Leverage effect in energy futures

Abstract

We propose a comprehensive treatment of the leverage effect, i.e. the relationship between returns and volatility of a specific asset, focusing on energy commodities futures, namely Brent and WTI crude oils, natural gas and heating oil. After estimating the volatility process without assuming any specific form of its behavior, we find the volatility to be longterm dependent with the Hurst exponent on a verge of stationarity and non-stationarity. Bypassing this using by using the detrended cross-correlation and the detrending movingaverage cross-correlation coefficients, we find the standard leverage effect for both crude oil. For heating oil, the effect is not statistically significant, and for natural gas, we find the inverse leverage effect. Finally, we also show that none of the effects between returns and volatility is detected as the long-term cross-correlated one. These findings can be further utilized to enhance forecasting models and mainly in the risk management and portfolio diversification.

Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

“…Kanamura (2009) examined the same in natural gas prices, while Knittel and Roberts (2005) found leverage in North California hourly electricity prices. Kristoufek (2014) examines Brent, WTI, natural gas and heating oil and finds that the leverage effect is exhibited by natural gas while it is not significant for heating oil; Brent crude oil and WTI exhibit standard leverage effect. Nomikos and Andriosopoulos (2012) employed the EGARCH model to incorporate time varying volatility and found asymmetric leverage effect for gasoline, natural gas, propane, and Interconnection Electricity Firm On Peak Price Index, whereas standard leverage effect was observed for WTI, heating oil and heating oil-WTI crack spread.…”

“…Cheong (2009) examines WTI and Brent crude oil markets for the evidence of persistence using GARCH specification models and finds that the persistence in WTI is more than Brent crude oil. Kristoufek (2014) examines Brent, WTI, natural gas and heating oil and observes that the volatility is long-term dependent. Alvarez-Ramirez et al (2002); Serletis and Andreadis (2004) also observed persistence in crude oil volatility.…”

Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets

“…In addition, Kristoufek (2014) finds that the long-memory effect is important for the crude oil price volatility. He also observes that significant leverage effect on the crude oil market that is 2 See also Serra (2013) for the recent survey of the related literature on biofuels-related volatility.…”

Quantifying cross-correlation between Ibovespa and Brazilian blue-chips: The DCCA approach

“…The ρ DCCA coefficient is defined as the ratio between the detrended covariance function (by DCCA method [3]) and the detrended variance function (by DFA method [4]), and can be considered as an evolution of the DCCA method, widely used in recent years [5][6][7][8][9]. The ρ DCCA was applied in meteorology [10], in time series of homicide and attempted homicide [11], in economy [12][13][14][15][16][17], among others. Furthermore, the ρ DCCA was compared with Pearson's correlation coefficient and has been shown more efficient [18], mainly for two non-stationary time series.…”