.org/10.24191/jcrinn.v6i2.220.

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Forecasting bankruptcy remains crucial, especially during this pandemic. Managers, financial institutions, and government agencies rely on the information regarding an impending bankruptcy threat to make decisions. This paper developed a straightforward bankruptcy prediction model using the fuzzy logic approach for individuals and companies to evaluate their performance and analyse the tendency of getting bankrupt. A sample of 250 respondents from banks and financial firms were tested using the qualitative risk factors, namely, industrial risk, management risk, financial flexibility, credibili
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Babirath, Julia, Karel Malec, Rainer Schmitl, Kamil Maitah, and Mansoor Maitah. "Forecasting based on spectral time series analysis: prediction of the Aurubis stock price." Investment Management and Financial Innovations 17, no. 4 (2020): 215–27. http://dx.doi.org/10.21511/imfi.17(4).2020.20.

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The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio du
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35

Vidmant, O. S. "Forecasting the volatility of Financial Time Series by Tree Ensembles." World of new economy 12, no. 3 (2019): 82–89. http://dx.doi.org/10.26794/2220-6469-2018-12-3-82-89.

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The use of new tools for economic data analysis in the last decade has led to significant improvements in forecasting. This is due to the relevance of the question, and the development of technologies that allow implementation of more complex models without resorting to the use of significant computing power. The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. More stringent liquidity and transparency standards in relation to the financial sector also encourage part
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Ramos-Pérez, Eduardo, Pablo J. Alonso-González, and José Javier Núñez-Velázquez. "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility." Mathematics 9, no. 15 (2021): 1794. http://dx.doi.org/10.3390/math9151794.

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Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper intro
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37

USYK, Vladislavа, and Serhii VOITKO. "Risk management related to the use of payment systems." Economics. Finances. Law, no. 7/1 (July 30, 2021): 30–35. http://dx.doi.org/10.37634/efp.2021.7(1).6.

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Payment system is a complex system of managing funds transfers and settlements between economic entities, which ensures the rational implementation of the basic functions, uninterrupted financing of activities using the latest methods and payment instruments used to reduce cash flow and ensure the effectiveness of monetary, monetary policy countries. In the course of its functioning, the payment system, like any economic system, can be exposed to the risks of its professional activity. Characteristic risks of the payment system are credit risk, liquidity risk, currency risk, operational, legal
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38

Huang, Chun-Sung, Chun-Kai Huang, and Knowledge Chinhamu. "Assessing The Relative Performance Of Heavy-Tailed Distributions: Empirical Evidence From The Johannesburg Stock Exchange." Journal of Applied Business Research (JABR) 30, no. 4 (2014): 1263. http://dx.doi.org/10.19030/jabr.v30i4.8675.

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<p>It has been well documented that the empirical distribution of daily logarithmic returns from financial market variables is characterized by excess kurtosis and skewness. In order to capture such properties in financial data, heavy-tailed and asymmetric distributions are required to overcome shortfalls of the widely exhausted classical normality assumption. In the context of financial forecasting and risk management, the accuracy in modeling the underlying returns distribution plays a vital role. For example, risk management tools such as value-at-risk (VaR) are highly dependent on th
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39

Afreen, Maria. "Analysing the Return on Asset to Construct Foretelling Indicator for Bangladeshi Banking Sector." International Journal of Finance & Banking Studies (2147-4486) 9, no. 4 (2020): 11–22. http://dx.doi.org/10.20525/ijfbs.v9i4.790.

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Financial institutions and banks are required to follow mechanisms to monitor the positions and create stimulas for sensible risk-taking by divisions a well as individuals. Risk measurement comprises of the quantification of risk exposures, whereas risk management demonstrates to the overall procedures by which managers fulfill these needs to identify the risks and recognise the category of the risks it faces. This research targerts on the economic instability faced by banks in financial arena in terms of the crises affairs in regard of economic distress. Here, the methodology followed is base
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40

Попова and Anna Popova. "VAR As a Tool to Assess the Market Risk of Trading Positions of a Commercial Bank." Economics 4, no. 2 (2016): 58–64. http://dx.doi.org/10.12737/18769.

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Forecasting of any risk is the crucial activity for any commercial bank. In current
 situation market risk is an important element needed to be analyzed. The
 probability of this type of risk may be affected by the change in the market value
 of financial instruments and by the volatility of foreign exchange rates. Nowadays
 in Russia each organization should conduct proper risk-management and be able
 to predict possible losses. The article presents the assessment of the market risk
 by the example of the price of the common share of the Bank of Moscow.
 For
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Jeon, Chan-Soo. "Value-at-Risk Forecasting using Realized Volatility Models and GARCH-type Models." Journal of Derivatives and Quantitative Studies 21, no. 2 (2013): 135–67. http://dx.doi.org/10.1108/jdqs-02-2013-b0001.

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The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more
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42

Gao, Lu, and Jian Xiao. "Big Data Credit Report in Credit Risk Management of Consumer Finance." Wireless Communications and Mobile Computing 2021 (June 15, 2021): 1–7. http://dx.doi.org/10.1155/2021/4811086.

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Traditional consumer finance is a modern financial service method that provides consumer loans to consumers of all classes. With the gradual improvement of China’s credit reporting system, big data credit reporting has effectively made up for the lack of traditional credit reporting and has been widely used in the consumer finance industry. In this context, the in-depth analysis of the specific application of big data credit reporting in the credit risk management of consumer finance and the strengthening of the research on the application of big data credit reporting in the credit risk manage
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43

MILOVIDOVA, Yana D. "Risk-Management of Investment Projects in Implementation of Objects of Real Economy." Journal of Advanced Research in Law and Economics 10, no. 4 (2019): 1309. http://dx.doi.org/10.14505//jarle.v10.4(42).32.

