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Independent Factor Autoregressive Conditional Density Model

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  • Alexios Ghalanos
  • Eduardo Rossi
  • Giovanni Urga

Abstract

In this article, we propose a novel Independent Factor Autoregressive Conditional Density (IFACD) model able to generate time-varying higher moments using an independent factor setup. Our proposed framework incorporates dynamic estimation of higher comovements and feasible portfolio representation within a non-elliptical multivariate distribution. We report an empirical application, using returns data from 14 MSCI equity index iShares for the period 1996 to 2010, and we show that the IFACD model provides superior VaR forecasts and portfolio allocations with respect to the Conditionally Heteroskedastic Independent Component Analysis of Generalized Orthogonal (CHICAGO) and DCC models.

Suggested Citation

  • Alexios Ghalanos & Eduardo Rossi & Giovanni Urga, 2015. "Independent Factor Autoregressive Conditional Density Model," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 594-616, May.
  • Handle: RePEc:taf:emetrv:v:34:y:2015:i:5:p:594-616
    DOI: 10.1080/07474938.2013.808561
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    2. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," Journal of Banking & Finance, Elsevier, vol. 126(C).
    3. Lassance, Nathan & Vrins, Frédéric, 2019. "Robust portfolio selection using sparse estimation of comoment tensors," LIDAM Discussion Papers LFIN 2019007, Université catholique de Louvain, Louvain Finance (LFIN).
    4. Wolfgang Karl Härdle & David Kuo Chuen Lee & Sergey Nasekin & Alla Petukhina, 2018. "Tail Event Driven ASset allocation: evidence from equity and mutual funds’ markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(1), pages 49-63, January.
    5. Basher, Syed Abul & Sadorsky, Perry, 2016. "Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH," Energy Economics, Elsevier, vol. 54(C), pages 235-247.
    6. Umar, Zaghum & Hussain Shahzad, Syed Jawad & Kenourgios, Dimitris, 2019. "Hedging U.S. metals & mining Industry's credit risk with industrial and precious metals," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    7. Boudt, Kris & Cornilly, Dries & Verdonck, Tim, 2020. "Nearest comoment estimation with unobserved factors," Journal of Econometrics, Elsevier, vol. 217(2), pages 381-397.
    8. Boudt, Kris & Lu, Wanbo & Peeters, Benedict, 2015. "Higher order comoments of multifactor models and asset allocation," Finance Research Letters, Elsevier, vol. 13(C), pages 225-233.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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