Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets

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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.

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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)
    • 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
    • Conceptualization
      Muneer Shaik, Lakshmi Padmakumari
    • Formal Analysis
      Muneer Shaik
    • Methodology
      Muneer Shaik
    • Software
      Muneer Shaik
    • Supervision
      Muneer Shaik, Lakshmi Padmakumari
    • Writing – original draft
      Muneer Shaik
    • Writing – review & editing
      Muneer Shaik, Lakshmi Padmakumari
    • Investigation
      Lakshmi Padmakumari
    • Validation
      Lakshmi Padmakumari