Post Model Correction in Risk Analysis and Management

Abstract

This work focuses on Value at Risk (VaR) and Expected Shortfall (ES) in conjunction with the so called, low price effect. In order to improve forecasts of risk measures like VaR or ES when low price effect is present, we propose the low price correction which does not involve additional parameters and instead of returns it relies on asset prices. The forecasting ability of the proposed methodology is measured by appropriately adjusted popular evaluation measures, like MSE and MAPE as well as by backtesting methods. For illustrative and comparative purposes a real example from the Athens Stock Exchange as well as a number of penny stocks from Nasdaq, NYSE and NYSE MKT are fully examined. The proposed technique is always applicable, but its superiority and effectiveness is evident in extreme economic scenarios and severe stock collapses. The proposed methodology that pays attention not only to the asset return but also to the asset price, provides sufficient evidence that prices could contain important information which could if taken under consideration, results in improved forecasts of risk estimation.

Keywords

• EWMA
• ARCH
• GARCH
• APARCH
• FIGARCH
• Expected shortfall
• VaR
• PVaR
• Violation ratios; Normalised shortfall
• EPS
• Leverage effect
• Low price effect
• Low price correction
• Backtesting