Measuring the Time-Frequency Dynamics of Return and Volatility Connectedness in Global Crude Oil Markets

Abstract

This study analyzes return and volatility spillovers across global crude oil markets for 1 January 1991 to 27 April 2018, using an empirical technique from the time and frequency domains, and makes four key contributions. First, the spillover tables reveal that the West Texas Intermediate (WTI) futures market, which is a common indicator of crude oil indices, contributes the least to both return and volatility spillovers. Second, the results also show that the long-term factor contributes the most to returns spillover, while the short-term factor contributes the most in terms of volatility. Third, the rolling analyses show that the time-variate connectedness in terms of returns tends to be strong, but there was no noticeable change from 1991 to April 2018 in terms of volatility. Finally, the major events between 1991 and April 2018, namely the Asian currency crisis (1997–1998) and the global financial crisis (2007–2008), caused a rise in the total connectedness of returns and volatility.

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

“…Second, with the assistance of the Barunik and Krehik [18] methodology and following the methods of Toyoshima and Hamori [27], the spillover indexes obtained by the Diebold-Yilmaz approach were decomposed into three frequency bands: the high frequency, 'Frequency H', roughly corresponding to 1-5 days; the medium frequency, 'Frequency M', roughly corresponding to 5-21 days; and the low frequency, 'Frequency L', roughly corresponding to 21 days to infinity.…”

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

“…For the dynamics of spillover effects, we employ the moving-window method to analyze the DY12 and BK18. Similar to the existing literature (Toyoshima and Hamori, 2018;Balli et al, 2019;Wang and Wang, 2019) we set the length of the window at 250 trading days, 370 trading days, and 500 trading days. We find that the plots of these trading days have almost the same trends.…”

Measurement of Connectedness and Frequency Dynamics in Global Natural Gas Markets

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