TITLE:
Value at Risk Models in Indian Markets: A Predictive Ability Evaluation Study
AUTHORS:
Kushagra Goel, Sunny Oswal
KEYWORDS:
Value-at-Risk, Monte Carlo Simulation, Implied Volatility, Historical Based Approach
JOURNAL NAME:
Theoretical Economics Letters,
Vol.9 No.8,
December
9,
2019
ABSTRACT: Value at risk (VaR) is a method of measuring the potential loss in portfolio value for a
given distribution of historical returns over a given time period. Measurement
of risk therefore becomes essential for a
corporate decision. This study attempts to rank the overall predictive ability
of select value at risk models in estimating market risks of Indian financial
markets. This study estimates the respective predictive ability by employing
numerical and graphical measures. The findings plug the gaps in the literature
and estimate the best method to be used
in the industry. The results evidentially prove that parametric model using normal distribution with GARCH (1,1) fits best for estimating value at risk.