The Efficacy of Model-Based Volatility Forecasting: Empirical Evidence in Taiwan

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题名:	The Efficacy of Model-Based Volatility Forecasting: Empirical Evidence in Taiwan
作者:	臧仕維;Tzang, Shyh-Weir;Chih-Hsing Hung;So-De Hsyu
贡献者:	財務金融學系
关键词:	Realized volatility;range volatility;semiparametric fractional autoregressive model;multiplicative error model;GJR-GARCH model;variance gamma garch model
日期:	2009-04
上传时间:	2012-11-26 02:37:31 (UTC+0)
摘要:	The paper adopts several time series models to assess the forecasting efficiency of future realized volatility in Taiwan stock market. The paper finds that, for 1-day directional accuracy forecast performance, semiparametric fractional autoregressive model (SEMIFAR, Beran and Ocker, 2001) ranks highest with 78.52% hit accuracy, followed by multiplicative error model (MEM, Engle, 2002), and augmented GJR-GARCH model. For 1-day forecasting errors evaluated by root mean squared errors (RMSE), GJR-GARCH model augmented with high-low range volatility ranks the highest, followed by SEMIFAR and MEM model, both of which, however, outperform augmented GJR-GARCH by the measure of mean absolute value (MAE) and p-statistics (Blair, Poon and Taylor, 2001).
關聯:	International Research Journal of Finance and Economics
显示于类别:	[財務金融學系] 期刊論文

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