• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.291-305
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• 2014

Persistence is one of the typical characteristics appearing in the volatility of financial time series. According to the recent researches, the volatility persistence may be due to either volatility shifts or long-range dependence. In this paper, we consider residual-based CUSUM tests to distinguish volatility persistence, long-range dependence and volatility shifts in GARCH models. It is observed that this test procedure achieve reasonable powers without a size distortion. Moreover, we employ AIC and BIC criteria to estimate the change points and the number of change points in volatility. We demonstrate the superiority of residual-based CUSUM tests on various Monte Carlo simulations and empirical data analysis.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.