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Business Cycle and Stock Market Volatility: A Particle Filter Approach

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  • Casarin, Roberto
  • Trecroci, Carmine

Abstract

The recent observed decline of business cycle variability suggests that broad macroeconomic risk may have fallen as well. This may in turn have some impact on equity risk premia. We investigate the latent structures in the volatilities of the business cycle and stock market valuations by estimating a Markov switching stochastic volatility model. We propose a sequential Monte Carlo technique for the Bayesian inference on both the unknown parameters and the latent variables of the hidden Markov model. Sequential importance sampling is used for filtering the latent variables and kernel estimator with a multiple-bandwidth is employed to reconstruct the parameter posterior distribution. We find that the switch to lower variability has occurred in both business cycle and stock market variables along similar patterns.

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Bibliographic Info

Paper provided by Paris Dauphine University in its series Economics Papers from University Paris Dauphine with number 123456789/6830.

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Date of creation: 2006
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Publication status: Published in Cahiers du CEREMADE, 2006
Handle: RePEc:dau:papers:123456789/6830

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Keywords: Markov Switching; Stochastic Volatility; Business Cycle; Equity Market; Particle Filters; Sequential Monte Carlo;

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  1. Margaret M. McConnell & Gabriel Perez Quiros, 1998. "Output fluctuations in the United States: what has changed since the early 1980s?," Staff Reports 41, Federal Reserve Bank of New York.
  2. Watson, Mark W, 1994. "Business-Cycle Durations and Postwar Stabilization of the U.S. Economy," American Economic Review, American Economic Association, vol. 84(1), pages 24-46, March.
  3. Richard Clarida & Jordi Gali & Mark Gertler, 1997. "Monetary Policy Rules in Practice: Some International Evidence," NBER Working Papers 6254, National Bureau of Economic Research, Inc.
  4. Martin Lettau, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, 06.
  5. Francesco Menoncin & Rosella Nicolini, 2005. "The optimal behaviour of firms facing stochastic costs," Working Papers ubs0501, University of Brescia, Department of Economics.
  6. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.
  7. Monica Billio & Roberto Casarin & Domenico Sartore, 2007. "Bayesian Inference on Dynamic Models with Latent Factors," Working Papers 2007_34, Department of Economics, University of Venice "Ca' Foscari".
  8. Lettau, Martin & Ludvigson, Sydney & Wachter, Jessica, 2006. "The Declining Equity Premium: What Role Does Macroeconomic Risk Play?," CEPR Discussion Papers 5519, C.E.P.R. Discussion Papers.
  9. Michele Polo & Carlo Scarpa, 2003. "Entry Without Competition," Working Papers 245, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  10. Francesco Menoncin & Marco Tronzano, 2007. "Optimal Real Exchange Rate Targeting. A Stochastic Analysis," Revue économique, Presses de Sciences-Po, vol. 58(4), pages 807-840.
  11. Michele Moretto & Paola Valbonesi, . "Dynamic Firm Regulation with Endogenous Profit-Sharing," Working Papers ubs0410, University of Brescia, Department of Economics.
  12. Lior Menzly & Tano Santos & Pietro Veronesi, 2004. "Understanding Predictability," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 1-47, February.
  13. Nicolas Chopin, 2001. "Sequential Inference and State Number Determination for Discrete State-Space Models through Particle Filtering," Working Papers 2001-34, Centre de Recherche en Economie et Statistique.
  14. Chiara Dalla Nogare & Roberto Ricciuti, . "Chief Executives' Term Limits and Fiscal Policy Choices: International Evidence," Working Papers ubs0411, University of Brescia, Department of Economics.
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