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A model for vast panels of volatilities

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  • Matteo Luciani

    ()
    (Université Libre de Bruxelles)

  • David Veredas

    ()
    (Université Libre de Bruxelles)

Abstract

Realized volatilities, when observed over time, share the following stylised facts: comovements, clustering, long-memory, dynamic volatility, skewness and heavy-tails. We propose a dynamic factor model that captures these stylised facts and that can be applied to vast panels of volatilities as it does not suffer from the curse of dimensionality. It is an enhanced version of Bai and Ng (2004) in the following respects: i) we allow for longmemory in both the idiosyncratic and the common components, ii) the common shocks are conditionally heteroskedastic, and iii) the idiosyncratic and common shocks are skewed and heavy-tailed. Estimation of the factors, the idiosyncratic components and the parameters is simple: principal components and low dimension maximum likelihood estimations. A Monte Carlo study shows the usefulness of the approach and an application to 90 daily realized volatilities, pertaining to S&P100, from January 2001 to December 2008, evinces, among others, the following findings: i) All the volatilities have long-memory, more than half in the nonstationary range, that increases during financial turmoils. ii) Tests and criteria point towards one dynamic common factor driving the co-movements. iii) The factor has larger long-memory than the assets volatilities, suggesting that long–memory is a market characteristic. iv) The volatility of the realized volatility is not constant and common to all. v) A forecasting horse race against 8 competing models shows that our model outperforms, in particular in periods of stress.

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Bibliographic Info

Paper provided by Banco de Espa�a in its series Banco de Espa�a Working Papers with number 1230.

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Length: 44 pages
Date of creation: Sep 2012
Date of revision:
Handle: RePEc:bde:wpaper:1230

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Keywords: Realized volatilities; vast dimensions; factor models; long–memory; forecasting;

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References

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  1. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, Elsevier, vol. 80(23-24), pages 1806-1813, December.
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  20. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
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Citations

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Cited by:
  1. Dungey, Mardi & Luciani, Matteo & Veredas, David, 2012. "Ranking systemically important financial institutions," Working Papers, University of Tasmania, School of Economics and Finance 15473, University of Tasmania, School of Economics and Finance, revised 21 Nov 2012.
  2. Ezzat, Hassan, 2012. "The Application of GARCH Methods in Modeling Volatility Using Sector Indices from the Egyptian Exchange," MPRA Paper 51584, University Library of Munich, Germany.
  3. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz, Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
  4. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series, National Centre for Econometric Research 80, National Centre for Econometric Research.
  5. Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2013. "Firm Volatility in Granular Networks," NBER Working Papers 19466, National Bureau of Economic Research, Inc.
  6. Bernard Herskovic & Bryan T. Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2014. "The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications," NBER Working Papers 20076, National Bureau of Economic Research, Inc.

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