A TEST FOR CONDITIONAL HETEROSKEDASTICITY IN TIME SERIES MODELS

Anil Kumar Bera · Matthew Higgins

47 citations0/0 files storedJournal of Time Series Analysis

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Financial Risk and Volatility Modeling Monetary Policy and Economic Impact Advanced Statistical Methods and Models

Abstract

Abstract. When testing for conditional heteroskedasticity and nonlinearity, the power of the test in general depends on the functional forms of conditional heteroskedasticity and nonlinearity that are allowed under the alternative hypothesis. We suggest a test for conditional heteroskedasticity and nonlinearity with the nonlinear autoregressive conditional heteroskedasticity model of Higgins and Bera as the alternative. Standard testing procedures are not applicable since our nonlinear autoregressive conditional heteroskedasticity (ARCH) parameter is not identified under the null hypothesis. To resolve this problem, we apply the procedure recently proposed by Davies. Power and size of the suggested test are investigated through simulation, and an empirical application of testing for ARCH in exchange rates is also discussed.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1992ARCH modeling in finance 1986Modelling the persistence of conditional variances 1992A Class of Nonlinear Arch Models 1984ARMA MODELS WITH ARCH ERRORS 1988ON THE CORRELATION STRUCTURE FOR THE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTIC PROCESS

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