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Nearest comoment estimation with unobserved factors

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  • Boudt, Kris
  • Cornilly, Dries
  • Verdonck, Tim

Abstract

We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study confirms the large gains in accuracy compared to the traditional sample comoments. The empirical usefulness of the novel framework is shown in applications to portfolio allocation under non-Gaussian objective functions and to the extraction of factor loadings in a dataset with mental ability scores.

Suggested Citation

  • Boudt, Kris & Cornilly, Dries & Verdonck, Tim, 2020. "Nearest comoment estimation with unobserved factors," Journal of Econometrics, Elsevier, vol. 217(2), pages 381-397.
    • Handle: RePEc:eee:econom:v:217:y:2020:i:2:p:381-397
    DOI: 10.1016/j.jeconom.2019.12.009
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    1. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    2. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    3. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    4. Eric Jondeau & Emmanuel Jurczenko & Michael Rockinger, 2018. "Moment Component Analysis: An Illustration With International Stock Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 576-598, October.
    5. 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.
    6. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    7. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    8. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    9. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55, January.
    10. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
    11. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    12. Bonhomme, Stphane & Robin, Jean-Marc, 2009. "Consistent noisy independent component analysis," Journal of Econometrics, Elsevier, vol. 149(1), pages 12-25, April.
    13. Stoyan Stoyanov & Svetlozar Rachev & Frank Fabozzi, 2013. "Sensitivity of portfolio VaR and CVaR to portfolio return characteristics," Annals of Operations Research, Springer, vol. 205(1), pages 169-187, May.
    14. Stéphane Bonhomme & Jean-Marc Robin, 2009. "Consistent Noisy Independent Component Analysis," Post-Print hal-00642732, HAL.
    15. Lionel Martellini & Volker Ziemann, 2010. "Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection," Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1467-1502, April.
    16. repec:hal:spmain:info:hdl:2441/eu4vqp9ompqllr09j01si09a2 is not listed on IDEAS
    17. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    18. Matteo Luciani & David Veredas, 2015. "Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 163-176, April.
    19. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    20. 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.
    21. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    22. Stéphane Bonhomme & Jean-Marc Robin, 2009. "Consistent Noisy Independent Component Analysis," PSE-Ecole d'économie de Paris (Postprint) hal-00642732, HAL.
    23. repec:hal:wpspec:info:hdl:2441/eu4vqp9ompqllr09j01si09a2 is not listed on IDEAS
    24. Karlis, Dimitris, 2002. "An EM type algorithm for maximum likelihood estimation of the normal-inverse Gaussian distribution," Statistics & Probability Letters, Elsevier, vol. 57(1), pages 43-52, March.
    25. Ab Mooijaart, 1985. "Factor analysis for non-normal variables," Psychometrika, Springer;The Psychometric Society, vol. 50(3), pages 323-342, September.
    26. Campbell Harvey & John Liechty & Merrill Liechty & Peter Muller, 2010. "Portfolio selection with higher moments," Quantitative Finance, Taylor & Francis Journals, vol. 10(5), pages 469-485.
    27. Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
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    Cited by:

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    2. 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).

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    More about this item

    Keywords

    Higher-order multivariate moments; Latent factor model; Minimum distance estimation; Risk assessment; Structural equation modelling;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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