Dynamic frequency connectedness between oil and natural gas volatilities

Dynamic Spillovers Between International Crude Oil Market and China's Commodity Sectors: Evidence From Time-Frequency Perspective of Stochastic Volatility

“…Since the global financial crisis (GFC), a growing number of studies are conducted to explore the connectedness between crude oil and commodity markets, and their methods can be broadly classified into several categories: VAR or structural VAR (SVAR) (Wang et al, 2014;de Nicola et al, 2016); GARCH models (Ji and Fan, 2012;Ewing and Malik, 2013;Jiang et al, 2019); Copula models (Koirala et al, 2015); nonparametric causality analysis (Nazlioglu et al, 2013); vector error correction model (VECM); Markov regime switching (MRS) models (Uddin et al, 2018); and forecast error variance decomposition (FEVD) (Diebold et al, 2017;Lovcha and Perez-Laborda, 2020). However, the previous literature generally underestimates connectedness among commodity markets of a particular class or group, while there are few studies focusing on the oil-commodity nexus at the industry level.…”

“…In fact, the forecast horizon H is not as important as the GFEVD implemented here, which is unconditional. Because the static results of the GFEVD framework over the entire sampling period may smooth out the results when the relationship between the variables changes over time (Lovcha and Perez-Laborda, 2020), this paper considers both the static and dynamic spillover effects to obtain more comprehensive estimations. For the dynamics of spillover effects, we employ the moving-window method to analyze the DY12 and BK18.…”

“…After controlling for macroeconomic and other factors, even the unrelated commodity prices tend to change together (de Nicola et al, 2016). Crude oil prices are effective in transmitting shocks to other commodity markets (Choi and Hammoudeh, 2010;Nazlioglu et al, 2013;Ahmadi et al, 2016;Luo and Ji, 2018;Lovcha and Perez-Laborda, 2020). However, although literature has focused on the association between crude oil prices and a particular class of commodity (mostly energy or agricultural commodities), the findings are mixed and ambiguous in general, which may due to different modeling techniques and timescales (Tiwari et al, 2020).…”

“…First, as far as we know, this study is the first to explore the dynamic volatility spillovers between crude oil price and various China's commodity sectors including precious metals, nonferrous metals, coal coke, and steel ore; nonmetal building materials; energy products; chemical products; grains; oils and fats; and soft commodities. Some empirical studies have investigated the volatility connectedness between crude oil price and commodities of a particular class or group, such as the agricultural commodities (Nazlioglu et al, 2013;Mensi et al, 2014;Wang et al, 2014;Luo and Ji, 2018), the energy commodities (Ng and Donker, 2013;Lovcha and Perez-Laborda, 2020), the precious metals commodities (Ewing and Malik, 2013;Bildirici and Turkmen, 2015), and the industries commodities (Choi and Hammoudeh, 2010). However, the results of the current literature examining the relationship between energy prices and other commodity prices are mixed and generally ambiguous, which may be due to the use of different models based on various assumptions and analysis of different timescales (Tiwari et al, 2020).…”

Influence of Fluctuations in Fossil Fuel Commodities on Electricity Markets: Evidence from Spot and Futures Markets in Europe

“…Many recent studies have used this method to study spillover effects and connectedness between assets, such as Singh et al [19], Kang and Lee [20], and Malik and Umar [21]. In a more recent study, Lovcha and Perez-Laborda [22] used the Diebold-Yilmaz approach and the Barunik and Krehlik methodology to analyze the volatility connectedness between Henry Hub natural gas and West Texas Intermediate (WTI) crude oil in the time and frequency domains. Lau et al [23] used the E-GARCH model and the Barunik and Krehlik methodology approach to investigate the return and volatility spillover effects among white precious metals, gold, oil, and global equity.…”