Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume

被引：37
|
作者：
Khuntia, Sashikanta [1 ]
Pattanayak, J. K. [1 ]
机构：
[1] Indian Sch Mines, Indian Inst Technol, Dept Management Studies, Dhanbad, Bihar, India
来源：
FINANCE RESEARCH LETTERS | 2020年 / 32卷
关键词：
Bitcoin; Cryptocurrencies; Volatility; Long memory; Adaptive market hypothesis; MARKET HYPOTHESIS; INEFFICIENCY;
D O I：
10.1016/j.frl.2018.12.025
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
This paper evaluates the adaptive pattern of long memory in the volatility of intra-day bitcoin returns. It also tests the impact of the trading volume on time-varying long memory. Our finding confirms long memory in the volatility of intra-day bitcoin returns is not an all-or-nothing phenomenon; it is adaptive to change in time and creation of events and, therefore, adheres to the proposition of the adaptive market hypothesis. This paper reveals the explanatory power of trading volume on long memory during bearish and bullish movements.
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页数：8
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