Abstract
This study investigates whether the implied crude oil volatility and the historical OPEC price volatility can impact the return to and volatility of the energy-sector equity indices in Iran. The analysis specifically considers the refining, drilling, and petrochemical equity sectors of the Tehran Stock Exchange. The parameter estimation uses the quasi-Monte Carlo and Bayesian optimization methods in the framework of a generalized autoregressive conditional heteroskedasticity model, and a complementary Bayesian network analysis is also conducted. The analysis takes into account geopolitical risk and economic policy uncertainty data as other proxies for uncertainty. This study also aims to detect different price regimes for each equity index in a novel way using homogeneous/non-homogeneous Markov switching autoregressive models. Although these methods provide improvements by restricting the analysis to a specific price-regime period, they produce conflicting results, rendering it impossible to draw general conclusions regarding the contagion effect on returns or the volatility transmission between markets. Nevertheless, the results indicate that the OPEC (historical) price volatility has a stronger effect on the energy sectors than the implied volatility has. These types of oil price shocks are found to have no effect on the drilling sector price pattern, whereas the refining and petrochemical equity sectors do seem to undergo changes in their price patterns nearly concurrently with future demand shocks and oil supply shocks, respectively, gaining dominance in the oil market.
Highlights
As global financial markets become more integrated, knowledge of their mutual interplay becomes more important for market participants to choose appropriate investment strategies
As our primary deduction on the cross-market association is based on the generalized autoregressive conditional heteroskedasticity (GARCH) model parameters, we meticulously performed their estimation using a variety of techniques, as shown in Tables 2, 3 and 4
In the Oil refining (ORI) sector, for example, the limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS)/Bayesian methods estimate a positive effect of Oil volatility index (OVX)/OPEC basket oil price (OPEC), whereas the quasi-Monte Carlo (QMC) method finds that the effect is insignificant
Summary
As global financial markets become more integrated, knowledge of their mutual interplay becomes more important for market participants to choose appropriate investment strategies. Sadorsky 2006; Driesprong et al 2008; Park and Ratti 2008; Chen 2009; Filis 2010; Basher et al 2012), and still others find no relationship (Huang et al 1996; Cong et al 2008; Apergis and Miller 2009; Miller and Ratti 2009; Reboredo and Rivero-Castro 2014; Hatemi et al 2017) These conflicting results may arise owing to several underlying pitfalls in the studies, such as not considering the level of oil dependence among stock markets, not explicitly considering heterogeneity in the context in which the aggregate index is exposed to gains or losses from changes in the oil price, the nature of the oil price shock considered, and the time-varying element used (Smyth and Narayan 2018)
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