In this paper, we propose a new statistic to detect for variance change point under long memory, the null distribution of the test statistic is derived and the consistency is proved under the alternative hypothesis. In order to facilitate the practical application, we present a sieve bootstrap procedure which can give asymptotic correct critical value. Simulations show that the proposed method performs well both for small to large and large to small variance change point in long memory time series, Further more, we illustrated our method by a set of IBM stock data.

Ma Jianqi. Testing and Application for variance Change Points in Long Memory time series. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 2: 28-37. https://doi.org/10.25236/AJCIS.2020.030205.

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