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

Muneer Shaik,Lakshmi Padmakumari

doi:10.21511/imfi.19(1).2022.04

Abstract

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.

Highlights

Financial markets have become increasingly global and sophisticated in the current stage of the world economy
The results found that VaR models perform poorly during crisis periods like global financial crisis (GFC) and COVID-19 compared to the overall sample period
The price using eight different models like Binomial test (Bin), POF, Traffic Light (TL), time until first failure (TUFF), series are converted to return series computed as conditional coverage (CC), Time between failures (TBF), CCI, and TBFI tests were applied

Summary

Introduction

Financial markets have become increasingly global and sophisticated in the current stage of the world economy. The COVID-19 disaster has had grave undesirable consequences on a global scale, hurting multiple economies and deteriorating their conditions, perhaps leading to a catastrophic recession akin to that seen during the Global Recession of 2008–2009. The study computes three popular VaR estimation techniques (namely, historical simulation model (HS), normal distribution method (N), and exponential weighted moving average (EWMA) model) and compares their predictive performance using eight different backtesting approaches (namely, Traffic Light (TL), Binomial (Bin), proportion of failure (POF), time till first failure (TUFF), conditional coverage independence (CCI), conditional coverage (CC), time between failures independence (TBFI), and time between failures (TBF) tests).

Objectives

Methods

Results

Conclusion