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http://dx.doi.org/10.5351/KJAS.2022.35.3.395

Outlier detection for multivariate long memory processes  

Kim, Kyunghee (Department of Statistics, Sungkyunkwan University)
Yu, Seungyeon (Department of Statistics, Sungkyunkwan University)
Baek, Changryong (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.3, 2022 , pp. 395-406 More about this Journal
Abstract
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.
Keywords
outlier detection; multivariate long memory process; VHAR; robust regression;
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