The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH–BEKK model

Abstract

Aiming to investigate the evolution of mean and volatility spillovers between oil and stock marketsin the time and frequency dimensions,we employed WTI crude oil prices, the S&P 500 (USA) index and the MICEX index (Russia) for the period Jan. 2003-Dec. 2014 as sample data. We first applied a wavelet-based GARCH-BEKK method to examine the spillover features in frequency dimension.To consider the evolution of spillover effectsin time dimension at multiple-scales, we then divided the full sample period into three sub-periods, pre-crisis period, crisis period, and post-crisis period. The results indicate thatspillover effects varyacross wavelet scales in terms of strength and direction.By analysis the time-varying linkage, we found the different evolution features of spillover effects between the Oil-US stock market and Oil-Russia stock market. The spillover relationship between oil and US stock market is shifting to short-termwhile the spillover relationship between oil and Russia stock market is changing to all time scales. That result implies that the linkage between oil and US stock market is weakening in the long-term, and the linkage between oil and Russia stock market is getting close in all time scales. This may explain the phenomenon that the US stock index and the Russia stock index showed the opposite trend with the falling of oil price in the post-crisis period. .

Networks of volatility spillovers among stock markets

“…This proposition is partially confirmed in our results as coefficients of the net trade to GDP are mostly positive and often significant. However, for some subsamples, particularly those corresponding to the period of the financial crisis, the respective coefficient is negative and significant, which might have resulted because the number of spillovers from the US market 21 was increasing, while the US market had a negative net trade. A similar idea is behind using FDI net outflows in our specifications, where the effects were positive and significant most of the time.…”

“…Attention has been paid by the researchers to the equity and stock markets (Güloğlu et al [17]; Boubaker and Ali Raza [18]; Výrost et al [19]; Al Rahahleh and Bhatti [20]; Liu et al [21]) as well as to commodity markets (Ji and Guo [22]; Lahmiri [23]). Additional evidence has been put forward for spillovers between spot and futures markets (Liu and Wang [24]; Kang et al [25]) or spot and derivative markets (Kim and Ryu [26]).…”

“…Additional evidence has been put forward for spillovers between spot and futures markets (Liu and Wang [24]; Kang et al [25]) or spot and derivative markets (Kim and Ryu [26]). Among the assets, a special attention was received by crude oil (Kang et al [27]; Liu et al [21]) due to its general economic importance. Finally, it was shown that stock market linkages and their structure vary with crisis periods (Araújo and Louçã [28], Sandoval and Franca [29], Lyócsa et al [6], Majapa and Gossel [30]).…”

Impact of Oil Price Changes on Stock Returns of UK Oil and Gas Companies: A Wavelet-Based Analysis

“…Moreover, they found WTI market was leading in most time. By using the similar method, Liu et al (2017) studied the mean and volatility spillovers between WTI crude oil prices and stock indices of US and Russia for the period January 2003-December 2014. Their results indicated that spilover effects were different in terms of strength and direction across wavelet scales.…”

Bidirectional spillover effect between Russian stock index and the selected commodities

“…Unlike traditional methodologies, MODWT is able to observe different time-horizons without shrinking the sample size and without the wastage of the valuable information. The idea to adopt wavelet analysis in spillover framework came from very few recent papers - Dajčman (2013), Barunik and Vacha, (2013) and Liu et al (2017), who considered the idea to observe the cross-market spillover effect via different wavelet scales. As complementary analysis, we try to assess in which time period and over the wavelet scales high correlation between RTS index and selected commodities exists, which might help portfolio analysts, risk hedgers and global investors who combine these two assets.…”