News or Noise? Signal Extraction Can Generate Volatility Clusters From IID Shocks
AbstractWe 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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Bibliographic InfoPaper provided by Florida International University, Department of Economics in its series Working Papers with number 0304.
Length: 38 pages
Date of creation: Nov 2003
Date of revision:
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; Dynamic Treatment Effect Models &bull Diffusion Processes
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-15 (All new papers)
- NEP-CFN-2005-10-15 (Corporate Finance)
- NEP-ETS-2005-10-15 (Econometric Time Series)
- NEP-FMK-2005-10-15 (Financial Markets)
- NEP-MAC-2005-10-15 (Macroeconomics)
- NEP-RMG-2005-10-15 (Risk Management)
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