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Risk management in financial operations is based on a diverse structure and mobility of the portfolio that allow for a structural change of a project - namely, achieving synergies in the formation of the investment portfolios. However, financial operations do not always correlate with the stock exchange rules or other covenants. This provides important evidence for the restructuring purposes of the investment portfolio. In this regard, forecasting and the development of the toolkit of the risk management may be implemented only under conditions of the real object of the economy. The idea that
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44

Sun, Yi, Quan Jin, Qing Cheng, and Kun Guo. "New tool for stock investment risk management." Industrial Management & Data Systems 120, no. 2 (2019): 388–405. http://dx.doi.org/10.1108/imds-03-2019-0125.

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Purpose The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior. Design/methodology/approach Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock. Findings It turns out that the lead–lag relation
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45

Kis, Iryna. "STRATEGIC ENVIRONMENTAL RISK MANAGEMENT OF TRANSPORT ENTERPRISES." Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management, no. 2(4) (April 19, 2021): 24–33. http://dx.doi.org/10.20998/2413-3000.2021.4.4.

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The article is devoted to the study of theoretical and methodological aspects of strategic risk management of environmental risks of the transport enterprise. The urgency of the issue is due to the acceleration of changes in the external environment of economic activity of transport enterprises, which determines the need for adjustments to the management system. Strategic environmental risk management is currently the main direction for its effective management. Statistical data on the number of enterprises in the transport industry, the introduction of new fixed assets, the financial result o
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46

Tripathy, Naliniprava, and Ashish Garg. "Forecasting Stock Market Volatility: Evidence from Six Emerging Markets." Journal of International Business and Economy 14, no. 2 (2013): 68–93. http://dx.doi.org/10.51240/jibe.2013.2.4.

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This paper forecasts the stock market volatility of six emerging countries by using daily observations of indices over the period of January 1999 to May 2010 by using ARCH, GARCH, GARCH-M, EGARCH and TGARCH models. The study reveals the positive relationship between stock return and risk only in Brazilian stock market. The analysis exhibits that the volatility shocks are quite persistent in all country’s stock market. Further the asymmetric GARCH models find a significant evidence of asymmetry in stock returns in all six country’s stock markets. This study confirms the presence of leverage eff
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47

Yeh, Hsiang-Yuan, Yu-Ching Yeh, and Da-Bai Shen. "Word Vector Models Approach to Text Regression of Financial Risk Prediction." Symmetry 12, no. 1 (2020): 89. http://dx.doi.org/10.3390/sym12010089.

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Linking textual information in finance reports to the stock return volatility provides a perspective on exploring useful insights for risk management. We introduce different kinds of word vector representations in the modeling of textual information: bag-of-words, pre-trained word embeddings, and domain-specific word embeddings. We apply linear and non-linear methods to establish a text regression model for volatility prediction. A large number of collected annually-published financial reports in the period from 1996 to 2013 is used in the experiments. We demonstrate that the domain-specific w
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48

LIU, WEN-QIONG, and WEN-LI HUANG. "HEDGING OF SYNTHETIC CDO TRANCHES WITH SPREAD AND DEFAULT RISK BASED ON A COMBINED FORECASTING APPROACH." International Journal of Theoretical and Applied Finance 22, no. 02 (2019): 1850057. http://dx.doi.org/10.1142/s0219024918500577.

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Hedging of credit derivatives, especially the Collateralized Debt Obligations (CDOs), is the prerequisite of risk management in financial market. Since both spread risk and default risk exist, the models in existing literature resort to the incomplete-market theory to derive the hedging strategies. From another point of view, the construction of hedging strategies of CDO might be regarded as the process of forecasting the changes in value of CDO by the changes in value of hedging instruments. Based on this idea, this paper proposes an alternative hedging approach via the combined forecasting a
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RANA, RAJEEV SINGH. "A Modelling Value-at-Risk in investment banks: “Empirical evidence of JP Morgan, Merrill Lynch and Bank of America”." Journal of Global Economy 14, no. 2 (2018): 146–69. http://dx.doi.org/10.1956/jge.v14i2.487.

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The objective of paper is to assess the efficiency of financial model to capture increasing volatilities across asset class markets of the three investment banks. For which data will be collect to forecast the credit risk, and to know how well our standard tools forecast volatility, particularly during the turmoil that extend throughout the globe. Volatility prediction is a critical task in asset valuation and risk management for investors and financial intermediaries. The paper will focus on Value-at-Risk (VaR) which is a standard model that has been forecasted using both non-parametric and p
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Клычова, Гузалия, Guzaliya Klychova, Алсу Закирова, Alsu Zakirova, Зульфат Хамидуллин, and Zulfat Khamidullin. "METHODOLOGICAL APPROACHES TO THE FORMATION OF THE FINANCIAL MANAGEMENT POLICY OF RECEIVABLE ARREARS." Vestnik of Kazan State Agrarian University 14, no. 1 (2019): 126–31. http://dx.doi.org/10.12737/article_5ccedf74305b80.69036555.

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In modern conditions, an objective need arises in controlling education and the state of accounts receivable, determining credit policy, analyzing and ranking clients, forecasting cash receipts from debtors, and reducing bad debts. Using the methods of theoretical and empirical research, such as analysis and synthesis, classification, proof, systems approach, logical method, the significance of the credit policy is revealed, its elements are considered in detail, the main problems of the credit policy organization are highlighted. On the basis of the materials presented by practitioners in thi
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