Analyzing time-varying volatility spillovers between the crude oil markets using a new method

Liu, Tangyong; Gong, Xu

doi:10.1016/j.eneco.2020.104711

Cited by 146 publications

(41 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the most crucial pricing benchmarks in the global crude oil which could be considered as an important criterion for investors. In a recent study, Liu and Gong (2020) confirmed that WTI plays a prominent role in the international crude oil markets. Concomitantly, examining the relationship between the two largest crude oil markets and an inclusive sample of cryptocurrency coins by employing new econometrics models would provide us with deeper insights into the spill-over effects.…”

Section: Empirical Methodology and Datamentioning

confidence: 78%

Financial modelling, risk management of energy instruments and the role of cryptocurrencies

et al. 2020

View full text Add to dashboard Cite

This paper empirically investigates whether cryptocurrencies might have a useful role in financial modelling and risk management in the energy markets. To do so, the causal relationship between movements on the energy markets (specifically the price of crude oil) and the value of cryptocurrencies is analysed by drawing on daily data from April 2013 to April 2019. We find that shocks to the US and European crude oil indices are strongly connected to the movements of most cryptocurrencies. Applying a non-parametric statistic, Transferring Entropy (an econophysics technique measuring information flow), we find that some cryptocurrencies (XEM, DOGE, VTC, XLM, USDT, XRP) can be used for hedging and portfolio diversification. Furthermore, the results reveal that the European crude oil index is a source of shocks on the cryptocurrency market while the US oil index appears to be a receiver of shocks.

show abstract

Section: Empirical Methodology and Datamentioning

confidence: 78%

Financial modelling, risk management of energy instruments and the role of cryptocurrencies

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Naturally, high-frequency forecasts of oil volatility can be incorporated into mixed data sampling (MIDAS) models by policymakers to predict the future path of low frequency real activity and nominal variables, and then accordingly undertake monetary and fiscal policy decisions to counteract the possible recessionary impact on the economy. In this regard, it must be noted that while we concentrate on the WTI oil market, given that oil volatility across the various regional markets are connected with each other ( Liu and Gong, 2020 ), policy authorities around the world should closely monitor the importance of these shocks in determining uncertainty and associated negative impact on their respective domestic economies.…”

Section: Discussionmentioning

confidence: 99%

The predictive power of oil price shocks on realized volatility of oil: A note

et al. 2020

View full text Add to dashboard Cite

This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.

show abstract

“…Following Wen et al (2016), Gong and Lin (2017, 2018b, 2021), Hsu and Murray (2007), Jian et al (2018), Qiu et al (2019), Dai et al (2020), Liu and Gong (2020) and Tang et al (2021), we use the realized volatility (RV) as an agent variable of unobserved market volatility. The weekly realized volatility (

R V_{t}

) is calculated by

R V_{t} = \sum_{i = 1}^{M} r_{t i}^{2}

where

r_{t i}

is the i th daily return in week

t

.…”

Section: Datamentioning

confidence: 99%

Analyzing the frequency dynamics of volatility spillovers across precious and industrial metal markets

Liu

Gong

Lin

2021

Journal of Futures Markets

Self Cite

View full text Add to dashboard Cite

This paper investigates the volatility spillovers across precious and industrial metal markets over the period 1993–2019 based on the DY and BK methods. Results are summarized as follows: (1) while volatility spillovers across industrial metals are higher than across precious metals, the opposite occurs during crisis periods where precious metals cause net volatility spillovers to industrial metals; (2) volatility spillovers of the two metal groups show different dynamics in the short‐, medium‐ and long‐term components, especially in the short‐ and medium‐term components.

show abstract

Analyzing time-varying volatility spillovers between the crude oil markets using a new method

Cited by 146 publications

References 57 publications

Financial modelling, risk management of energy instruments and the role of cryptocurrencies

Financial modelling, risk management of energy instruments and the role of cryptocurrencies

The predictive power of oil price shocks on realized volatility of oil: A note

Analyzing the frequency dynamics of volatility spillovers across precious and industrial metal markets

Contact Info

Product

Resources

About