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Contagion in Global Bond Markets

  • Received : 2024.06.13
  • Accepted : 2024.08.05
  • Published : 2024.08.30

Abstract

Purpose: The paper analyzes for detecting unexpected shocks such as global financial crisis and COVID-19 pandemic, and contagion between countries by capturing in the mean-shift, variance-covariance-shift, and skewness-coskewness-shift parameters of interest rates. Research design, data and methodology: A flexible multivariate model of interest rates is provided by allowing for regime switching and a joint skewed normal distribution. The model is applying to the structural breaks of crisis and contagion between the US and the selected global bond markets during the global financial crisis and COVID-19 pandemic, respectively. Inspection of the moment statistics weakly suggests a flight to safety to the US during the global financial crisis and to Canada during the COVID-19 pandemic. Results: The results indicate that risk averse investors had a higher risk appetite for the US and Canada assets during the crisis regimes, compared to their counterparts. Conclusions: The results show that coskewness contagion dominates correlation contagion, and coskewness contagion is significant for the Korea and Japan-US pairs for the global financial crisis and the Euro-US pair for the COVID-19 pandemic. All channels of structural breaks of crisis and contagion are significant when considered jointly, reinforcing the need to consider contagion and structural breaks during crises in a multivariate setting.

Keywords

Acknowledgement

The corresponding author would thank colleagues who attended the department seminars for helpful comments on an earlier paper, on which this paper builds. This work was supported by the 2023 Inje University research grant.

References

  1. Andrews, D.W.K. & Monahan, J.C. (1992). An improved heteroscedasticity and autocorrelation consistent covariance matrix estimator. Econometrica, 60, 953-966. https://doi.org/10.2307/2951574
  2. Bai, J. & Perron, P. (2003). Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics, 18, 1-22. https://doi.org/10.1002/jae.659
  3. Bera, A. & John, S. (1983). Tests for Multivariate Normality with Pearson Alternatives. Communications in Statistics - Theory and Methods, 12, 103-117. https://doi.org/10.1080/03610928308828444
  4. Black, F. (1972). Capital Market Equilibrium with Restricted Borrowing. Journal of Business, 45, 444-54. https://doi.org/10.1086/295472
  5. Chan, J.C., Hsio, C.Y. & Fry-McKibbin, R.A. (2019). A Regime Switching Skew-Normal Model of Contagion. Studies in Nonlinear Dynamics and Econometrics, 23, 20170001.
  6. Chib, S. (1996). Calculating Posterior Distributions and Modal estimates in Markov Mixture Models. Journal of Econometrics, 75, 79-97. https://doi.org/10.1016/0304-4076(95)01770-4
  7. Chopra, M. & Mehta, C. (2022). Is the COVID-19 pandemic more contagious for the Asian stock markets? A comparison with the Asian financial, the US subprime and the Eurozone debt crisis. Journal of Asian Economics, 79, 101450.
  8. Doornik, J.A. & Hansen, H. (2008). An Omnibus Test for Univariate and Multivariate Normality. Oxford Bulletin of Economics and Statistics, 70, 927-939. https://doi.org/10.1111/j.1468-0084.2008.00537.x
  9. Forbes, K.J., & Rigobon, R. (2002). No Contagion, Only Interdependence: Measuring Stock Market Comovements. The Journal of Finance, 57, 2223-61. https://doi.org/10.1111/0022-1082.00494
  10. Fruhwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models, Springer, New York.
  11. Fry-McKibbin, R.A. & Hsiao, C.Y. (2018). Extremal Dependence and Contagion. Econometric Reviews, 37, 626-649. https://doi.org/10.1080/07474938.2015.1122270
  12. Fry-McKibbin, R.A., Martin, V.L. & Tang, C. (2010). A New Class of Tests of Contagion with Applications. Journal of Business and Economic Statistics, 28, 423-36. https://doi.org/10.1198/jbes.2010.06060
  13. Fry-McKibbin, R.A., Martin, V.L. & Tang, C. (2014). Financial Contagion and Asset Pricing. Journal of Banking & Finance, 47, 296-308. https://doi.org/10.1016/j.jbankfin.2014.05.002
  14. Fry-McKibbin, R.A., Hsiao, C.Y. & Martin, V.L. (2019). Joint Tests of Contagion with Applications. Quantitative Finance, 19, 473-490. https://doi.org/10.1080/14697688.2018.1475747
  15. Geweke. J. (1991). Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities. Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, 571-78.
  16. Geweke, J. (2010). Complete and Incomplete Econometric Models, Princeton University Press.
  17. Guidolin, M., & Tam, Y.M. (2013). A Yield Spread Perspective on the Great Financial Crisis: Break-Point Test Evidence. International Review of Financial Analysis, 26, 18-39. https://doi.org/10.1016/j.irfa.2012.05.001
  18. Hamilton, J.D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57, 2, 357-84. https://doi.org/10.2307/1912559
  19. Jeffreys, H. (1961). Theory of Probability, Clarendon Press, Oxford, Third Edition.
  20. Kim, B. (2016). A Robustified Jarque-Bera Test for Multivariate Normality. Economics Letters, 140, 48-52. https://doi.org/10.1016/j.econlet.2016.01.007
  21. Kroese, D.P., Taimre, T. & Botev, Z.I. (2011). Handbook of Monte Carlo Methods, John Wiley & Sons, New York.
  22. Liu, J., Wu, S. & Zidek, J.V. (1997). On segmented multivariate regressions. Statistica Sinica, 7, 497-525.
  23. Ljung, G.M. & Box, G.E.P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303. https://doi.org/10.1093/biomet/65.2.297
  24. Mardia, K. (1970). Measures of Multivariate Skewness and Kurtosis with Applications. Biometrika, 57, 519-530. https://doi.org/10.1093/biomet/57.3.519
  25. Perron, P. (1997). L'estimation de modeles avec changements structurels multiples. Actualite Economique ,73, 457-505. https://doi.org/10.7202/602236ar
  26. Robert, C.P. (1995). Simulation of Truncated Normal Variables. Statistics and Computing, 5, 121-125. https://doi.org/10.1007/BF00143942
  27. Sahu, S.K., Dey, D.K. & Branco, M.D. (2003). A New Class of Multivariate Skew Distributions with Applications to Bayesian Regression Models. The Canadian Journal of Statistics, 31, 129-150. https://doi.org/10.2307/3316064
  28. Yao, Y. C. (1988). Estimating the number of change-points via Schwarz' criterion. Statistics and Probability Letters, 6, 181-189. https://doi.org/10.1016/0167-7152(88)90118-6
  29. Zhou, M. & Shao, Y. (2014). A Powerful Test for Multivariate Normality. Journal of Applied Statistics, 41, 351-363. https://doi.org/10.1080/02664763.2013.839637