×

A multivariate threshold stochastic volatility model. (English) Zbl 1151.91540

Summary: We introduce in this paper a multivariate threshold stochastic volatility model for multiple financial return time series. This model allows the dynamic structure of return and volatility to change according to a threshold model while accounting for the interdependence of financial returns. Through the threshold volatility modeling, we can understand the impact of market news on volatility asymmetry. Estimation of unknown parameters are carried out using Markov chain Monte Carlo techniques. Simulations show that our estimators are reliable in moderately large sample sizes. We apply the model to three market indice data and estimate time-varying correlations among the indice returns.

MSC:

91B28 Finance etc. (MSC2000)
91B84 Economic time series analysis
91B82 Statistical methods; economic indices and measures

References:

[1] Asai, M.; McAleer, M., Dynamic asymmetric leverage in stochastic volatility models, Econ. Rev., 24, 317-332 (2005) · Zbl 1075.62092
[2] Asai, M.; McAleer, M., Multivariate stochastic volatility: a review, Econ. Rev., 25, 145-175 (2006) · Zbl 1107.62108
[3] Asai, M.; McAleer, M., Asymmetric multivariate stochastic volatility, Econ. Rev., 25, 453-473 (2006) · Zbl 1112.62116
[4] M. Asai, M. McAleer, The structure of dynamic correlations in multivariate stochastic volatility models, J. Econ. (2007). forthcoming.; M. Asai, M. McAleer, The structure of dynamic correlations in multivariate stochastic volatility models, J. Econ. (2007). forthcoming. · Zbl 1429.62457
[5] F. Black, Studies of stock price volatility changes, in: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, 1976, pp. 177-181.; F. Black, Studies of stock price volatility changes, in: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, 1976, pp. 177-181.
[6] Bollerslev, T., Generalised autoregressive conditional Heteroscedasticity, J. Econ., 31, 307-327 (1986) · Zbl 0616.62119
[7] Carter, C. K.; Kohn, R., On Gibbs sampling for state space models, Biometrika, 81, 541-553 (1994) · Zbl 0809.62087
[8] Chan, D.; Kohn, R.; Kirby, C., Multivariate stochastic volatility models with correlated errors, Econ. Rev., 25, 245-274 (2006) · Zbl 1113.62127
[9] Chen, C. W.S.; So, M. K.P., On a threshold heteroscedastic model, International Journal of Forecasting, 22, 73-89 (2006)
[10] Chib, S.; Nardari, F.; Shephard, N., Analysis of high dimensional multivariate stochastic volatility models, J. Econ., 134, 341-371 (2006) · Zbl 1418.62377
[11] Christie, A., The stochastic behavior of common stock variances: value, leverage and interest rate effects, J. Financ. Econ., 10, 407-432 (1982)
[12] A. Cipollini, G. Kapetanios, A stochastic variance factor model for large datasets and an application to S&P data, working paper 506, Department of Economics, University of London, 2004.; A. Cipollini, G. Kapetanios, A stochastic variance factor model for large datasets and an application to S&P data, working paper 506, Department of Economics, University of London, 2004.
[13] Danielsson, J., Stochastic volatility in asset prices estimation with simulated maximum likelihood, J. Econ., 64, 375-400 (1994) · Zbl 0825.62953
[14] Danielsson, J., Multivariate stochastic volatility models: estimation and a comparison with VGARCH models, J. Empir. Financ., 5, 155-173 (1998)
[15] Ghysels, E.; Harvey, A. C.; Renault, E., Stochastic volatility, (Rao, C. R.; Maddala, G. S., Handbook of Statistics (1996), Elsevier: Elsevier Amsterdam), 119-191
[16] Glosten, L.; Jagannathan, R.; Runkle, D., On the relation between the expected value and the volatility of the nominal excess return on stocks, J. Financ., 48, 1779-1801 (1993)
