Applications of state space models in finance

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202 References Wells, C. (1994). Variable betas on the stockholm exchange 1971-1989. Applied Economics 4, 75–92. Wells, C. (1996). The Kalman Filter in Finance. Advanced Studies in Theoretical and Applied Econometrics. Dordrecht, The Netherlands: Kluwer Academic Publishers. West, M. and J. Harrison (1997). Bayesian Forecasting and Dynamic Models (2 ed.). New York, NY, USA: Springer-Verlag. Yao, J. and J. Gao (2004). Computer-intensive time-varying model approach to the systematic risk of Australian industrial stock returns. Australian Journal of Management 29 (1), 121– 146. Yu, J. (2002). Forecasting volatility in the New Zealand stock market. Applied Financial Economics 12, 193–202. Yu, J. (2005). On leverage in a stochastic volatility model. Journal of Econometrics 127 (2), 165–178. Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control 18 (5), 931–955. Zivot, E., J. Wang, and S. J. Koopman (2002). State space modeling in macroeconomics and finance using SsfPack in S+FinMetrics. Technical report. Available at http://faculty.washington.edu/ezivot/statespacesurvey.pdf (13.4.2005). Zucchini, W., A. Berzel, and J. Bulla (2006). Hidden Markov models. Lecture notes, Institute for Statistics and Econometrics, Georg-August-University, Göttingen, Germany.
State space models play a key role in the estimation of time-varying sensitivities in financial markets. The objective of this book is to analyze the relative merits of modern time series techniques, such as Markov regime switching and the Kalman filter, to model structural changes in the context of widely used concepts in finance. The presented material will be useful for financial economists and practitioners who are interested in taking time-variation in the relationship between financial assets and key economic factors explicitly into account. The empirical part illustrates the application of the various methods under consideration. As a distinctive feature, it includes a comprehensive analysis of the ability of time-varying coefficient models to estimate and predict the conditional nature of systematic risks for European industry portfolios. ISBN: 978-3-941875-22-7 Universitätsverlag Göttingen
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Sascha Mergner Applications of Stat
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erschienen im Universitätsverlag G
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Bibliographische Information der De
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Contents List of figures . . . . .
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Contents ix 4.5 Model selection and
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Contents xi 7.5 Concluding remarks
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xiv List of figures 6.14 Histograms
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Notation and conventions The outlin
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xx Used abbreviations and symbols L
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xxii Used abbreviations and symbols
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xxiv Used abbreviations and symbols
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Preface The work on this book began
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Chapter 1 Introduction “Economist
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1.2 Research objectives 3 1.2 Resea
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1.3 Organization of the thesis 5 to
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8 2 Some stylized facts of weekly s
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10 2 Some stylized facts of weekly
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18 3 Linear Gaussian state space mo
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Chapter 4 Markov regime switching M
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4.1 Basic concepts 45 R t (%) 20 10
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4.1 Basic concepts 47 probability d
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4.3 Parameter estimation 49 X 1 X 2
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4.3 Parameter estimation 51 4.3.2.1
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4.4 Forecasting and decoding 53 4.4
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4.4 Forecasting and decoding 55 mod
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Chapter 5 Conditional heteroskedast
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5.1 Autoregressive conditional hete
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5.2 Stochastic volatility 65 or the
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5.2 Stochastic volatility 67 5.2.1.
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5.2 Stochastic volatility 69 likeli
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5.2 Stochastic volatility 71 linear
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5.2 Stochastic volatility 73 3. An
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5.3 Multivariate conditional hetero
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84 6 Time-varying market beta risk
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Chapter 7 A Kalman filter based con
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7.1 Factor modeling 125 uncondition
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7.1 Factor modeling 127 predictabil
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7.1 Factor modeling 129 usefulness,
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7.2 Specification of a conditional
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7.3 The risk factors 133 Table 7.1:
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7.3 The risk factors 135 introduced
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7.3 The risk factors 137 auxiliary
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7.4 Empirical results 139 Table 7.4
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7.4 Empirical results 141 are consi
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7.4 Empirical results 145 β TS 0.4
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Page 183 and 184: 7.4 Empirical results 151 Table 7.9
Page 185 and 186: 7.4 Empirical results 153 cumulativ
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Page 189 and 190: Conclusion and outlook 157 cannot o
Page 191 and 192: Appendix A Brief review of asset pr
Page 193 and 194: A.3 Alternative asset pricing model
Page 195 and 196: Appendix B Figures
Page 197 and 198: Figures 165 β t β t β t 2.0 1.5
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Page 223 and 224: References Abell, J. D. and T. M. K
Page 225 and 226: References 193 Bulla, J. (2006). Ap
Page 227 and 228: References 195 Fabozzi, F. J. and J
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Page 231 and 232: References 199 Lie, F., R. Brooks,
Page 233: References 201 Shephard, N. (1993).
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