Testing for persistence in stock returns with GARCH-stable shocks
AbstractWe 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 InfoArticle provided by Taylor & Francis Journals in its journal Quantitative Finance.
Volume (Year): 4 (2004)
Issue (Month): 3 ()
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- J. Huston McCulloch & Prasad V. Bidarkota, 2003. "Signal Extraction can Generate Volatility Clusters," Computing in Economics and Finance 2003 59, Society for Computational Economics.
- 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.
- 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.
- 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.
- Jonathan B. Hill, 2005. "On Tail Index Estimation Using Dependent,Heterogenous Data," Working Papers 0512, Florida International University, Department of Economics.
- 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.
- Prasad Bidarkota & Khurshid M. Kiani, 2004. "No Predictable Components in G7 Stock Returns," Working Papers 0416, Florida International University, Department of Economics.
- Jonathan B. Hill, 2005.
"On Tail Index Estimation for Dependent, Heterogenous Data,"
0505005, EconWPA, revised 27 May 2005.
- Hill, Jonathan B., 2010. "On Tail Index Estimation For Dependent, Heterogeneous Data," Econometric Theory, Cambridge University Press, vol. 26(05), pages 1398-1436, October.
- 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.
- 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.
- Khurshid Kiani, 2010. "Predictable Signals in Excess Returns: Evidence from Non-Gaussian State Space Models," Economics Bulletin, AccessEcon, vol. 30(2), pages 1217-1232.
- 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.
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