Dynamic Interactive Cycles during the 2008 Financial Crisis

Ioannis M. Neokosmidis, Vassilis Polimenis

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DOI: 10.4236/me.2010.11001                

Abstract

This paper focuses on the analysis of the 2008 financial crisis and how it affects the global financial markets. We analyze three major markets (US, UK, and ASIA) that are represented by the levels of three broad stock indices S&P 500, FTSE 100 and Hang Seng respectively. Our methodology is based on cointegration analysis and Granger causality test in order to examine the interaction between the markets (information flows). Additionally, we study the volatility transmission based on multivariate GARCH analysis. We find significant changes in information flows before and during the financial crisis.

Keywords

Unit Root Test, Cointegration, Granger Causality, Multivariate Volatility Processes, Financial Crisis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	K. J. Forbes and R. Rigobon, “Contagion in Latin America: Definitions, Measurement, and Policy Implications,” Economia, Vol. 1, No. 2, 2001, pp. 1-46.
[2]	A. Lo and A. C. Mackinlay, “An Econometric Analysis of Nonsynchronous Trading,” Journal of Econometrics, Vol. 45, No. 1-2, 1990, pp. 181-211.
[3]	R. Engle, “Autoregressive Conditional Heteroskedasticity with Estimates of theVariance of U.K. Inflation,” Econometrica, Vol. 50, No. 4, 1982, pp. 987-1008.
[4]	R. Engle, “Arch Selected Readings,” Oxford University Press, Oxford, 1995.
[5]	R. Engle, “The use of ARCH/GARCH Models in Applied Econometrics,” Journal of Economic Perspectives, Vol. 15, No. 4, 2001, pp. 157-168.
[6]	T. Bollerslev, “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, Vol. 31, No. 3, 1986, pp. 307-327.
[7]	T. Bollerslev, R. F. Engle and D. B. Nelson, “ARCH Models,” In: R. Engle and D. McFadden, Eds., Handbook of Econometrics, North Holland Press, Amsterdam, 1994.
[8]	D. A. Dickey and W. A. Fuller, “Distributions of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of American Statistical Association, Vol. 74, No. 366, 1979, pp. 427-481.
[9]	D. A. Dickey and W. A. Fuller, “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, Vol. 49, No. 4, 1981, pp. 1057-1072.
[10]	H. Akaike, “Information Theory and an Extension of the Maximum Likelihood Principle,” In: B. N. Petrov and Csaki, Eds., 2nd International Symposium on Information Theory, Academia Kiado, Budapest, 1973, pp. 267-281.
[11]	R. Harris and R. Sollis, “Applied Time Series Modelling and Forecasting,” John Wiley, New York, 2003.
[12]	R. F. Engle and C. W. J. Granger, “Cointegration and Error Correction: Representation, Estimation and Testing,” Econometrica, Vol. 55, No. 2, 1987, pp. 251-276.
[13]	C. Gourieroux and J. Jasiak, “Financial Econometrics,” Princeton University Press, Princeton and Oxford, 2001.
[14]	R. Engle, “Wald, Likelihood Ratio and Lagrange Multiplier Tests in Econometrics,” In: Z. Griliches and M. D. lntriligator, Eds., Handbook of Econometrics II, 1983, pp. 796-801.
[15]	T. Bollerslev, R. Engle and J. M. Wooldridge, “A Capital-Asset Pricing Model with Time-Varying Covariances,” Journal of political Economy, Vol. 96, No. 1, 1988, pp. 116-131.
[16]	R. Tsay, “Analysis of Financial Time Series,” John Wiley & Sons, New Jersey, 2005.
[17]	T. Bollerslev, “Modeling the Coherence in Short-Term Nominal Exchange Rates: A Multivariate Generalized ARCH Approach,” Review of Economics and Statistics, 1990.