Publication: Testing for volatility co-movement in bivariate stochastic volatility models
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2017
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Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
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The paper considers the problem of volatility co-movement, namely as to whether two financial returns have perfectly correlated common volatility process, in the framework of multivariate stochastic volatility models and proposes a test which checks the volatility co-movement. The proposed test is a stochastic volatility version of the co-movement test proposed by Engle and Susmel (1993), who investigated whether international equity markets have volatility co-movement using the framework of the ARCH model.
In empirical analysis we found that volatility co-movement exists among closelylinked stock markets and that volatility co-movement of the exchange rate markets tends to be found when the overall volatility level is low, which is contrasting to the often-cited finding in the financial contagion literature that financial returns have co-movement in the level during the financial crisis.
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Asai, M., McAleer, M. and Yu, J. (2006). Multivariate stochastic volatility: A review. Econometric Reviews, 25(2-3):145-175.
Chernoff, H. (1954). On the distribution of the likelihood ratio. The Annals of Mathematical Statistics, 25(3):573-578.
Chesher, A. (1984). Testing for neglected heterogeneity. Econometrica, 52(4):865-872.
Chiba, M. and Kobayashi, M. (2013). Testing for a single-factor stochastic volatility in bivariate series. Journal of Risk of Financial Management, 6(1):31-618.
Cipollini, A. and Kapetanios, G. (2008). A stochastic variance factor model for large datasets and an application to S&P data. Economics Letters, 100(1):130-134.
Damelsson, J. (1998). Multivariate stochastic volatility models: estimation and a comparison with vgarch models. Journal of Empirical Finance, 5(2):155-173.
Davidson, R. and MacKinnon, J. G. (1993). Estimation and inference in econometrics. Oxford University Press.
Engle, R. F. and Kozicki, S. (1993). Testing for common features. Journal of Business & Economic Statistics, 11(4):369-380.
Engle, R. F. and Susmel, R. (1993). Common volatility in international equity markets. Journal of Business & Economic Statistics, 11(2):167-176.
Fleming, J., Kirby, C. and Ostdiek, B. (1998). Information and volatility linkages in the stock, bond, and money markets. Journal of Financial Economics, 49(1):111-137.
Forbes, K. J. and Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5):2223-2261.
Greengard, L. and Strain, J. (1991). The fast Gauss transform. SIAM Journal on Scientific and Statistical Computing, 12(1):79-94.
Hamlton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2):357-384.
Harvey, A., Ruiz, E. and Shephard, N. (1994). Multivariate stochastic variance models. The Review of Economic Studies, 61(2):247-264.
Kitagawa, G. (1987). Non-Gaussian state-space modeling of nonstationary time series [with discussion] Journal of the American Statistical Association, 82(1):1032-1063.
Kitagawa, G. (1995). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models Journal of Computational and Graphical Statistics, 5(1):1-125.
Stock, J. H. and Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460):1167-1179.
Tauchen, G. E. and Pitts, M. (1983). The price variability-volume relationship on speculative markets. Econometrica, 51(2):485-505.
Watanabe, T. (1999). A non-linear filtering approach to stochastic volatility models with an application to daily stock returns. Journal of Applied Econometrics, 14(2):101-121.