Investigating the leverage effect in commodity markets with a recursive estimation approach |
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| Institution: | 1. IPAG Business School, IPAG Lab, 184 Boulevard Saint-Germain, 75006 Paris, France;2. Lombard Odier Asset Management, and University Paris 1 (CES), France;1. University of Evry, 2, rue Facteur Cheval, 91025 Evry, France;2. ESSCA – School of Management, Strategy and Management, 55 quai Alphonse Le Gallo, 92513 Boulogne-Billancourt Cedex, France;3. University of Minho, Department of Economics and Economic Policies Research Unit (NIPE), Campus of Gualtar, 4710-057 Braga, Portugal;4. London School of Economics, LSE Alumni Association, Houghton Street, London WC2 2AE, United Kingdom;1. Department of Business and Economics, Ursinus College, 601 East Main Street, Collegeville, PA 19426, United States;2. Department of Economics, Fordham University, 113 West 60th Street, NY, NY 10023, United States;1. University of Evry, France;2. ESSCA School of Management, France;1. Department of Economics, University of Calgary, Calgary, Alberta T2N 1N4, Canada;2. Department of Economics and Finance, University of South Alabama, Mobile, AL 36688, United States |
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| Abstract: | 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). |
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| Keywords: | Leverage effect Commodities Mixture of normal distribution Recursive estimation EGARCH |
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