Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets

Abstract

Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as demand-side benchmarks, we document new stylized facts about cyclical properties of the transmission mechanism generated by volatility shocks with heterogeneous frequency responses. Our first key finding is that shocks to volatility with response shorter than one week are increasingly important to the transmission mechanism over the studied period. Second, demand-side shocks to volatility are becoming increasingly important in creating short-run connectedness. Third, the supply-side shocks to volatility resonating in both the long run and short run are important sources of connectedness.

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

“…In addition, we can also identify the impact of other important economic or geopolitical events from Figure 4A, such as Iran's geopolitical tensions in 2012, China's market-oriented reform on July 20, 2013 and Organization of Petroleum Exporting Countries's (OPEC's) production cut agreement at the end of 2017 and so on. Our findings are consistent with the view that these events represent important geopolitical and economic factors affecting risk spillovers or the shocks of oil supply and demand (Krehlík and Baruník, 2017;Wang and Wang, 2019). As displayed in Figure 4B, it is clear that the total volatility spillover in our system is also mainly driven by high-frequency information (in dark turquoise), although the contribution of the medium-frequency and low-frequency components is significant.…”

Does Investor Sentiment Affect Clean Energy Stock? Evidence from TVP-VAR-Based Connectedness Approach

“…Those could be attributed to emergency events, such as the global financial crisis in 2008, the European sovereign debt crisis in 2011, and the China-United States trade war in 2018. In line with previous literature [32][33][34], our study provides evidence that supports the notion that the connectedness across different assets increases rapidly during financial turmoil. The dynamic connectedness exhibited some significant decreases (e.g., 2012-2014) after the turmoil in the financial market; a reasonable explanation for those decreases could be the economic recovery and economic prosperity.…”

Asymmetric and time-frequency spillovers among commodities using high-frequency data

“…As can be seen from Table 1, there is growing evidence of the volatility spillover among commodity markets from both network and time-frequency domain perspectives. However, no evidence exists to date that capitalizes on high-frequency data to investigate the presence of asymmetric volatility spillovers among commodity markets in the time-frequency domain, despite the recently documented benefits of high-frequency data from the investment and policy aspects of commodity markets (Luo & Ji, 2018;Křehlík & Baruník, 2017;Baruník & Kocenda, 2019;Lu, Yang, & Liu, 2019). As suggested by Křehlík and Baruník (2017), the short-and long-run dynamics in energy market connectedness have an important bearing for systemic risk, especially when modelling the high-frequency aspects of volatility.…”

“…In the time-frequency domain, the possibility of asymmetric volatility spillovers may arise from the economic linkages among commodity markets (Casassus et al, 2013) or due to the shocks to economic activity which impact variables at various frequencies with various strengths (Baruník and Křehlík, 2018). Building on this literature, the foremost contribution of this study is as part of the much needed and emerging stream of studies using highfrequency data to uncover new stylized facts in certain commodity groups (Luo & Ji, 2018;Křehlík & Baruník, 2017;Baruník & Kocenda, 2019;Lu, Yang, & Liu, 2019). Using high-frequency data, we provide network-based evidence of asymmetric volatility connectedness among a wide range of commodities in the time-frequency domain.…”

“…This carries a great deal of economic relevance not only for formulating portfolio strategies and mitigating investor risk, but also for devising and implementing appropriate policy initiatives to stabilize the markets. Also, Křehlík & Baruník (2017) suggest that decomposing volatility into positive and negative components allows us to elucidate the commodity connectedness in a detailed manner that is potentially revealing and can be used for trading, portfolio formation, risk preference, and policy framing. Likewise, the relevance of time-frequency analysis of asymmetric spillovers emanates from the fact that different economic agents operate at different investment horizons -expressed in trading frequencies -that are associated with various types of investors, trading tools, and strategies that correspond to these various trading frequencies (Gencay et al, 2010;Conlon et al, 2016;Bredin et al, 2017).…”