FORECASTING VALUE AT RISK (VAR) FOR EMERGING AND DEVELOPED MARKETS

This paper explores different approaches to modelling and forecasting VaR, using both historical simulation and volatility-weighted bootstrap methods, where volatility is estimated using GARCH (1,1) and EGARCH (1,1). It examines the one day predictive ability of three historical simulation VaR models at the 90%, 95%, and 99% confidence levels for developed and emerging equity markets for the period 2011- 2017 that witnessed difficult and extreme market conditions. 870 scenarios of future returns are generated for each of the 500 days representing the out of sample period extending from March 2015 up to January 2017 in order to estimate the corresponding VaR for both markets. The GARCH (1,1) volatility-weighted model is accepted for both markets and is classified as the best performing model. The EGARCH (1,1) volatility-weighted model's results were inconclusive; in fact, the back-test was accepted at all confidence levels for the developed markets while rejected at the 99% confidence level for the emerging markets. The basic historical simulation failed in estimating an accurate VaR for the emerging markets.