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Conditionally heteroskedastic factor models with skewness and leverage effects

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  • Dovonon, Prosper

Abstract

Conditional heteroskedasticity, skewness and leverage effects are well known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional moment conditions are proposed allowing inference by the generalized method of moments (GMM). The model is also shown to be closed under temporal aggregation. An application to daily excess returns on sectorial indices from the U.K. stock market provides a strong evidence for dynamic conditional skewness and leverage with a sharp efficiency gain resulting from accounting for both effects. The estimated volatility persistence from the proposed model is lower than that estimated from models that rule out such effects. I also find that the longer the returns’ horizon, the fewer conditionally heteroskedastic factors may be required for suitable modeling and the less strong is the evidence for dynamic leverage. Some of these results are in line with the main findings of Harvey and Siddique (1999) and Jondeau and Rockinger (2003), namely that accounting for conditional skewness impacts the persistence in the conditional variance of the return process.

Suggested Citation

  • Dovonon, Prosper, 2008. "Conditionally heteroskedastic factor models with skewness and leverage effects," MPRA Paper 40206, University Library of Munich, Germany, revised Feb 2012.
  • Handle: RePEc:pra:mprapa:40206
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    Citations

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    Cited by:

    1. Catherine Doz & Éric Renault, 2004. "Conditionally Heteroskedastic Factor Models: Identification and Instrumental Variables Estimation," CIRANO Working Papers 2004s-37, CIRANO.
    2. Prosper Dovonon, 2013. "Conditionally Heteroskedastic Factor Models With Skewness And Leverage Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1110-1137, November.
    3. Huang, Shih-Feng & Tu, Ya-Ting, 2014. "Asymptotic distribution of the EPMS estimator for financial derivatives pricing," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 129-145.
    4. Catherine Doz & Eric Renault, 2004. "Conditionaly Heteroskedastic Factor Models : Identificationand Instrumental variables Estmation," THEMA Working Papers 2004-13, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    5. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," The School of Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    6. Kuruppuarachchi, Duminda & Premachandra, I.M., 2016. "Information spillover dynamics of the energy futures market sector: A novel common factor approach," Energy Economics, Elsevier, vol. 57(C), pages 277-294.

    More about this item

    Keywords

    Factor models; conditional heteroskedasticity; conditional leverage; conditional skewness; temporal aggregation; generalized method of moments;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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