A New Test for ARCH Effects and Its Finite-Sample Performance

Yongmiao Hong · Ramsey D. Shehadeh

47 citations0/0 files storedJournal of Business and Economic Statistics

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Financial Risk and Volatility Modeling Advanced Statistical Methods and Models Complex Systems and Time Series Analysis

Abstract

We propose a test for autoregressive conditional heteroscedasticity based on a weighted sum of the squared sample autocorrelations of squared residuals from a regression, typically with greater weight given to lower-order lags. The tests of Engle, Box and Pierce, and Ljung and Box are equivalent to the test with equal weighting. Our test does not require formulation of an alternative and permits choice of the lag number via data-driven methods. Simulation studies show that the new test performs reasonably well in finite samples especially with greater weight on lower-order lags. We apply the test in two empirical examples.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1992ARCH modeling in finance 1991Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation 1996A test for independence based on the correlation dimension 1994Automatic Lag Selection in Covariance Matrix Estimation 1992Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances 1987Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model

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2015Tests for serial dependence in static, non-Gaussian factor models 2001ONE-SIDED TESTING FOR ARCH EFFECTS USING WAVELETS 2020New testing approaches for mean–variance predictability

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