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Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks

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Author Info

  • Gabriele Fiorentini

    ()
    (University of Florence and The Rimini Centre for Economics Analysis, Italy.)

  • Giorgio Calzolari

    ()
    (University of Florence)

  • Enrique Sentana

    ()
    (CEMFI, Spain)

Abstract

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters. We also propose alternative indirect estimators for large-scale models, and explain how to apply our procedures to many other dynamic latent variable models. We analyse the small sample behaviour of our indirect estimators and several likelihood-based procedures through an extensive Monte Carlo experiment with empirically realistic designs. Finally, we apply our procedures to weekly returns on the Dow 30 stocks.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 40-07.

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Date of creation: Jul 2007
Date of revision: Jul 2007
Handle: RePEc:rim:rimwps:40-07

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Keywords: ARCH; Idiosyncratic risk; Inequality constraints; Kalman filter; Sequential estimators; Simulation estimators; Volatility.;

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Citations

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Cited by:
  1. Sirio Aramonte & Marius del Giudice Rodriguez & Jason J. Wu, 2011. "Dynamic factor value-at-risk for large, heteroskedastic portfolios," Finance and Economics Discussion Series 2011-19, Board of Governors of the Federal Reserve System (U.S.).
  2. Gabriele Fiorentini & Giorgio Calzolari & Enrique Sentana, 2007. "Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks," Working Paper Series 40-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  3. Gabriele Fiorentini & Enrique Sentana, 2012. "Tests For Serial Dependence In Static, Non-Gaussian Factor Models," Working Papers wp2012_1211, CEMFI.
  4. Araújo, Fabio & Issler, João Victor, 2011. "A Stochastic discount factor approach to asset pricing using panel data asymptotics," Economics Working Papers (Ensaios Economicos da EPGE) 717, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  5. Gabriele Fiorentini & Enrique Sentana, 2010. "Dynamic Specification Tests for Static Factor Models," Working Paper Series 04_10, The Rimini Centre for Economic Analysis.
  6. Anna Gottard & Giorgio Calzolari, 2014. "Alternative estimating procedures for multiple membership logit models with mixed effects: indirect inference and data cloning," Econometrics Working Papers Archive 2014_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  7. Francesco Audrino & Fulvio Corsi & Kameliya Filipova, 2010. "Bond Risk Premia Forecasting: A Simple Approach for Extracting¨Macroeconomic Information from a Panel of Indicators," University of St. Gallen Department of Economics working paper series 2010 2010-09, Department of Economics, University of St. Gallen.
  8. Stelios Arvanitis & Antonis Demos, 2014. "On the Validity of Edgeworth Expansions and Moment Approximations for Three Indirect Inference Estimators," DEOS Working Papers 1406, Athens University of Economics and Business.

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