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Change point analysis in Bitcoin return series : a robust approach
Communications for Statistical Applications and Methods 2021;28:511-520
Published online September 30, 2021
© 2021 Korean Statistical Society.

Junmo Song a, Jiwon Kang1,b

aDepartment of Statistics, Kyungpook National University, Korea;
bDepartment of Computer Science and Statistics, Jeju National University, Korea
Correspondence to: 1 Department of Computer Science and Statistics, Jeju National University, 102, Jejudaehak-ro, Jeju-si, Jeju-do, Korea. E-mail: jwkang@jejunu.ac.kr
Received April 9, 2021; Revised May 18, 2021; Accepted May 21, 2021.
Over the last decade, Bitcoin has attracted a great deal of public interest and Bitcoin market has grown rapidly. One of the main characteristics of the market is that it often undergoes some events or incidents that cause outlying observations. To obtain reliable results in the statistical analysis of Bitcoin data, these outlying observations need to be carefully treated. In this study, we are interested in change point analysis for Bitcoin return series having such outlying observations. Since these outlying observations can affect change point analysis undesirably, we use a robust test for parameter change to locate change points. We report some significant change points that are not detected by the existing tests and demonstrate that the model allowing for parameter changes is better fitted to the data. Finally, we show that the model with parameter change can improve the forecasting performance of Value-at-Risk.
Keywords : Bitcoin, GARCH model, change point analysis, parameter change, robust, outlying observations