Applications of state space models in finance

More documents

Recommendations

Info

192 References Baum, L. E. and T. Petrie (1966). Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics 37, 1554–1563. Baum, L. E., T. Petrie, G. Soules, and N. Weiss (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics 41, 164–171. Bauwens, L., S. Laurent, and J. V. K. Rombouts (2003). Multivariate GARCH models: A survey. Discussion Paper 2003/31, CORE (Center for operations research and econometrics), Louvain-la-Neuve, France. Available at http://ssrn.com/abstract=411062 (29.12.2004). Bera, A. K. and M. L. Higgins (1993). ARCH models: Properties, estimation and testing. Journal of Economic Surveys 7 (4), 305–366. Berndt, E. K., B. H. Hall, R. E. Hall, and J. A. Hausman (1974). Estimation inference in nonlinear structured models. Annals of Economic and Social Measurement 3 (4), 653–665. Bhandari, L. C. (1988). Debt/equity ratio and expected common stock returns: Empirical evidence. Journal of Finance 43 (2), 507–528. Bhar, R. and S. Hamori (2004). Hidden Markov Models: Applications to Financial Economics. Advanced Studies in Theoretical and Applied Econometrics. Dordrecht, The Netherlands: Kluwer Academic Publishers. Black, F. (1976). Studies of stock price volatility changes. In Proceedings of the American Statistical Association Annual Meeting, Business and Economic Statistic Section, Washington, DC, USA, pp. 177–181. Blanchard, O. J. and S. Fischer (1989). Lectures on Macroeconomics. Cambridge, MA, USA: The MIT Press. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307–327. Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. Review of Economics and Statistics 69 (3), 542–547. Bollerslev, T. (1990). Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics 72 (3), 498–505. Bollerslev, T., Y. Chou, R. and F. Kroner, K. (1992). ARCH modeling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52 (1–2), 5–59. Bollerslev, T., R. F. Engle, and D. B. Nelson (1994). ARCH models. In R. F. Engle and D. L. McFadden (Eds.), Handbook of Econometrics, Volume 4, Chapter 49, pp. 2959–3038. Amsterdam, The Netherlands: Elsevier Science B.V. Bollerslev, T., R. F. Engle, and J. M. Wooldridge (1988). A capital asset pricing model with time-varying covariances. Journal of Political Economy 96 (1), 116–131. Bos, T. and P. Newbold (1984). An empirical investigation of the possibility of stochastic systematic risk in the market model. Journal of Business 57 (1), 35–41. Braun, P. A., D. B. Nelson, and A. M. Sunier (1995). Good news, bad news, volatility and betas. Journal of Finance 50 (5), 1575–1603. Brennan, N., S. Hartmann, S. Fagg, M. Dowle, A. Luck, G. Brar, and M. Turner (2000). A new European risk attribute model (ERAM). Technical report, Schroder Salomon Smith Barney, Equity Research Europe, Quantitative Strategy. Brooks, R. D., R. W. Faff, and M. D. McKenzie (1998). Time–varying beta risk of Australian industry portfolios: A comparison of modelling techniques. Australian Journal of Management 23 (1), 1–22. Broto, C. and E. Ruiz (2004). Estimation methods for stochastic volatility models: A survey. Journal of Economic Surveys 18 (5), 613–649. Brown, R. L., J. Durbin, and J. M. Evans (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B 37 (2), 149–192.
References 193 Bulla, J. (2006). Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series. Ph. D. thesis, Institute for Statistics and Econometrics, Georg-August- University, Göttingen, Germany. Bulla, J. and A. Berzel (2006). Computational issues in parameter estimation for stationary hidden Markov models. Computational Statistics (to appear). Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics 18 (2), 373–399. Campbell, J. Y. and L. Hentschel (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31 (3), 281–318. Campbell, J. Y., A. W. Lo, and A. C. MacKinlay (1997). The Econometrics of Financial Markets. Princeton, NJ, USA: Princeton University Press. Campbell, K., J. Enos, D. Gerlanc, and D. Kane (2006a). Backtests: using the backtest package. Available at http://www.r-project.org (8.2.2007). Campbell, K., J. Enos, D. Gerlanc, and D. Kane (2006b). The backtest package, version 0.1-1. Available at http://www.r-project.org (8.2.2007). Campbell, K., J. Enos, and D. Kane (2007). Performing equity investment simulations with the portfolioSim package. Available at http://www.r-project.org (8.2.2007). Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance 52 (1), 57–82. Chan, D. X., R. Kohn, and C. Kirby (2005). Multivariate stochastic volatility models with correlated errors. SSRN Working Paper Series, Social Science Research Network. Available at http://ssrn.com/abstract=876348 (9.5.2006). Chan, L. K., Y. Hamao, and J. Lakonishok (1991). Fundamentals and stock returns in japan. Journal of Finance 46 (5), 1739–1764. Chan, L. K., N. Jegadeesh, and J. Lakonishok (1996). Momentum strategies. Journal of Finance 51 (5), 1681–1713. Chan, L. K., J. Karceski, and J. Lakonishok (1998). The risk and return from factors. Journal of Financial and Quantitative Analysis 33 (3), 159–188. Chen, N.-F., R. Roll, and S. A. Ross (1986). Economic forces and the stock market. Journal of Business 59 (3), 383–403. Chib, S. and E. Greenberg (1996). Markov Chain Monte Carlo simulation methods in econometrics. Econometric Theory 12 (3), 409–431. Chib, S., F. Nardari, and N. Shephard (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics 134 (2), 341–371. Chow, G. C. (1984). Random and changing coefficient models. In Z. Griliches and M. D. Intriligator (Eds.), Handbook of Econometrics, Volume 2, pp. 1213–1245. Amsterdam, The Netherlands: Elsevier Science Publishers B.V. Clark, P. K. (1987). The cyclical component of U.S. economic activity. Quarterly Journal of Economics 102 (4), 797–814. Cochrane, J. H. (2005). Asset Pricing (Revised ed.). Princeton, NJ, USA: Princeton University Press. Collins, D. W., J. Ledolter, and J. Rayburn (1987). Some further evidence on the stochastic properties of systematic risk. Journal of Business 60 (3), 425–448. Daniel, K. and S. Titman (1997). Evidence on the characteristics of cross sectional variation in stock returns. Journal of Finance 52 (1), 1–33. Danielsson, J. (1994). Stochastic volatility in asset prices, estimation with simulated maximum likelihood. Journal of Econometrics 64 (1–2), 375–400. Davidson, R. and J. G. MacKinnon (2004). Econometric Theory and Methods. Oxford, UK: Oxford University Press. de Jong, P. (1988a). A cross–validation filter for time series models. Biometrika 75 (3), 594–600. de Jong, P. (1988b). The likelihood for a state space model. Biometrika 75 (1), 165–169.
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 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: References Abell, J. D. and T. M. K
Page 227 and 228: References 195 Fabozzi, F. J. and J
Page 229 and 230: References 197 Harvey, A. C., E. Ru
Page 231 and 232: References 199 Lie, F., R. Brooks,
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?