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News or Noise? Signal Extraction Can Generate Volatility Clusters From IID Shocks

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Author Info
Prasad Bidarkota () (Department of Economics, Florida International University)
J. Huston McCulloch (Department of Economics, Ohio State University)

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Abstract

We develop a framework in which information about firm value is noisily observed. Investors are then faced with a signal extraction problem. Solving this would enable them to probabilistically infer the fundamental value of the firm and, hence, price its stocks. If the innovations driving the fundamental value of the firm and the noise that obscures this fundamental value in observed data come from non-Gaussian thick-tailed probability distributions, then the implied stock returns could exhibit volatility clustering. We demonstrate the validity of this effect with a simulation study.

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File URL: http://www.fiu.edu/orgs/economics/wp2003/03-04.pdf
File Format: application/pdf
File Function: First version, 2003
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Publisher Info
Paper provided by Florida International University, Department of Economics in its series Working Papers with number 0304.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 38 pages
Date of creation: Nov 2003
Date of revision:
Handle: RePEc:fiu:wpaper:0304

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Related research
Keywords: stock returns; volatility clusters; GARCH processes; signal extraction; thick-tailed distributions; simulations;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  3. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56. [Downloadable!] (restricted)
    Other versions:
  4. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290. [Downloadable!] (restricted)
    Other versions:
  5. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290. [Downloadable!] (restricted)
  6. Groenendijk, Patrick A. & Lucas, Andre & de Vries, Casper G., 1995. "A note on the relationship between GARCH and symmetric stable processes," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 253-264, September. [Downloadable!] (restricted)
  7. Liu, Shi-Miin & Brorsen, B Wade, 1995. "Maximum Likelihood Estimation of a Garch-Stable Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(3), pages 273-85, July-Sept. [Downloadable!] (restricted)
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  9. Prasad V. Bidarkota, 2003. "Do Fluctuations in U.S. Inflation Rates Reflect Infrequent Large Shocks or Frequent Small Shocks?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 765-771, 04. [Downloadable!] (restricted)
  10. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May. [Downloadable!] (restricted)
  11. Ghose, Devajyoti & Kroner, Kenneth F., 1995. "The relationship between GARCH and symmetric stable processes: Finding the source of fat tails in financial data," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 225-251, September. [Downloadable!] (restricted)
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