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Testing for persistence in stock returns with GARCH-stable shocks

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  • Prasad Bidarkota
  • J Huston Mcculloch

Abstract

We investigate persistence in CRSP monthly excess stock returns, using a state space model with stable disturbances. The non-Gaussian state space model with volatility persistence is estimated by maximum likelihood, using the optimal filtering algorithm given by Sorenson and Alspach (1971 Automatica 7 465-79). The conditional distribution has a stable α of 1.89, and normality is strongly rejected even after accounting for GARCH. However, stock returns do not contain a significant mean-reverting component. The optimal predictor is the unconditional expectation of the series, which we estimate to be 9.8% per annum.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

Volume (Year): 4 (2004)
Issue (Month): 3 ()
Pages: 256-265

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Handle: RePEc:taf:quantf:v:4:y:2004:i:3:p:256-265

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Cited by:
  1. Jonathan B. Hill, 2005. "On Tail Index Estimation Using Dependent,Heterogenous Data," Working Papers 0512, Florida International University, Department of Economics.
  2. Hill, Jonathan B., 2010. "On Tail Index Estimation For Dependent, Heterogeneous Data," Econometric Theory, Cambridge University Press, vol. 26(05), pages 1398-1436, October.
  3. Prasad Bidarkota & J. Huston McCulloch, 2003. "News or Noise? Signal Extraction Can Generate Volatility Clusters From IID Shocks," Working Papers 0304, Florida International University, Department of Economics.
  4. J. Huston McCulloch & Prasad V. Bidarkota, 2003. "Signal Extraction can Generate Volatility Clusters," Computing in Economics and Finance 2003 59, Society for Computational Economics.
  5. J. Huston McCulloch & Prasad V. Bidarkota, 2002. "Signal Extraction Can Generate Volatility Clusters From IID Shocks," Working Papers 02-04, Ohio State University, Department of Economics.
  6. Prasad V. Bidarkota & Brice V. Dupoyet & J. Huston McCulloch, 2005. "Asset Pricing with Incomplete Information under Stable Shocks," Working Papers 0514, Florida International University, Department of Economics.
  7. Khurshid Kiani, 2010. "Predictable Signals in Excess Returns: Evidence from Non-Gaussian State Space Models," Economics Bulletin, AccessEcon, vol. 30(2), pages 1217-1232.
  8. Khurshid M. Kiani, 2006. "Predictability in Stock Returns in an Emerging Market: Evidence from KSE 100 Stock Price Index," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(3), pages 369-381.
  9. José Curto & José Pinto & Gonçalo Tavares, 2009. "Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions," Statistical Papers, Springer, vol. 50(2), pages 311-321, March.
  10. KIANI, Khurshid M., 2007. "Determination Of Volatility And Mean Returns: An Evidence From An Emerging Stock Market," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 4(1), pages 103-118.
  11. Bidarkota, Prasad V. & Dupoyet, Brice V. & McCulloch, J. Huston, 2009. "Asset pricing with incomplete information and fat tails," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1314-1331, June.
  12. Prasad Bidarkota & Khurshid M. Kiani, 2004. "No Predictable Components in G7 Stock Returns," Working Papers 0416, Florida International University, Department of Economics.

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