淡江大學機構典藏:Item 987654321/31497
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    Title: 金融商品波動性預測
    Other Titles: The forecasting volatility of financial goods
    Authors: 許能凱;Hsu, Neng-kai
    Contributors: 淡江大學財務金融學系碩士班
    李命志;Lee, Ming-chih
    Keywords: 波動性預測;一般化自我迴歸異質條件變異數模型;限制最小平方估計式模型;一般化分散落後形式模型;forecasting volatility;GARCH;the restricted least squares(RLS);GEN
    Date: 2006
    Issue Date: 2010-01-11 00:50:06 (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定價的正確性。本研究以三種不同類別的金融市場,包含股價指數、匯率及個股股價,一共六種金融商品為研究對象,進行六個波動性估計模型之預測能力之比較,包括STD、EWMA、GARCH(1,1)-N、GARCH(1,1)-G、及Ederington and Guan (2005)所提出的RLS和GEN模型。以下列幾個議題來探討何種模型的預測績效最佳:1、過去報酬衝擊權重設定的適當性。2、參數估計值與估計程序的關聯性3、採用四種評估預測績效之方法,希望藉以判斷模型預測對評估方法是否敏感,以及模型的預測績效是否具有普遍性。期望透過本研究之實證分析,期待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。實證結果發現:
    1、 GARCH(1,1)放太多的權重在最新的觀察上,而給予較舊的觀察值之權重是不夠的。
    2、 對於相同的模型用不同的參數估計程序,則會導致完全不同的參數估計值。
    3、 整體而言不論在何種金融市場均以GEN模型的預測績效最好,其次為RLS模型,而GARCH(1,1)-G模型普遍優於GARCH(1,1)-N模型。
    We apply Ederington and Guan (2005), to examine the forecasting ability of sixth time-series volatility models, including historical variance, EWMA, GARCH(1,1)-N, GARCH(1,1)-G, and restricted least squares (RLS) and GEN. We seek to determine why one model or group of models forecasts another focusing on three issues: 1、the proper weighting of older versus recent observations, 2、the relevance of the parameter estimation procedure, and 3、we use four criterions to measure forecast ability. Our evidence indicates
    1、The GARCH(1,1)model puts too much weight on the most recent observations and not enough on older observations.
    2、Different parameter estimation procedures result in quite different parameter estimates for the same model.
    3、The GEN model is the best volatility forecasting model, RLS model is the second, and GARCH(1,1)-G model is always superior to GARCH(1,1)-N model.
    Appears in Collections:[Graduate Institute & Department of Banking and Finance] Thesis

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