Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets
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DOIhttp://dx.doi.org/10.21511/imfi.19(1).2022.04
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Article InfoVolume 19 2022, Issue #1, pp. 51-63
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Value-at-risk (VaR) is the most common and widely used risk measure that enterprises, particularly major banking corporations and investment bank firms employ in their risk mitigation processes. The purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on BRICS (Brazil, Russia, India, China, South Africa) and US stock market indices. The study employs three different VaR estimation models, namely normal (N), historical (HS), exponential weighted moving average (EMWA) procedures, and eight backtesting models. The empirical analysis is conducted during three different periods: overall period (2006–2021), global financial crisis (GFC) period (2008–2009), and COVID-19 period (2020–2021). The results show that the EMWA model performs better compared to N and HS estimation models for all the six stock market indices during overall and crisis sample periods. The results found that VaR models perform poorly during crisis periods like GFC and COVID-19 compared to the overall sample period. Furthermore, the study result shows that the predictive accuracy of VaR methods is weak during the COVID-19 era when compared to the GFC period.
- Keywords
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JEL Classification (Paper profile tab)C30, C53, C58, G10
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References34
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Tables3
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Figures3
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- Figure 1. Comparison of returns and VaR at 95% for different models (2006–2021)
- Figure 2. Comparison of returns and VaR at 95% for different models (GFC, 2008–2009)
- Figure 3. Comparison of returns and VaR at 95% for different models (COVID-19, 2020–2021)
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- Table 1. Descriptive statistics of the return series
- Table 2. VaR model evaluation through different backtesting methods for 2006–2021
- Table 3. VaR model evaluation through different backtesting methods for the GFC (2008–2009) and COVID-19 (2020–2021) periods
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- Basel Committee on Banking Supervision. (1996). Supervisory Framework for the Use of ‘Backtesting’ in Conjunction with the Internal Models Approach to Market Risk Capital Requirements.
- Bonga-Bonga, L., & Nleya, L. (2016). Assessing portfolio market risk in the BRICS economies: use of multivariate GARCH models (MPRA Paper 75809). University Library of Munich, Germany.
- Burdorf, T., & van Vuuren, G. (2018). An evaluation and comparison of Value at Risk and Expected Shortfall. Investment Management and Financial Innovations, 15(4), 17-34.
- Cheong, C. W., Isa, Z., & Nor, A. S. M. (2011). Cross market value-at-risk evaluations in emerging markets. African Journal of Business Management, 5(22), 9385-9400.
- Choi, P., & Min, I. (2011). A comparison of conditional and unconditional approaches in value-at-risk estimation. Japanese Economic Review, 62(1), 99-115.
- Christoffersen, P. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.
- Cogneau, P. (2015). Backtesting Value-at-Risk: how good is the model? In Intelligent Risk: knowledge for the PRMIA community (pp. 028-034).
- Danielsson, J. (2012). Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk, with Implementation in R and MATLAB. Wiley Finance.
- Degiannakis, S., Floros, C., & Livada, A. (2012). Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence. Managerial Finance, 38(4), 436-452.
- Desheng, W., & Chatpailin, T. (2019). Value at Risk Performance in BRICS Countries. IEEE International Conference on Cybernetics.
- Edwards, D. W. (2014). Risk management in trading. Techniques to drive profitability of hedge funds and trading desks. New Jersey: John Wiley & Sons.
- Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801.
- Haas, M. (2001). New Methods in Backtesting. Bonn: Financial Engineering Research Center Caesar.
- Iglesias, E. M. (2015). Value at risk of the main stock market indexes in the European Union (2000–2012). Journal of Policy Modeling, 37(1), 1-13.
- Jiang, Y., Fu, Y., & Ruan, W. (2019). Risk spillovers and portfolio management between precious metal and BRICS stock markets. Physica A: Statistical Mechanics and its Applications, 534, 120993.
- Jobayed, A. (2017). Evaluating the predictive performance of Value-at-Risk (VaR) models on Nordic market indices (Master’s Thesis). Hansen School of Economics.
- Jorion, P. (2011). Financial Risk Manager Handbook (6th ed.). Wiley Finance.
- Kupiec, P. (1995). Techniques for Verifying the Accuracy of Risk Management Models. Journal of Derivatives, 3, 73-84.
- Lin, C. H., & Shen, S. S. (2006). Can the student-t distribution provide accurate value at risk? The Journal of Risk Finance, 7(3), 292-300.
- Mabitsela, L., Maré, E., & Kufakunesu, R. (2015). Quantification of VaR: A Note on VaR Valuation in the South African Equity Market. Journal of Risk and Financial Management, 8(1), 103-126.
- Mathworks. (2017). Overview of VaR backtesting.
- McNeil, A., Frey, R., & Embrechts, P. (2005). Quantitative Risk Management. Princeton University Press.
- Miletic, M., & Miletic, S. (2015). Performance of Value at Risk models in the midst of the global financial crisis in selected CEE emerging capital markets. Economic research – Ekonomska istraživanja, 28(1), 132-166.
- Mukta, K., & Muneer, S. (2020a). Are BRICS stock market indices mean reverting? Evidence based on expected lifetime range ratio. International Journal of Business and Economics, 19(2), 169-186.
- Mukta, K., & Muneer, S. (2020b). Testing asymmetry in mean reversion based on high and low prices: Evidence from BRICS markets. Journal of Public Affairs, e2443.
- Muteba Mwamba, J., & Beytell, D. (2015). Value At Risk, Minimum Capital Requirement and the use of Extreme Value Distributions: An application to BRICS Markets. International Business & Economics Research Journal, 14(1), 135-144.
- Naradh, K., Chinhamu, K., & Chifurira, R. (2021). Estimating the value-at-risk of JSE indices and South African exchange rate with Generalized Pareto and stable distributions. Investment Management and Financial Innovations, 18(3), 151-165.
- Nieppola, O. (2009). Backtesting Value-at-Risk Models. Aalto University.
- Omari, C., Mundia, S., & Ngina, I. (2020). Forecasting Value-at-Risk of Financial Markets under the Global Pandemic of COVID-19 Using Conditional Extreme Value Theory. Journal of Mathematical Finance, 10(4), 569-597.
- Ozun, A., & Cifter, A. (2007). Portfolio value-at-risk with time-varying copula: Evidence from Latin America. Journal of Applied Sciences, 7(14), 1916-1923.
- Ramalho, D. R. V. (2020). Predictive performance of value-at-risk models: Covid-19 “Pandemonium” (Master’s Thesis). Universidade de Lisboa.
- Song, Q., Liu, J., & Sriboonchitta, S. (2019). Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas. Mathematics, 7(3), 274.
- Su, Y-C., Huang, H-C., & Wang, S. (2010). Modeling value at risk of financial holding company: time varying vs. traditional models. Banks and Bank Systems, 5(1).
- Subbiah, M., & Fabozzi, F. J. (2016). Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques. International Review of Financial Analysis, 45(C), 189-201.