Théoret, Raymond and Racicot, François-Éric (2010): "Forecasting stochastic Volatility using the Kalman filter: an application to Canadian Interest Rates and Price-Earnings Ratio". Published in: Aestimatio. The IEB International Journal of Finance No. 1 (December 2010): pp. 1-20.
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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.
Item Type: | MPRA Paper |
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Original Title: | "Forecasting stochastic Volatility using the Kalman filter: an application to Canadian Interest Rates and Price-Earnings Ratio" |
English Title: | "Forecasting stochastic Volatility using the Kalman filter: An Application to Canadian Interest Rates and Price-Earnings Ratio" |
Language: | English |
Keywords: | Stochastic volatility, Kalman filter, P/E ratio forecast, Interest rate forecast |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates G - Financial Economics > G3 - Corporate Finance and Governance > G31 - Capital Budgeting ; Fixed Investment and Inventory Studies ; Capacity C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C19 - Other C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C49 - Other |
Item ID: | 35911 |
Depositing User: | IEB Research Department |
Date Deposited: | 17 Jan 2012 07:20 |
Last Modified: | 26 Sep 2019 10:20 |
References: | Andersen, T. G. and Benzoni, L. (2010), Stochastic volatility, WP[2010-10], CREATES. Andersen, T. G., Bollerslev, T., Christoffersen, P.F. and Diebold, F. (2005). Volatility Forecasting, WP [05-011], PIER. Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities, Journal of Political Economy, 81, pp. 637-654. Fornari, F. and Mele, A. (2006). Approximating volatility diffusions with CEV-ARCH models, Journal of Economic Dynamics & Control, 30, pp. 931-966. Gregoriou, G. N. (2009) Book Review of: The Econometric Analysis of Hedge Fund Returns: An Errorsin- Variables Perspective, Journal of Wealth Management, 12, pp. 138-139. Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge (UK). James, J. and Webber N. (2000). Interest Rate Modelling, Wiley, New York. Mills, T. C. (1999). The Econometric Modelling of Financial Time Series, 2nd edition, Cambridge University Press, Cambridge (UK). Nelson, D. (1990). ARCH models as diffusion approximations, Journal of Econometrics, 45, pp. 7-38. Nelson, D. B. and Foster, D. P. (1994). Asymptotic Filtering Theory for Univariate ARCH models, Econometrica, 62, pp. 1-41. Reprinted in Rossi (1996), chap. 8. Racicot, F. É. and Théoret, R. (2001). Traité d’économétrie financière, Presses de l’Université du Québec(PUQ), Québec. Racicot, F.-É. and Théoret, R. (2006). Finance computationnelle et gestion des risques, 2ième édition, Presses de l’Université du Québec (PUQ), Québec. Racicot, F. É. and Théoret, R. (2007a). A study of dynamic market strategies of hedge funds using the Kalman filter, Journal of Wealth Management, 10, pp. 94-106. Racicot, F. É. and Théoret, R. (2007b). Book Review of: Option Pricing Models & Volatility Using Excel- VBA (by F. D. Rouah and G. Vainberg), Journal of Derivatives & Hedge Funds, 13, pp. 181-183. Racicot, F. É. and Théoret, R. (2008). The Econometric Analysis of Hedge Fund Returns: An Errorsin- Variables Perspective, Netbiblo, A Coruña. Racicot, F.É. and Théoret, R. (2009). Modeling hedge fund retunrs using the Kalman filter: An errors-invariables perspective, WP[2009-06], Chaire d’information financière et organisationnelle, ESG-UQAM. Racicot, F. É. and Théoret, R. (2010). Hedge fund returns, Kalman filter, and errors-in-variables, Atlantic Economic Journal, 38, pp. 377-378. Rouah, F. D. and Vainberg, G. (2007). Option Pricing Models & Volatility Using Excel-VBA, Wiley, New York. Rossi, P. E. (1996). Modelling Stock Market Volatility, Academic Press, San Diego, California. Taylor, S. J. (1986). Modelling Financial Time Series, Wiley, New York. Taylor, S. J. (1994). Modelling stochastic volatility: A review and comparative studies, Mathematical Finance, 4, pp. 183-204. Théoret, R., Rostan, P. and Zabré, L. (2004). Difficultés de calculer les cotes des swaps de volatilité, Insurance and Risk Management, 72, pp. 301-320. Wang, P. (2003). Financial Econometrics: Methods and Models, Routledge, London. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/35911 |