Evidence of infinite and finite jump processes in commodity futures prices: Crude oil and natural gas

COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis

“…It implies that the mean term, the variance term, and the jump drive commodity prices simultaneously, while if we ignore the jump term, then we cannot accurately fit the price movement pattern. Indeed, several studies have found that jumps in commodity prices affect changes in the variance and mean of commodity prices ( Alqahtani et al, 2021 ; Bouri et al, 2021 ; Cao et al, 2018 ; Liu et al, 2020 ). Exploring the interaction between the COVID-19 outbreak and the commodity price jumps in China is a thought-provoking topic, and offers market participants a better risk management perspective.…”

Relationships between Copper Futures Markets from the Perspective of Jump Diffusion

“…Liu and Xu compared and analyzed the detection level and efficacy of eight different jump test methods using the Monte Carlo analysis method, and found that BN-S non-parametric test methods have more advantages than the TMPV methods when there is a large fluctuation in the sample data [27]. Cao and Guernsey found that the fluctuation of crude oil and natural gas futures prices could be decomposed into an infinite active fluctuation diffusion process and a lower, but larger fluctuation process [28]. Gong et al constructed a generalized doubleexponential distribution jump-diffusion model based on a Markov chain Monte Carlo simulation and found that it could better capture the peak and thick tail characteristics of the rate of return distribution and that the probability of the rise and fall of stock index futures returns and stock index spot returns presented asymmetry [29].…”

Residual Shape Risk on Natural Gas Market with Mixed Jump Diffusion

“…The evaluation of RSR requires modelling of prices of evaluated commodity, in our application natural gas. The literature (Baum et al 2018;Benth et al 2008;Borovkova & Mahakena 2015;Brix et al 2018;Cao et al 2018;Gomez-Valle et al 2017a;Hsu et al 2017;Mason & Wilmot 2014;Mishra & Smyth 2016;Safarov & Atkinson 2017) shows that the gas prices and energy commodities in general have quite complex price distribution as compared to financial assets. Gas prices commonly depart from normality by exhibiting heavy tails and a leptokurtic shape (Benth et al 2008).…”

“…Gas prices commonly depart from normality by exhibiting heavy tails and a leptokurtic shape (Benth et al 2008). They also exhibit jumps (Cao et al 2018;Ficura & Witzany 2016;Mason & Wilmot 2014), a time-varying volatility (Baum et al 2018;Brix et al 2018), and a mean reversion (Brix et al 2018;Hsu et al 2017). Moreover, they are affected by many other factors like storage, weather, seasonality and political events and decisions (Gomez-Valle et al 2018).…”