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條件高階動差於財務金融市場之應用

A New Parametric Approach to Modeling Generalized Autoregressive Conditional Density Model at Higher Order Moments

摘要


本文創新提出一般化自我迴歸條件密度Moments-in-Mean(ARCD Moments-in-Mean)模型,除了允許高階動差受外在訊息影響而隨時間變化外,並將條件變異數、條件偏態及條件峰態序列納入報酬方程式,評估不同風險型態動差特性對預期報酬的影響。實証結果顯示,股票市場偏態及峰態特性受外在訊息影響而隨時間改變;其次,高階動差風險訊息對價格變動具顯著影響,說明完整描述高階動差特性的重要性;再者,衡量市場上漲下跌傾向的偏態特性,對價格呈現高度解釋效力。此外,波動不對稱性可能來自於偏態特性的欠缺考量,証實分佈假設對於波動行為探討的重要性。

並列摘要


This paper presents a flexible Autoregressive Conditional Density Moments-in-Mean (ARCD Moments-in-Mean) model. Our innovative approach not only allow the higher order moments to be time-varying function of conditioning information, but also extend the traditional ARCH-M model by explicitly modeling the influence of conditional moments (conditional variance, skewness and kurtosis) on the conditional expectation of the data series. The empirical results suggest a preponderance of evidence to support the performance of ARCD Moments-in-Mean model on competing alternatives. Of particular, the time varying skewness, capturing the up/downside risk, is found to exhibit significant effect in explaining the expected return than that of second and fourth moment. In addition, it is also found that the asymmetry of conditional distribution is potentially possible to substitute the asymmetry in the volatility specification demonstrating the importance to specify the proper distribution specification on financial econometric modeling.

並列關鍵字

GARCH model skewness kurtosis moments risk premium distribution

參考文獻


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被引用紀錄


林秀蓉(2008)。股價波動性預測〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2008.00973
陳佳琪(2008)。原油價格波動性預測〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2008.00971
劉筱盈(2007)。高階動差的國際資訊傳遞效果-台灣與美國股價指數之實證研究〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2007.01035
吳家華(2007)。條件偏態於資產定價上之應用-台灣證券市場之實證研究〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2007.00724
Shen, Y. H. (2011). 台灣股票市場中個別公司報酬率風險和報酬的關係 [master's thesis, National Central University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0031-1903201314422407

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