期刊名称：AESTIMATIO : the IEB International Journal of Finance
印刷版ISSN：2173-0164
出版年度：2010
期号：1
页码：28-47
出版社：Instituto de Estudios Bursátiles
摘要：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 omis- sion 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 con- ditioned by autoregressive past information. Second, we filter the S&P500 price-earn- ings (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 gener- ating processes. We find that our forecast is close to a GARCH(1,1) profile.
关键词：Stochastic volatility; Kalman filter; P/E ratio forecast; Interest rate forecast.
