Testing for changes in the unconditional variance of financial time series.

Vicente Aragó Manzana · Andreu Sansó · Josep Lluís Carrion‐i‐Silvestre

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Financial Risk and Volatility Modeling Monetary Policy and Economic Impact Complex Systems and Time Series Analysis

Abstract

Inclan and Tiao (1994) proposed a test for the detection of changes in unconditional variance which has been used in financial time series analyses. In this article we demonstrate some serious drawbacks to the test when used with this type of data. More specifically, it has big size distortions for leptokurtic and platykurtic innovations. Moreover, the size distortions are more extreme for heteroskedastic conditional variance processes. These results invalidate the test's practical use for financial time series. To overcome these problems, we propose new tests that take the fourth order moment properties of disturbances and conditional heteroskedasticity into explicit account. Monte Carlo experiments demonstrate the better performance of these tests. The new tests' application to the same series in Aggarwal, Inclan and Leal (1999) reveal that the changes in variance they detect are spurious.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1988Testing for a Unit Root in Time Series Regression 1992ARCH modeling in finance 1994Automatic Lag Selection in Covariance Matrix Estimation 1990Testing for a Unit Root in a Time Series With a Changing Mean 1994Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance 1990Stationarity and Persistence in the GARCH(1,1) Model

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