Measurement of extreme market risk: Insights from a comprehensive literature review

The experience of past financial market turmoil suggests that in addition to eroding investor wealth, the severe consequences of rare extreme market events can spillover and impair the broader real economies. In this context, this paper is an evaluation of the methodological and empirical advances in the measurement of the extreme market risk. This paper argues that a major reason for the origin of such risks post 1980s has been the unintended consequence of asymmetric monetary policy to sustain the rise of financial markets. Thereafter, this review identified the value at risk (VaR) and VaR-based alternative expected shortfall (ES) as the principal measures of extreme market risk. The deficiencies in the standard modelling approaches for VaR-ES measures have led to several advanced estimation methodologies. However, the lack of identification of optimal methodology, in the internal models approach (IMA) regime where financial institutions (FI's) can choose suitable VaR-ES modelling technique incentivizes regulatory arbitrage and other inconsistencies. Therefore, this paper investigates the theoretical and empirical research literature on VaR and ES estimation for financial asset market prices. This paper finds that the extreme value theory (EVT) followed closely by the filtered historical simulation (FHS) are highly accurate methodologies. In addition, Mixture distributions, asymmetric and non-linear versions of the conditional quantile (CQ) approach, (volatility) asymmetry and long memory conditional volatility models, especially those assuming skewed and leptokurtic distributions offer accurate VaR and ES measures.