Connectedness between the Crude Oil Futures and Equity Markets During the Pre- and Post-Financialisation Eras

Abstract

This paper examines the effect of financialisation of futures markets has on the relationship between crude oil futures and equities by using the VAR-DCC-GARCH model. Specifically, by accounting for the systematic patterns of commodity price volatility, namely, seasonality and maturity effects for the pre-financialisation (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) and post-financialisation (2004-2019) period. While speculation that reflects non-commercial investors' activity is found to have a negative impact on crude oil futures' volatility before the financialisation period, open interest as a measure of liquidity has a negative effect after 2004. The finding indicates weakening seasonality in crude oil futures and diminishing Samuelson maturity effect i.e. volatility of the contract increases as it nears to expiration since financialisation. This confirms the importance of accounting for volatility dynamics while contributing to financialisation debate.

Co-Movement between Commodity and Equity Markets Revisited - an Application of the Thick Pen Method

“…Similar to Jach (2017), we also find some evidence where the TPMA measure of a given scale resembles results from the MTTPMA measure of cross-correlation. Comparing our results with Wadud et al (2021), we find similar interdependencies in crude oil futures-equities. Third, our study also reveals that in the short-term feature (i.e., in higher frequencies), co-movements are lower than in the longer-term feature (i.e., in lower frequencies) co-movement.…”

“…The ARCH/GARCH framework presents a popular method to measure linkage among asset classes or assessing contagion that include either univariate, bivariate or multivariate GARCH models. Time-varying conditional correlation frameworks have featured in past studies: Baba-Engle-Kraft-Kroner (BEKK) (Ewing & Malik, 2013); Diagonal Vector Error Conditional Heteroscedasticity (VECH) (Degiannakis et al, 2013); Constant Conditional Correlation (CCC) (Sadorsky, 2012); Dynamic Conditional Correlation (DCC) (Baur & Lucey, 2009;Roy & Roy, 2017); DCC with External repressor (DCCX) (Wadud et al, 2021); Asymmetric DCC (ADCC) (Cappiello et al, 2006;Kenourgios, 2014); Asymmetric Generalised DCC (AGDCC) (Cappiello et al, 2006;Shahzad et al, 2017); Fractionally Integrated Asymmetric Power ARCH DCC (FIAPARCH-DCC) (Conrad et al, 2011;Dimitriou et al, 2013), VARMA DCC (Kumar et al, 2019); and DCC with the Student t-distribution (TDCC) (Pesaran & Pesaran, 2010) used in the past studies.…”

“…Likewise, we assess the differences between co-movements of index and off-index commodities with the equity index. In the context of the financialisation of commodities, Wadud et al (2021) explore the interconnectedness between crude oil futures and the equity index by using the VAR-DCC-GARCH model, which is model/parameter specific. They use a parametric method but it is useful to see the results of interdependence through a non-parametric method.…”

“…Technically, forward filling for missing data is not required because the TPMA and MTTPMA methods can be run with non-synchronous data. However, as we followWadud et al (2021) for dataset selection, we decide to use the forward fill method for consistency.7 This data series has recently (30 June 2021) been discontinued as publicly available data.…”