Applications of state space models in finance

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200 References Pagan, A. R. (1996). The econometrics of financial markets. Journal of Empirical Finance 3 (1), 15–102. Palm, F. C. (1996). GARCH models of volatility. In G. S. Maddala and C. R. Rao (Eds.), Handbook of Statistics, Volume 14, Chapter 7, pp. 209–240. Amsterdam, The Netherlands: Elsevier Science B. V. Pesaran, M. H. and A. Timmermann (1995). Predictability of stock returns: Robustness and economic significance. Journal of Finance 50 (4), 1201–1228. Pitt, M. K. and N. Shephard (1999). Time varying covariances: A factor stochastic volatility approach. In J. M. Bernardo, J. O. Berger, A. P. David, and A. F. M. Smith (Eds.), Bayesian Statistics 6, pp. 547–570. Oxford, UK: Oxford University Press. R Development Core Team (2005). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77 (2), 257–286. Richard, J. F. and W. Zhang (1996). Econometric modeling of UK house prices using accelerated importance sampling. Oxford Bulletin of Economics and Statistics 58 (4), 601–613. Ripley, B. D. (1987). Stochastic Simulation. New York, NY, USA: John Wiley & Sons. Riskmetrics Group (1996). Riskmetrics technical document (4 ed.). Rosenberg, B. (1973). Random coefficients models: the analysis of a cross-section of time series by stochastically convergent parameter regression. Annals of Economic and Social Measurement 2 (4), 399–428. Rosenberg, B., K. Reid, and R. Lanstein (1985). Persuasive evidence of market inefficiency. Journal of Portfolio Management 11 (3), 9–17. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory 13 (3), 341–360. Roweis, S. T. and Z. Ghahramani (1999). A unifying review of linear Gaussian models. Neural Computation 11 (2), 305–345. Rubinstein, M. (1976). The valuation of uncertain income streams and the pricing of options. Bell Journal of Economics 7 (2), 407–425. Russell Investment Group (2007). Russell Indexes Construction and Methodology report. Russell Investment Group. Available at http://www.russell.com/ Indexes/PDF/Methodology.pdf (26.1.2007). Rydén, T., T. Terasvirta, and S. Asbrink (1998). Stylized facts of daily return series and the hidden Markov model. Journal of Applied Econometrics 13 (3), 217–244. Samuelson, P. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review 6, 41–49. Sandmann, G. and S. J. Koopman (1998). Estimation of stochastic volatility models through Monte Carlo Maximum Likelihood. Journal of Econometrics 87 (2), 271–301. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistic 6 (2), 461–464. Schweppe, F. (1965). Evaluation of likelihood functions for Gaussian signals. IEEE Transactions on Information Theory 11, 61–70. Schwert, G. W. and P. J. Seguin (1990). Heteroskedasticity in stock returns. Journal of Finance 45 (4), 1129–1155. Sentana, E. (1995). Quadratic ARCH models. Review of Economic Studies 62 (4), 639–661. Shanken, J. (1990). Intertemporal asset pricing: An empirical investigation. Journal of Econometrics 45 (1–2), 99–120. Shanken, J. (1992). On the estimation of beta-pricing models. Review of Financial Studies 5 (1), 1–33. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance 19, 425–442.
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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 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
Page 209 and 210: Appendix C Tables
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Page 217 and 218: Tables 185 Table C.7: Parameter est
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Page 221 and 222: Tables 189 Table C.8: Out-of-sample
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
Page 229 and 230: References 197 Harvey, A. C., E. Ru
Page 231: References 199 Lie, F., R. Brooks,
Page 235: State space models play a key role
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