Francois-Éric Racicot () (Département des sciences administratives, Université du Québec (Outaouais)) Raymond Théoret () (Département de stratégie des affaires, Université du Québec (Montréal))
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The popularity of Kalman filter is increasing in financial studies, notably to estimate diffusion processes. In this article, we show how we can use it to forecast the volatility of returns and the price-earnings ratio of the S&P500. The Kalman filter is consequently very versatile when variables, as volatility or forecasted price-earnings ratio, are unobserved. But the forecaster must use his judgment when he uses the Kalman filter. An error of specification in the model may give way to very biased forecasts.
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Paper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number
UQO-DSA-wp0312005.
Length: 22 pages Date of creation: 01 Aug 2005 Date of revision: Handle: RePEc:pqs:wpaper:0312005
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005.
"Volatility Forecasting,"
NBER Working Papers
11188, National Bureau of Economic Research, Inc.
[Downloadable!] (restricted)
Other versions:
Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005.
"Volatility Forecasting,"
PIER Working Paper Archive
05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
[Downloadable!]
Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005.
"Volatility Forecasting,"
CFS Working Paper Series
2005/08, Center for Financial Studies.
[Downloadable!]
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