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    Title: 現貨價格可測下最小變異避險策略
    Other Titles: Hedging strategy when spot price changes are partially predictable
    Authors: 黃淑茹;Huang, Shu-ju
    Contributors: 淡江大學財務金融學系碩士班
    李命志;Lee, Ming-chih
    Keywords: 原油期貨;避險;MVHR;OLS;BGARCH;Crude oil futures;hedge;MVHR;OLS;BGARCH
    Date: 2009
    Issue Date: 2010-01-11 01:03:34 (UTC+8)
    Abstract: 本文之研究標的為全球二十六個國家下之三十一個原油類市場之現貨與紐約商品交易所(NYMEX)的輕原油期貨,資料期間為1997年1月3日至2008年12月26日的週資料。由於在許多市場中,現貨價格的變動是可以部分被預測的,而當使用傳統迴歸模型估計避險比率時,卻忽略了預期現貨價格變動的因素。本文於傳統OLS模型與受限制OLS模型(含訊息)的研究過程中發現:(1)傳統迴歸模型估計出的最小變異避險比率雖具不偏性,但不具效率性;且(2)估計避險部位與不避險部位的風險時產生高估的情形;與(3)估計避險下風險降低的程度產生低估的情形,而在加入預期現貨價格變動因子後,將有助於提升避險比率的效率性。
    本文利用四種避險模型分析原油期貨之避險績效,使投資組合之報酬變異數為最小下的避險比率(MV)為最適避險比率,避險模型包含了OLS、受限制OLS(含訊息)、VAR、BGARCH模型,進行實證研究,比較在何種模型下的避險績效較佳。實證結果發現在比較四種避險模型之避險績效時,以雙變量GARCH模型為最佳避險績效模型,此外,更進一步發現最小變異避險比率之估計式會受到期初避險時新訊息的影響,當加入考量影響預期現貨價格改變的因子後,將提高迴歸結果之效率性及降低偏誤。
    The data used in the research are weekly prices of thirty-one crude oil markets in twenty-six countries and crude oil futures in NYMEX. The sample period extends from January, , 1997 to December, 26, 2008. In many markets, the changes in the spot price are partially predictable, but the traditional regression method is lack of the anticipated changes in the spot price. In this research, the traditional OLS model and restricted OLS model show the following case: (1) although unbiased, traditional regression estimates of the minimum variance hedge ratio are inefficient, (2) estimates of the risk of both hedged and un-hedged positions are biased upward, and (3) estimates of the percentage risk reduction achievable through hedging are biased downward.
    We research four major hedging model includong OLS、Restricted OLS、VAR、BGARCH, and use minimum variances hedge ratio(MVHR)approach to analyse which model gets the best hedge efficiency. For crude oil cross hedges, the bivariate GARCH model provides greater hedged efficiency than other models. Further find that, incorporating the expected change in the spot price, the regression results would be in a substantial increase in efficiency and reduction in the bias.
    Appears in Collections:[財務金融學系暨研究所] 學位論文

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