Applications of state space models in finance

More documents

Recommendations

Info

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).
References 199 Lie, F., R. Brooks, and R. Faff (2000). Modelling the equity beta risk of Australian financial sector companies. Australian Economic Papers 39, 301–311. Liesenfeld, R. and J.-F. Richard (2003). Univariate and multivariate stochastic volatility models: estimation and diagnostics. Journal of Empirical Finance 10 (4), 505–531. Ling, S. and M. McAleer (2002). Stationarity and the existence of moments of a family of GARCH processes. Journal of Econometrics 106 (1), 109–117. Lintner, J. (1965). The valuation of risky assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics 47 (1), 13–37. Liodakis, M., G. Brar, R. Crenian, and M. Dowle (2003). Estimating betas using fundamentals. Technical report, Citigroup Smith Barney, Equity Research Europe, Quantitative Strategy. Lo, A. W. and A. C. MacKinlay (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies 1 (1), 41–66. Lo, A. W. and A. C. MacKinlay (1997). Maximizing predictability in the stock and bond markets. Macroeconomic Dynamics 1, 102–134. Lo, A. W. and A. C. MacKinlay (1999). A Non-Random Walk Down Wall Street. Princeton, NJ, USA: Princeton University Press. MacDonald, I. L. and W. Zucchini (1997). Hidden Markov and Other Models for Discrete-valued Time Series, Volume 70 of Monographs on Statistics and Applied Probability. London, UK: Chapman & Hall. MacKinlay, A. C. (1995). Multifactor models do not explain deviations from the CAPM. Journal of Financial Economics 38 (1), 3–28. Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business 36 (4), 394–419. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York, NY, USA: John Wiley & Sons. McLachlan, G. J. and T. Krishnan (1997). The EM Algorithm and Extensions. New York, NY, USA: John Wiley & Sons. Meinhold, R. J. and N. D. Singpurwella (1983). Understanding the Kalman filter. American Statistician 37 (2), 123–127. Melino, A. and S. M. Turnbull (1990). Pricing foreign currency options with stochastic volatility. Journal of Econometrics 45 (1–2), 239–265. Mergner, S. and J. Bulla (2008). Time-varying beta risk of pan-European industry portfolios: A comparison of alternative modeling techniques. European Journal of Finance (forthcoming). Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica 41 (5), 867–887. Meyer, R. and J. Yu (2000). BUGS for a Bayesian analysis of stochastic volatility models. Econometrics Journal 3, 198–215. Moonis, S. A. and A. Shah (2002). Testing for time variation in beta in India. Available at http://www.mayin.org/ajayshah/pdfdocs (3.1.2005). Moskowitz, T. J. and M. Grinblatt (1999). Do industries explain momentum? Journal of Finance 54 (4), 1249–1290. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59 (2), 347–370. Nicholls, D. F. and A. R. Pagan (1985). Varying coefficient regression. In E. J. Hannan, P. R. Krishnaiah, and M. M. Rao (Eds.), Handbook of Statistics, Volume 5, pp. 413–449. Amsterdam, The Netherlands: Elsevier Science Publishers B. V. Omori, Y., S. Chib, N. Shephard, and J. Nakajima (2004). Stochastic volatility with leverage: fast likelihood inference. Economics Papers 2004-W19, Economics Group, Nuffield College, University of Oxford. Available at http://ideas.repec.org/p/nuf/econwp/0419.html (7.5.2005).
Page 1:
Sascha Mergner Applications of Stat
Page 4 and 5:
erschienen im Universitätsverlag G
Page 6 and 7:
Bibliographische Information der De
Page 9 and 10:
Contents List of figures . . . . .
Page 11 and 12:
Contents ix 4.5 Model selection and
Page 13:
Contents xi 7.5 Concluding remarks
Page 16 and 17:
xiv List of figures 6.14 Histograms
Page 19:
Notation and conventions The outlin
Page 22 and 23:
xx Used abbreviations and symbols L
Page 24 and 25:
xxii Used abbreviations and symbols
Page 26 and 27:
xxiv Used abbreviations and symbols
Page 29:
Preface The work on this book began
Page 33 and 34:
Chapter 1 Introduction “Economist
Page 35 and 36:
1.2 Research objectives 3 1.2 Resea
Page 37:
1.3 Organization of the thesis 5 to
Page 40 and 41:
8 2 Some stylized facts of weekly s
Page 42 and 43:
10 2 Some stylized facts of weekly
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
18 3 Linear Gaussian state space mo
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 75 and 76:
Chapter 4 Markov regime switching M
Page 77 and 78:
4.1 Basic concepts 45 R t (%) 20 10
Page 79 and 80:
4.1 Basic concepts 47 probability d
Page 81 and 82:
4.3 Parameter estimation 49 X 1 X 2
Page 83 and 84:
4.3 Parameter estimation 51 4.3.2.1
Page 85 and 86:
4.4 Forecasting and decoding 53 4.4
Page 87 and 88:
4.4 Forecasting and decoding 55 mod
Page 89 and 90:
Chapter 5 Conditional heteroskedast
Page 91 and 92:
5.1 Autoregressive conditional hete
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
5.2 Stochastic volatility 65 or the
Page 99 and 100:
5.2 Stochastic volatility 67 5.2.1.
Page 101 and 102:
5.2 Stochastic volatility 69 likeli
Page 103 and 104:
5.2 Stochastic volatility 71 linear
Page 105 and 106:
5.2 Stochastic volatility 73 3. An
Page 107 and 108:
5.3 Multivariate conditional hetero
Page 109 and 110:
Page 111 and 112:
Page 113:
Page 116 and 117:
84 6 Time-varying market beta risk
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 155 and 156:
Chapter 7 A Kalman filter based con
Page 157 and 158:
7.1 Factor modeling 125 uncondition
Page 159 and 160:
7.1 Factor modeling 127 predictabil
Page 161 and 162:
7.1 Factor modeling 129 usefulness,
Page 163 and 164:
7.2 Specification of a conditional
Page 165 and 166:
7.3 The risk factors 133 Table 7.1:
Page 167 and 168:
7.3 The risk factors 135 introduced
Page 169 and 170:
7.3 The risk factors 137 auxiliary
Page 171 and 172:
7.4 Empirical results 139 Table 7.4
Page 173 and 174:
7.4 Empirical results 141 are consi
Page 175 and 176:
7.4 Empirical results 143 Table 7.6
Page 177 and 178:
7.4 Empirical results 145 β TS 0.4
Page 179 and 180: 7.4 Empirical results 147 Table 7.7
Page 185 and 186: 7.4 Empirical results 153 cumulativ
Page 187 and 188: Chapter 8 Conclusion and outlook Th
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
Page 211 and 212: Tables 179 Table C.1 — continued
Page 213 and 214: Tables 181 Table C.3: In-sample mea
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
show all

Applications of state space models in finance

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?