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REFERENCES

  • Andersen, T., L. Benzoni and J. Lund (2002) “An Empirical Investigation of Continuous-Time Models for Equity Returns”, Journal of Finance, Vol. 57, pp. 12391284.
  • Awartani, M. A. B. and V. Corradi (2005) “Predicting the Volatility of the S&P-500 Stock Index Via GARCH Models: the Role of Asymmetries”, International Journal of Forecasting, Vol. 21, pp. 167183.
  • Bauwens, L. and M. Lubrano (1998) “Bayesian Inference on GARCH Models Using Gibbs Sampler”, Econometrics Journal, Vol. 1, pp. c23c46.
    Direct Link:
  • Berg, A., R. Meyer and J. Yu (2004) “DIC as a Model Comparison Criterion for Stochastic Volatility Models”, Journal of Business and Economic Statistics, Vol. 22, pp. 107120.
  • Black, F. (1976) “Studies of Stock Market Volatility Changes”, Proceedings of the American Statistical Association, Business and Economic Statistics Section, pp. 177181.
  • Bollerslev, T. (1986) “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, Vol. 31, pp. 307327.
  • Bollerslev, T., R. F. Engle and D. B. Nelson (1994) “ARCH Models”, in R. F. Endgle and D. McFadden, eds, The Handbook of Econometrics 4, Amsterdam: North-Holland, pp. 29593038.
  • Cai, J. (1994) “A Markov Model of Switching-Regime ARCH”, Journal of Business and Economic Statistics, Vol. 12, pp. 309316.
  • Chan, W. H. and J. M. Maheu (2002) “Conditional Jump Dynamics in Stock Market Returns”, Journal of Business and Economic Statistics, Vol. 20, pp. 377389.
  • Chernov, M., A. R. Gallant, E. Ghysels and G. Tauchen (2003) “Alternative Models for Stock Price Dynamics”, Journal of Econometrics, Vol. 116, pp. 225257.
  • Chib, S. (1995) “Marginal Likelihood from the Gibbs Output”, Journal of the American Statistical Association, Vol. 90, pp. 13131321.
  • Chib, S. (2001) “Markov Chain Monte Carlo Methods: Computation and Inference”, in J. J. Heckman and E. Leamer, eds, Handbook of Econometrics, Vol. 5, Amsterdam: North-Holland, pp. 35693649.
  • Chib, S. and E. Greenberg (1995) “Understanding the Metropolis-Hastings Algorithm”, American Statistician, Vol. 49, pp. 327335.
  • Chib, S. and E. Greenberg (1996) “Markov Chain Monte Carlo Simulation Methods in Econometrics”, Econometric Theory, Vol. 12, pp. 409431.
  • Chib, S. and I. Jeliazkov (2001) “Marginal Likelihood from the Metropolis-Hastings Output”, Journal of the American Statistical Association, Vol. 96, pp. 270291.
  • Chib, S. and I. Jeliazkov (2005) “Accept-Reject Metropolis-Hastings Sampling and Marginal Likelihood Estimation”, Statistica Neerlandica, Vol. 59, pp. 3044.
  • Chib, S., F. Nardari and N. Shephard (2002) “Markov Chain Monte Carlo Methods for Stochastic Volatility Models”, Journal of Econometrics, Vol. 108, pp. 281316.
  • Doornik, J. A. (2006) Ox: Object Oriented Matrix Programming, London: Timberlake Consultants Press.
  • Durbin, J. and S. J. Koopman (2002) “Simple and Efficient Simulation Smoother for State Space Time Series Analysis”, Biometrika, Vol. 89, pp. 603616.
  • Eraker, B., M. Johanners and N. G. Polson (2003) “The Impact of Jumps in Returns and Volatility”, Journal of Finance, Vol. 53, pp. 12691330.
  • Geweke, J. (1992) “Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments”, in J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds, Bayesian Statistics, Vol. 4, New York: Oxford University Press, pp. 169188.
  • Ghysels, E., A. C. Harvey and E. Renault (2002) “Stochastic Volatility”, in C. R. Rao and G. S. Maddala, eds, Statistical Methods in Finance, Amsterdam: North-Holland, pp. 119191.
  • Giot, P. and S. Laurent (2004) “Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models”, Journal of Empirical Finance, Vol. 11, pp. 379398.
  • Glosten, L. R., R. Jagannathan and D. Runkle (1993) “On the Relation between the Expected Value and the Volatility of Nominal Excess Returns on Stocks”, Journal of Finance, Vol. 48, pp. 17791801.
