Abstract
This article shows how state space models and the Kalman filter can advantageously be used to estimate time-varying factor sensitivities. The common alternative to stochastic parameter regression, rolling regression, is shown to be biased in situations where the actual factor sensitivities are not constant but vary over time. We propose a state space approach estimating time-varying factor sensitivities. The models are applied to typical asset management tasks such as exposure monitoring, performance attribution, style analysis and portfolio tracking. The assets used in this study are individual stocks, stock portfolios and currencies. Typical factors are economic ones (such as interest and exchange rates, commodity prices for instance), statistical ones (obtained by factor analysis) and style indices (e.g. growth/value and large/small capitalisation stock indices). The models are compared to traditional rolling estimation both in terms of reliability and flexibility. Sensitivity instability tests are also discussed. The models are applied to both European and American market data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bentz Yves, “Identifying and Modelling Conditional Factor Sensitivities: an Application to Equity Investment Management”, Doctoral Thesis, London Business School, 1997
Blume Marchall, “Betas and their Regression Tendencies”, Journal of Finance, 10, No.3 (June 1975), pp. 785–795
Bos Paul and Newbold Theodore, “An empirical investigation of the possibility of stochastic systematic risk in the market model”, Journal of Business, 57 (1984), pp.35–41.
Chow G. C., “Tests of Equality between sets of Coefficients in two Linear Regressions”, Econometrica 28, (1960), pp. 591–605
Cooley T.F. and Prescott E.C., “Varying parameter regression: A theory and some applications”, Annals of Economic and Social Measurement, 2, pp. 463–474, 1973
Cuthbertson K. and Taylor M. P., “Anticipated and unanticipated variables in the demand for M1 in the UK”, Manchester School, 57 (4), 319–39.
Doran Howard, E., “Constraining Kalman filter and smoothing estimates to satisfy timevarying restrictions”, The Review of Economics and Statistics, August 1991, pp.568–572
Elton Edwin J. and Gruber Martin, J., Modern Portfolio Theory and Investment Analysis, John Wiley and Sons, Inc, 1995
Fabozzi Frank, J. and Francis J.C., “Beta as a random coefficient”, Journal of financial and Quantitative Analysis, 13 (1978), pp. 101–116
Goldfeld S.M. and Quandt R.E., “The estimation of structural shifts by switching regressions”, Annals of Economic and Social Measurement, 2, pp. 475–485, 1973
Hansen Bruce, E., “Testing for Parameter Instability in Linear Models”, Journal of Policy Modeling, 14, No.4, (1992), pp. 517–533
Harvey Andrew C., Henry S. G. B., Peters S. and Wren-Lewis S., “Stochastic trends in dynamic regression models: an application to the employment-output equations”, Economic Journal, 96 (1986), pp. 975–85.
Jazwinski Andrew H., Stochastic Processes and Filtering Theory, Academic Press, 1970
Lintner John, “The valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets”, Review of Economics and Statistics, 47:13–37 February 1965
McGee V.E. and Carlton T.W., “Piecewise regression”, Journal of the American Statistical Association, 65, pp. 1109–1124, 1970
Mossin Jan, “Equilibrium in a Capital Asset Market”, Econometrica, October 1966
Nyblom J., “Testing for the Constancy of Parameters over Time”, Journal of the American Statistical Association, 84, 1989, pp.223–230
Ohlson J. and Rosenberg Barr, “Systematic risk of the CRSP equal-weighted common stock index: a history estimated by stochastic parameter regression”, Journal of Business, 55 (1982), 121–145
Rosenberg Barr and Guy James, “Predictions of Beta from Investment Fundamentals”, Financial Analyst Journal, 32 (May-June 1976), pp.60–72, 33(July-August 1976), pp. 62-70
Rosenberg Barr, “Random coefficient models: the analysis of a cross section of time series by stochastically convergent parameter regression”, Annals of Economic and Social Measurement, 2, pp. 399–428, 1973
Sharpe William, F., “Asset Allocation: Management Style and Performance Measurement”, Journal of Portfolio Management, 18, No.2, (Winter 1992), pp. 7–19
Vasicek O. “A Note on Using Cross-Sectional Information in Bayesian Estimation of Security Betas”, Journal of Finance, 8 (December 1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Bentz, Y., Connor, J.T. (1998). Time-Varying Factor Sensitivities in Equity Investment Management. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_23
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5625-1_23
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-7923-8309-3
Online ISBN: 978-1-4615-5625-1
eBook Packages: Springer Book Archive