TY - JOUR
T1 - The Dynamic Relationship Between Gas and Crude Oil Markets and the Causal Impact of US Shale Gas
AU - Ghosh, Sudeshna
AU - Tiwari, Aviral Kumar
AU - Doğan, Buhari
AU - Abakah, Emmanuel Joel Aikins
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - Although the recent debate in energy economics on the importance of oil price indexation versus shale gases suggest that big data can be used in predictive analysis in energy economics, little is known particularly in the context of shale gas and oil price interlinkages. Grounding our investigations in such directions we investigate in this paper the relationship between gas and crude oil markets and the impact US shale gas by employing time-varying causality method by Shi et al. (J Time Ser Anal 39(6):966–987, 2018; J Financ Econom 18(1):158–180, 2020) and cross-quantilogram correlation approach by Han et al. (J Econom 193(1):251–270, 2016). In particular, as a representative of the crude oil market, we use OPEC oil; WTI; Crude oil Oman; Crude oil Dubai while for the gas market, we use natural gas prices of UK NBP (National Balancing Point), NYMEX HH (Henry Hub) and US shale gas prices. Data period is from 11th January 2013 to 8th September 2020. We find significant negative spillovers from crude oil markets to natural gas markets particularly during moderate market conditions. The results suggest crucial implications in energy economics literature, to diversify assets to hedge against risks. We further find strong causality association between oil markets, natural gas markets and further oil markets and shale gas markets. Our findings describe that aftermath of the shale-gas boom the predictability nexus between oil and natural gas increased. Once we condition for shale gas the significant negative spill overs from oil markets to natural gas markets increases in the long-run. We suggest important policy prescriptions which have interconnected market repercussions.
AB - Although the recent debate in energy economics on the importance of oil price indexation versus shale gases suggest that big data can be used in predictive analysis in energy economics, little is known particularly in the context of shale gas and oil price interlinkages. Grounding our investigations in such directions we investigate in this paper the relationship between gas and crude oil markets and the impact US shale gas by employing time-varying causality method by Shi et al. (J Time Ser Anal 39(6):966–987, 2018; J Financ Econom 18(1):158–180, 2020) and cross-quantilogram correlation approach by Han et al. (J Econom 193(1):251–270, 2016). In particular, as a representative of the crude oil market, we use OPEC oil; WTI; Crude oil Oman; Crude oil Dubai while for the gas market, we use natural gas prices of UK NBP (National Balancing Point), NYMEX HH (Henry Hub) and US shale gas prices. Data period is from 11th January 2013 to 8th September 2020. We find significant negative spillovers from crude oil markets to natural gas markets particularly during moderate market conditions. The results suggest crucial implications in energy economics literature, to diversify assets to hedge against risks. We further find strong causality association between oil markets, natural gas markets and further oil markets and shale gas markets. Our findings describe that aftermath of the shale-gas boom the predictability nexus between oil and natural gas increased. Once we condition for shale gas the significant negative spill overs from oil markets to natural gas markets increases in the long-run. We suggest important policy prescriptions which have interconnected market repercussions.
KW - Big data
KW - Cross-quantile correlation
KW - Crude oil market
KW - Energy economics
KW - Natural gas market
KW - Time-varying causality
UR - http://www.scopus.com/inward/record.url?scp=85164122131&partnerID=8YFLogxK
U2 - 10.1007/s10614-023-10415-1
DO - 10.1007/s10614-023-10415-1
M3 - Article
AN - SCOPUS:85164122131
SN - 0927-7099
JO - Computational Economics
JF - Computational Economics
ER -