Investigating the leverage effect in commodity markets with a recursive estimation approach
Julien Chevallier () and
Florian Ielpo ()
Research in International Business and Finance, 2017, vol. 39, issue PB, 763-778
This paper investigates the presence of the leverage effect in commodities, in comparison with financial markets. The EGARCH model with a Mixture of Normals distribution (EGARCH-MN) is used to capture (i) heavy tails and skewness in the conditional returns, and (ii) leverage effects and time-varying long-term component in the volatility specification. Besides, the estimation strategy relies on an innovative recursive (REC) method, which allows disentangling the leverage effect from the unconditional skewness as an empirical result. When applied to a broadly diversified dataset of assets during 1995–2012, the EGARCH-MN models offers state-of-the-art specifications with leverage and fat-tailed skewed densities, that allow to contrast the specific characteristics of commodities with traditional assets (equities, bonds, FX).
Keywords: Leverage effect; Commodities; Mixture of normal distribution; Recursive estimation; EGARCH (search for similar items in EconPapers)
JEL-codes: C32 C58 G10 Q02 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:39:y:2017:i:pb:p:763-778
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