Ben Salem, Ameni and Safer, Imene and Khefacha, Islem (2022): Value-at-Risk (VAR) Estimation Methods: Empirical Analysis based on BRICS Markets.
Preview |
PDF
MPRA_paper_113350.pdf Download (4MB) | Preview |
Abstract
The purpose of this paper is to investigate some statistical methods to estimate the value-at-Risk (VaR) for stock returns in the BRICS countries for the period between 2011 to 2018. Four different risk methods are used to estimate VaR: Historical Simulation (HS), Riskmetrics, Historical Method and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Process. By applying the Backtesting technique, we try to test the effectiveness of this different methods by comparing the calculated VaR with the real realized losses (or gain) of the portfolio or the index. The results show that for the all-BRICS countries and at different confidence level; the Historical Method and the Historical Simulation are the appropriate methods. While the GARCH model failed to predict precisely the VaR for all BRICS countries.
Item Type: | MPRA Paper |
---|---|
Original Title: | Value-at-Risk (VAR) Estimation Methods: Empirical Analysis based on BRICS Markets |
English Title: | Value-at-Risk (VAR) Estimation Methods: Empirical Analysis based on BRICS Markets |
Language: | English |
Keywords: | Value-at-Risk, BRICS, Riskmetrics, Historical Simulation, GARCH, Historical Method, Backtesting, Confidence level. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics |
Item ID: | 113350 |
Depositing User: | Dr ISLEM KHEFACHA |
Date Deposited: | 21 Jun 2022 06:47 |
Last Modified: | 21 Jun 2022 06:47 |
References: | Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. Statistical methodology, 1(1-2), 105-128. Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3), 203-228. Bayer, S. (2018). Combining value-at-risk forecasts using penalized quantile regressions. Econometrics and statistics, 8, 56-77. Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52(1-2), 5-59. Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994). ARCH models. Handbook of econometrics, 4, 2959-3038. Bonga-Bonga, L., & Nleya, L. (2016). Assessing portfolio market risk in the BRICS economies: use of multivariate GARCH models. Brooks, C., & Persand, G. (2003). Volatility forecasting for risk management. Journal of forecasting, 22(1), 1-22. Diebold, F. X., & Lopez, J. A. (1995). Modeling volatility dynamics. In Macroeconometrics (pp. 427-472). Springer, Dordrecht. Engle, R. F., & Manganelli, S. (2001). Value at risk models in finance (No. 75). ECB Working Paper. Gajadharsingh, A. (2013). Méthodologie du calcul de la VaR de marché : revue de l'approche basée sur des simulations historiques. Mesure et analyse quantitative du risque-Caisse de dépôt et placement du Québec. Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Economic policy review, 2(1). Heynen, R.C. and Kat, H.M. (1994) Partial Barrier Options, The Journal of Financial Engineering, 3, 253-274. Jawwad, F. & Palgrave, M. (2014). Models at work: A Practitioner’s Guide to Risk Management, Global Financial Market, 613 pages. Jorion, P (2006) Value at Risk – The New Benchmark for Managing Financial Risk, Financial Markets and Portfolio Management, 21(3), 397-398. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. In fractals and scaling in finance (pp. 371-418). Springer, New York, NY. Manganelli, S., Robert F. & Engle (2001). Value at Risk Models in Finance, ECB Working Paper, 75, 41. Nieppola, O. (2009) Backtesting Value-at-Risk Models, Master thesis, Helsinki School Of Economics, 72 pages. Salem, A. B., Safer, I., & Khefacha, I. (2021, December) Value at Risk Estimation For the BRICS Countries: A Comparative Study. In 8th TSFS International conference in Finance and Accounting. Silver, C. & al. (2020) What Is Backtesting in Value at Risk (VaR)?, The Investopedia Express Podcast. Sobreira, N., & Louro, R. (2020). Evaluation of volatility models for forecasting Value-at-Risk and Expected Shortfall in the Portuguese stock market. Finance Research Letters, 32, 101098. West, K. D., & Cho, D. (1995). The predictive ability of several models of exchange rate volatility. Journal of econometrics, 69(2), 367-391. Wiener, Z. (1999). Introduction to VaR (value-at-risk). In Risk management and Regulation in Banking (pp. 47-63). Springer, Boston, MA. Yamai, Y., & Yoshiba, T. (2002). Comparative analyses of expected shortfall and value-at-risk: their estimation error, decomposition, and optimization. Monetary and economic studies, 20(1), 87-121. Yamai, Y., & Yoshiba, T. (2005). Value-at-risk versus expected shortfall: A practical perspective. Journal of Banking & Finance, 29(4), 997-1015. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/113350 |