Applications of state space models in finance

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198 References Keim, D. B. and R. F. Stambaugh (1986). Predicting returns in the stock and bond markets. Journal of Financial Economics 17 (2), 357–390. Kim, C.-J. (1993). Unobserved-component time series models with Markov-switching heteroskedasticity: Changes in regime and the link between inflation rates and inflation uncertainty. Journal of Business and Economic Statistics 11 (3), 341–349. Kim, C.-J. and C. R. Nelson (1989). The time-varying parameter model for modeling changing conditional variance: The case of the lucas hypothesis. Journal of Business and Economic Statistics 7 (4), 433–440. Kim, C.-J. and C. R. Nelson (1999). State-Space Models with Regime Switching. Cambridge, MA, USA: The MIT Press. Kim, S., N. Shephard, and S. Chib (1998). Stochastic volatility: Likelihood inference and comparison with ARCH models. Review of Economic Studies 65 (3), 361–393. Kohn, R. and C. F. Ansley (1989). A fast algorithm for signal extraction, influence and crossvalidation in state space models. Biometrika 76 (1), 65–79. Koopman, S. J. (1993). Disturbance smoother for state space models. Biometrika 80 (1), 117–126. Koopman, S. J. (1997). Exact initial Kalman filtering and smoothing for nonstationary time series models. Journal of the American Statistical Association 92 (440), 1630–1638. Koopman, S. J. and E. Hol-Uspensky (2001). The stochastic volatility in mean model: Empirical evidence from international stock markets. Working Paper, Department of Econometrics, Free University Amsterdam, Amsterdam, The Netherlands. Available at http://staff. feweb.vu.nl/koopman/papers/svm300701.pdf (18.3.2005). Koopman, S. J., B. Jungbacker, and E. Hol (2004). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Project, Department of Econometrics, Free University Amsterdam, Amsterdam, The Netherlands. Available at http://staff.feweb.vu.nl/koopman/projects/2004forc.pdf (21.3.2005). Koopman, S. J., N. Shephard, and J. A. Doornik (1999). Statistical algorithms for models in state space using SsfPack 2.2. Econometrics Journal 2 (1), 113–166. Available at http://www.ssfpack.com (19.3.2005). Lahiri, K. and J. G. Wang (1996). Interest rate spreads as predictors of business cycles. In G. S. Maddala and C. R. Rao (Eds.), Handbook of Statistics, Volume 14, Chapter 10, pp. 297–315. Amsterdam, The Netherlands: Elsevier Science B. V. Lakonishok, J., A. Shleifer, and R. W. Vishny (1994). Contrarian investment, extrapolation, and risk. Journal of Finance 49 (5), 1541–1578. Laurent, S. and J.-P. Peters (2002). G@RCH 2.2, an Ox package for estimating and forecasting various ARCH models. Journal of Economic Surveys 16, 447–484. Ledoit, O., P. Santa-Clara, and M. Wolf (2003). Flexible multivariate GARCH modeling with an application to international stock markets. The Review of Economics and Statistics 85 (1), 735–747. Lee, K. M. and S. J. Koopman (2004). Estimating stochastic volatility models: A comparison of two importance samplers. Studies in Nonlinear Dynamics and Econometrics 8 (2), Article 5. Lettau, M. and S. Ludvigson (2001). Resurrecting the (C)CAPM: A cross-sectional test when risk premia are time-varying. Journal of Political Economy 109 (6), 1238–1287. Leusner, J., J. D. Akhavein, and J. Swamy (1996). Solving an empirical puzzle in the capital asset pricing model. Finance and Economics Discussion Series 1996-14, The Federal Reserve Board, Washington, DC, USA. Available at http://www.federalreserve.gov/pubs/feds/1996/199614/199614pap.pdf (18.6.2006). Li, X. (2003). On unstable beta risk and its modelling techniques for New Zealand industry portfolios. Working Paper 03.01, Massey University Commerce, Auckland, New Zealand. Available at http://ssrn.com/abstract=503722 (29.12.2004).
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Page 1:
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 143 Table 7.6
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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 215 and 216: Tables 183 Table C.5: Out-of-sample
Page 217 and 218: Tables 185 Table C.7: Parameter est
Page 219 and 220: Tables 187 Table C.7 — continued
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: References 197 Harvey, A. C., E. Ru
Page 233 and 234: References 201 Shephard, N. (1993).
Page 235: State space models play a key role
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