Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

Abstract

This paper aims to propose the augmented GJR-GARCH (GJR-GARCH M ) model that extends the GJR-GARCH model by comprising overnight returns volatility (ONV), daily high-low prices range (PK), and fear index (VIX) as explanatory variables for the GJR's variance equation, respectively. The proposed models are used to estimate the daily value-at-risk values and evaluate their downside risk management performance for the SPDRs covering the period from 2009 to 2014. Empirical results show that the GJR-GARCH M model outperforms the GJR-GARCH model for most cases, suggesting that the GJR-GARCH-based VaR forecasts can be moderately improved with the additional information embodied in the ONV, PK and VIX volatility estimators. In addition, daily high-low prices range and VIX are far more informative than the overnight volatility estimator for improving the GJR-GARCH-based VaR forecasts. Risk managers can employ the proposed models for estimating and controling the potential loss of ETFs in the face of financial catastrophes.

Does the tail risk index matter in forecasting downside risk?

“…Although the accuracy of VaR forecasts might be improved through complicated model specifications, it could lack the efficiency in parameter estimation. The other strand of literature attempts to calibrate model accuracy by including additional variables in the variance equation of GARCH‐type models (e.g., Wang et al, 2016). Recently, Hood and Malik (2018) find that incorporating structural breaks in both the GARCH and filtered historical simulation models generates accurate VaR forecasts and achieves economically efficient daily capital charges.…”

On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index

“…Corrado and Truong (2007) augment the GJR-GARCH model of Glosten et al (1993) by intraday high-low price range and VIX in order to investigate their additional information for improving GJR-GARCH volatility forecasting accuracy. Recently, Wang et al (2016) propose the augmented GJR model by including various volatility estimators (overnight volatility, daily prices range, and VIX) as explanatory variables for the variance equations in GJR model. These models are used to estimate their daily VaR values for the Standard & Poor's Depositary Receipts (SPDRs).…”