Forecasting liquidity-adjusted VaR: A conditional EVT-copula approach

This study models the joint distribution of individual stock returns and bid-ask spreads using combined EGARCH EVT and combined GP-INGARCH-EVT processes for the marginals, and bivariate copulas for the dependence structure. We use the proposed approach to first simulate returns and spreads of individual stocks from different countries and regions, and then forecast the Liquidityadjusted Value-at-Risk (L-VaR) measure according to three types of L-VaR models. The backtesting results suggest that the proposed simulated L-VaR models perform better than the competing L-VaR/VaR models in forecasting L-VaR. It is also observed that the simulated L-VaR models perform better than the competing L-VaR/VaR models in predicting the economic downturn.