股價波動性預測

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Title:	股價波動性預測
Other Titles:	Forecasting the volatility of stock index returns
Authors:	林秀蓉;Lin, Hsiu-jung
Contributors:	淡江大學財務金融學系碩士班李命志;Lee, Ming-chih;吳佩珊;Wu, Pei-shan
Keywords:	波動性預測;GARCH;GJR GARCH;偏態;峰態;波動不對稱;Volatility forecasting;GARCH;GJR GARCH;GED;Skewness;Fat tails;Asymmetric
Date:	2008
Issue Date:	2010-01-11 01:06:02 (UTC+8)
Abstract:	波動性的預測攸關資產配置、投資組合避險、風險控管以及衍生性金融商品訂價的正確性。本研究以美國主要股價指數為主，包含道瓊平均工業指數、S&P 500綜合指數、那斯達克工業指數與費城半導體指數，一共4種美國主要指數為研究對象，進行波動性估計模型之預測能力比較。本研究係探討美國主要股價報酬之動態效果，考量在一般化GARCH模型與GJR GARCH模型下，其對模型的預測能力，另研究條件變異數是否能在分配不同的考量下，提昇模型之解釋能力。期望透過本研究之實證分析，可以獲得一個廣泛性預測較佳的模型，進一步提供投資決策者掌握金融資產報酬波動性的可行管道。實證結果：由RMSE與MAE比較模型之預測能力發現， GJR GARCH-GED模型優於GARCH-GED模型。此結果顯示出股價報酬存在波動不對稱的特性，即壞消息容易引發市場較大幅度的波動。此外，本研究發現分佈假設的妥適考量，對於估計效能提昇及參數估計之正確性具有重要影響力。 Volatility plays an important role in finance. If we can capture the characteristics of the motions of assets precisely, we could make good portfolios and control risks efficiently. This study investigates how specification of returns distribution influences the performance of volatility forecasting using two GARCH models and two GJR GARCH models（GARCH-N, GARCH-GED, GJR GARCH-N and GJR GARCH-GED）. Daily spot prices on the DOW, S&P500, NASDAQ and PHLX indices provide empirical sample for discussing and comparing the relative sample volatility predictive ability, given the growth potential of stock markets in America to the eyes of global investors. Empirical results indicate that the GJR GARCH-GED model is superior to the GARCH-GED model in forecasting U.S. stock market indices volatility, for all forecast horizons when model selection is based on RMSE or MAE. These findings show the signification of the asymmetry in the volatility specification. In other words, the empirical results show that bad news induces volatility greater than good news.
Appears in Collections:	[Graduate Institute & Department of Banking and Finance] Thesis

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