Chinese financial markets play an ever more pertinent role in the global economic context and are therefore increasingly relevant for stabilizing the economy. In this paper, we scrutinize the impact of a series of new policies on stock index futures trading, which have recently been enacted by the Chinese government. We pay particular attention to the way in which these have influenced commodity market volatilities and how their impact on liquidity has affected volatility more generally. Our results reveal a novel interaction between the new government policy and market forces which drive volatilities in commodity markets.
The oil futures market plays a vital role in the global financial system, especially after the negative future oil price rose during the COVID‐19 pandemic. This paper investigates the COVID‐19 impact on the interdependence between the US and Chinese oil futures markets by extending the dynamic conditional correlation‐generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) models with incorporating COVID‐19 variables and by applying vector autoregression (VAR) models. Our study reveals that the COVID‐19 pandemic enhanced the long‐run correlation between the two oil markets. In contrast, daily changes in pandemic severity had a negative effect on the short‐term transient correlation. Our results show that COVID‐19 changed the one‐direction causality from the US oil market to the Chinese market in the pre‐COVID period to a bidirectional causal relation between the two markets during the COVID period. It strengthened the volatility spillover effect from the Chinese to US markets. These findings are helpful to regulars' monitoring oil supply chain risk and investors' cross‐market hedging of spillover risks from a systematic risk perspective.
This paper develops a novel, general derivative pricing model which introduces a liquidity risk factor. The model variants we outline offer a sufficient degree of flexibility so as to enable the valuation of various types of derivative classes including futures, American options, and mortgage backed security options, whereas existing derivative models can only price liquidity risk in European derivatives. We validate the model with oil and gold futures data and compare it to a classical benchmark model void of any liquidity risk. We find that our model is significantly more accurate than the classical model for pricing both oil and gold contracts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.