Forecasting stochastic Volatility using the Kalman filter: an application to Canadian Interest Rates and Price-Earnings Ratio

Raymond Théoret and François-Éric Racicot ()

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, we aim at forecasting the stochastic volatility of key financial market variables with the Kalman filter using stochastic models developed by Taylor (1986,1994) and Nelson (1990). First, we compare a stochastic volatility model relying on the Kalman filter to the conditional volatility estimated with the GARCH model. We apply our models to Canadian short-term interest rates. When comparing the profile of the interest rate stochastic volatility to the conditional one, we find that the omission of a constant term in the stochastic volatility model might have a perverse effect leading to a scaling problem, a problem often overlooked in the literature. Stochastic volatility seems to be a better forecasting tool than GARCH(1,1) since it is less conditioned by autoregressive past information. Second, we filter the S&P500 price-earnings(P/E) ratio in order to forecast its value. To make this forecast, we postulate a rational expectations process but our method may accommodate other data generating processes. We find that our forecast is close to a GARCH(1,1) profile.

Keywords: Stochastic volatility; Kalman filter; P/E ratio forecast; Interest rate forecast (search for similar items in EconPapers)
JEL-codes: C13 C19 C49 G12 G31 (search for similar items in EconPapers)
Date: 2010-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Published in Aestimatio. The IEB International Journal of Finance 1 (2010): pp. 1-20

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Related works:
Working Paper: Forecasting stochastic Volatility using the Kalman filter: An Application to Canadian Interest Rates and Price-Earnings Ratio (2011)
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