Detecting Multiple Breaks in Financial Market Volatility Dynamics

tests detect change-points in the volatility dynamics which are associated
with the Asian and Russian financial crises. The empirical analysis is also
performed using high frequency data from the FX markets. The above tests
are applied to the Yen∕USS class of data driven volatility filters in an attempt
to provide more efficient approximations of the quadratic variation of the
process for which we also detect change-points.

References

[1] Aggarwal, R., Inclan, C. and R. Leal (1999), Volatility in Emerging
Stock Markets”, Journal of Financial and Quantitative Analysis, 34, 1-.

[2] Andersen, T. and T. Bollerslev (1998), “Answering the Skeptics: Yes,
Standard Volatility Models Do Provide Accurate Forecasts”, Interna-
tional Economic Review, 39, 885-905.

[3] Andersen, T., T. Bollerslev, F. X. Diebold and P. Labys (2001), “The
Distribution of Exchange Rate Volatility”, Journal of American Statis-
tical Association, 96, 42-55.

[4] Andreou, E. (2002), “Testing for change-points in long memory volatility
models for stock returns” Mimeo, University of Cyprus.

[5] Andreou, E. and E. Ghysels (2002), “Rolling sample volatility estima-
tors: some new theoretical, simulation and empirical results” Journal of
Business and Economic Statistics, forthcoming.

[6] Andrews, D. W. K. (1991), “Heteroskedasticity and Autocorrelation
Consistent Covariance Matrix Estimation,” Econometrica, 59, 817-858.

[7] Bai, J. (1994), “Least squares estimation of a shift in linear process”,
Journal of Time Series Analysis, 15, 453-472.

[8] Bai, J. (1997), “Estimating Multiple Breaks One at a Time”, Econo-
metric Theory, 13, 315-351.

[9] Bai, J. and Perron, P. (1998), “Estimating and Testing Linear Models
with Multiple Structural Changes, Econometrica, 66, 47-78.

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