Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

Abstract

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible.

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

“…They reveal that increasing OVX exhibits a larger negative influence on oil prices than declining OVX, indicating the existence of a long-run asymmetric cointegrating relationship between them. It is worth mentioning that this result of asymmetry is also recognized in previous findings [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Furthermore, Shaikh [44] uses neural network and quantile approaches and suggests that crude oil prices are aligned with OVX.…”

“…They find an asymmetric relationship and show evidence that the implied volatility is likely to be calm during global financial crises and increases after the crisis periods. Echaust and Just [42] study the tail dependence behavior of WTI oil returns and OVX changes. The results suggest that the strongest tail dependence of negative oil price shocks and OVX changes happens during the pandemic crisis.…”

“…In this regard, most studies examine the linear relationship between oil returns and OVX (e.g., [38][39][40]), whereas fewer consider the non-linear dependence. Agbeyegbe [41], Ji and Fan [27], Liu et al [21], and Echaust and Just [42] use approaches based on copula models; Li et al [43] apply a multiscale multivariate multifractal detrended fluctuation analysis; and Shaikh [44], Silva Junior [45], and Fousekis [46] use approaches based on quantile regression methods. Nevertheless, none of these studies assess the Granger causal relationship between crude oil returns and OVX based on quantilebased techniques accounting for various market conditions.…”

Quantifying information transfer between Commodities and Implied Volatilities in the Energy Markets: A Multi-frequency Approach

“…The literature studies document a strong asymmetric negative association between implied volatility index based on other markets such as stock (VIX), Euro (EZV), and Gold (GVZ) and the underlying index returns (Giot, 2005;Car and Wu, 2006;Whaley, 2009;Chiang, 2012;Pathak and Deb, 2020;Amoako et al, 2022) and all reported significant negative relations between the implied volatility indexes and underlying stock index returns. The four papers that come close to the current paper are Chen and Zou (2015), Shaikh (2019), Boateng et al (2021), and Echaust and Just (2021), and all also identified a significant inverse contemporaneous relationship between the asymmetric relationship between the OVX and energy commodity returns. Different models applied by these studies include GARCH models (Whaley, 2009), Granger causality (Chiang, 2012), Pooled regression models (Chiang, 2012), Kalman filter (Chen and Zou, 2015), Quantile regressions (Boateng et al, 2021;Shaikh, 2019), and Value-at-Risk (Echaust and Just, 2021).…”

“…The four papers that come close to the current paper are Chen and Zou (2015), Shaikh (2019), Boateng et al (2021), and Echaust and Just (2021), and all also identified a significant inverse contemporaneous relationship between the asymmetric relationship between the OVX and energy commodity returns. Different models applied by these studies include GARCH models (Whaley, 2009), Granger causality (Chiang, 2012), Pooled regression models (Chiang, 2012), Kalman filter (Chen and Zou, 2015), Quantile regressions (Boateng et al, 2021;Shaikh, 2019), and Value-at-Risk (Echaust and Just, 2021). While these studies enjoin important findings for both policy and investment, they fail to address the role of information content in the nexus.…”