Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
學年 108
學期 2
出版(發表)日期 2020-04-01
作品名稱 Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
作品名稱(其他語言)
著者 Jui-Cheng Hung; Hung-Chun Liu; J. Jimmy Yang
單位
出版者
著錄名稱、卷期、頁數 North American Journal of Economics and Finance、52、101165
摘要 This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing
關鍵字 Bitcoin; Realized GARCH model; Jump-robust realized measure; Realized bi-power variation; Realized tri-power variation
語言 en
ISSN 1062-9408
期刊性質 國外
收錄於 SSCI NotTSSCI
產學合作
通訊作者
審稿制度
國別 USA
公開徵稿
出版型式 ,電子版