[17] Engle, R. F., Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1008 (1982) · Zbl 0491.62099
[18] Harvey, A. C., Forecasting Structural Time Series Models and the Kalman Filter (1989), Cambridge University Press: Cambridge University Press Cambridge
[19] Harvey, A. C.; Ruiz, E.; Shephard, N., Multivariate stochastic variance models, Rev. Econ. Stud., 61, 247-264 (1994) · Zbl 0805.90026
[20] Harvey, A. C.; Shephard, N., Estimation of an asymmetric stochastic volatility model for asset returns, J. Bus. Econ. Stat., 14, 429-434 (1996)
[21] Jacquier, E.; Polson, N. G.; Rossi, P. E., Bayesian analysis of stochastic volatility models, J. Bus. Econ. Stat., 12, 371-378 (1994)
[22] Jacquier, E.; Polson, N. G.; Rossi, P. E., Bayesian analysis of stochastic volatility models with leverage effect and fat tails, J. Econ., 122, 185-212 (2004) · Zbl 1328.91254
[23] Kim, S.; Shephard, N.; Chib, S., Stochastic volatility: likelihood inference and comparison with ARCH models, Rev. Econ. Stud., 65, 361-393 (1998) · Zbl 0910.90067
[24] C.K. Kwan, W.K. Li, K. Ng, A multivariate threshold GARCH model with time-varying correlations, Econ. Rev., in press.; C.K. Kwan, W.K. Li, K. Ng, A multivariate threshold GARCH model with time-varying correlations, Econ. Rev., in press. · Zbl 1180.62123
[25] Li, W. K.; Lam, K., Modelling asymmetry in stock returns by a threshold autoregressive conditional heteroscedastic model, The Statistician, 44, 333-341 (1995)
[26] Li, C. W.; Li, W. K., On a double threshold autoregressive heteroskedastic autoregressive time series model, J. Appl. Econ., 11, 253-274 (1996)
[27] Liu, J. S.; Wong, W. H.; Kong, A., Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes, Biometrika, 81, 27-40 (1994) · Zbl 0811.62080
[28] Shephard, N., Statistical aspect of ARCH and stochastic volatility, (Cox, D. R.; Hinkley, D. V.; Barndorff-Nielsen, Time Series Models in Econometrics, Finance and other Fields (1996), Chapman & Hall: Chapman & Hall London), 1-67 · Zbl 0873.90018
[29] Shephard, N.; Pitt, M. K., Likelihood analysis of non-Gaussian measurement time series, Biometrika, 84, 653-667 (1997) · Zbl 0888.62095
[30] So, M. K.P.; Kwok, S. W.Y., A multivariate long memory stochastic volatility model, Phys. A, 362, 450-464 (2006)
[31] So, M. K.P.; Lam, K.; Li, W. K., A stochastic volatility model with Markov switching, J. Bus. Econ. Stat., 16, 244-253 (1998)
[32] So, M. K.P.; Li, W. K.; Lam, K., Multivariate modelling of the autoregressive random variance process, J. Time Ser. Anal., 18, 429-446 (1997) · Zbl 0927.62092
[33] So, M. K.P.; Li, W. K.; Lam, K., A threshold stochastic volatility model, J. Forecasting, 21, 473-500 (2002)
[34] S.J. Taylor, Financial returns modeled by the product of two stochastic processes. A study of daily sugar prices 1961-79, in: O.D., Anderson, (Ed.), Time Series Analysis: Theory and Practice 1, North-Holland Amsterdam 1982, pp. 203-226; S.J. Taylor, Financial returns modeled by the product of two stochastic processes. A study of daily sugar prices 1961-79, in: O.D., Anderson, (Ed.), Time Series Analysis: Theory and Practice 1, North-Holland Amsterdam 1982, pp. 203-226
[35] Tong, H., Threshold Models in Nonlinear Time Series Analysis (1983), Springer-Verlag: Springer-Verlag New York · Zbl 0527.62083
[36] Tong, H.; Lim, K. S., Threshold autoregression, limit cycles and cyclical data, J. R. Stat. Soc. B, 42, 245-292 (1980), (with discussion) · Zbl 0473.62081
[37] Yu, J., On leverage in a stochastic volatility model, J. Econ., 127, 165-178 (2005) · Zbl 1335.91116
[38] Yu, J.; Meyer, R., Multivariate volatility models: Bayesian estimation and model comparison, Econ. Rev., 25, 361-384 (2006) · Zbl 1113.62133
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.