  • Haas, M., S. Mittnik and M. S. Paolella (2004) “A New Approach to Markov-Switching GARCH Models”, Journal of Financial Econometrics, Vol. 2, pp. 493530.
  • Hamilton, J. D. and R. Susmel (1994) “Autoregressive Conditional Heteroscedasticity and Changes in Regime”, Journal of Econometrics, Vol. 64, pp. 307333.
  • Hansen, P. R. and A. Lunde (2005) “A Forecast Comparison of Volatility Models: Does Anything Beat A GARCH(1,1)? ”, Journal of Applied Econometrics, Vol. 20, pp. 873889.
  • Hentschel, L. (1995) “All in the Family Nesting Symmetric and Asymmetric GARCH Model”, Journal of Financial Economics, Vol. 39, pp. 71104.
  • Jacquier, E., N. G. Polson and P. E. Rossi (2004) “Bayesian Analysis of Stochastic Volatility with Fat-Tails and Correlated Errors”, Journal of Econometrics, Vol. 122, No. 1, pp. 185212.
  • de Jong, P. and N. Shephard (1995) “The Simulation Smoother for Time Series Models”, Biometrika, Vol. 82, pp. 339350.
  • Jorion, P. (1988) “On Jump Processes in the Foreign Exchange and Stock Markets”, Review of Financial Studies, Vol. 1, pp. 427445.
  • Kim, S., N. Shephard and S. Chib (1998) “Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models”, Review of Economic Studies, Vol. 65, pp. 361393.
    Direct Link:
  • Kobayashi, M. (2006) “Testing for Volatility Jumps in the Stochastic Volatility Process”, Asia-Pacific Financial Markets, Vol. 12, pp. 143157.
  • Lehar, A., M. Scheicher and C. Schittenkopf (2002) “GARCH Vs. Stochatic Volatility: Option Pricing and Risk Management”, Journal of Banking and Finance, Vol. 26, pp. 323345.
  • Li, H., M. T. Wells and C. L. Yu (2008) “A Bayesian Analysis of Return Dynamics with Lévy Jumps”, Review of Financial Studies, Vol. 21, pp. 23452378.
  • Maheu, J. M. and T. H. McCurdy (2004) “News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns”, Journal of Finance, Vol. 59, pp. 755793.
  • Nakajima, J. and Y. Omori (2009) “Leverage, Heavy-Tails and Correlated Jumps in Stochastic Volatility Models”, Computational Statistics and Data Analysis, Vol. 53, pp. 25352553.
  • Nakatsuma, T. (2000) “Bayesian Analysis of ARMA-GARCH Models: A Markov Chain Sampling Approach”, Journal of Econometrics, Vol. 95, pp. 5769.
  • Nelson, D. B. (1991) “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, Vol. 59, pp. 347370.
  • Omori, Y. and T. Watanabe (2008) “Block Sampler and Posterior Mode Estimation for Asymmetric Stochastic Volatility Models”, Computational Statistics and Data Analysis, Vol. 52, pp. 28922910.
  • Omori, Y., S. Chib, N. Shephard and J. Nakajima (2007) “Stochastic Volatility with Leverage: Fast Likelihood Inference”, Journal of Econometrics, Vol. 140, pp. 425449.
  • Pitt, M. and N. Shephard (1999) “Filtering Via Simulation: Auxiliary Particle Filter”, Journal of the American Statistical Association, Vol. 94, pp. 590599.
  • Raggi, D. and S. Bordignon (2006) “Comparing Stochastic Volatility Models through Monte Carlo Simulations”, Computational Statistics and Data Analysis, Vol. 50, pp. 16781699.
  • Shephard, N. (2005) Stochastic Volatility: Selected Readings, Oxford: Oxford University Press.
  • Shephard, N. and M. Pitt (1997) “Likelihood Analysis of Non-Gaussian Measurement Time Series”, Biometrika, Vol. 84, pp. 653667.
  • Vrontos, I. D., P. Dellaportas and D. N. Politis (2000) “Full Bayesian Inference for GARCH and EGARCH Models”, Journal of Business and Economic Statistics, Vol. 18, pp. 187198.
  • Watanabe, T. and Y. Omori (2004) “A Multi-Move Sampler for Estimating Non-Gaussian Time Series Models: Comments on S (1997)”, Biometrika, Vol. 91, pp. 246248.
  • Yu, J. (2005) “On Leverage in A Stochastic Volatility Model”, Journal of Econometrics, Vol. 127, pp. 165178.