本研究衡量不同的訊息衝擊下影響期貨市場報酬率和成交量關係的相對重要性，以及商品交易的熱絡頻繁程度對訊息衝擊的反應是否具有效率性。實證採用雙變數移動平均模型(BMAR)和雙變數向量自我迴歸模型(BVAR)，選取台灣指數期貨市場中大型台指期貨、小型台指期貨、電子指數期貨以及金融指數期貨等四種期貨商品，探討不同屬性的訊息對期貨市場價量關係的衝擊。並透過預測變異數分解及衝擊反應分析進一步瞭解期貨報酬率與成交量對訊息衝擊的動態反應，有助於我們了解價格變動的影響因素及影響方式。研究結果如下：
一、資訊性/恆久性訊息衝擊是解釋期貨報酬波動最有力的解釋因子，而非資訊性/短暫性衝擊為最能解釋成交量變動的主要因素。
二、資訊性/恆久性訊息的衝擊對期貨報酬率的變動是即刻且具實質劇烈影響效果的，但影響的程度在短時間內即顯著的降低，也就是市場調整的速度非常快速。
三、當衝擊來自非資訊性/短暫性訊息時，對成交量變化除了具有初期實質影響效果，其影響程度是隨時間演變呈現緩慢降低的現象。
四、在交易較熱絡頻繁的期貨商品(大型台指期貨)和相對較不頻繁的商品(金融指數期貨)兩市場之間，對訊息反應的程度確實存在著效率性差異。

This paper investigates the response of returns and volume to different information shocks in Taiwan Index Futures Markets using bivariate moving average representation (BMAR) and bivariate vector autoregression (BVAR) methodologies. In particular, the related component of returns and volume of futures due to different information shocks are identified, and the relative importance of each component are examined. Through variance decomposition and impulse response analysis, we are also able to see how futures returns and volume dynamically respond to the information shocks. The empirical results are as follows：
1.It is found that the informational/permanent components are the dominant components for returns movements, and the noninformational/ transitory components are the dominant components for trading volume.
2.Informational/permanent shocks initially have a substantial effect on futures returns, but this effect declines dramatically, which means this adjustment is very rapid.
3.Noninformational/transitory shocks initially have a substantial effect on trading volume, and this effect declines gradually over time.
4.The market response of actively traded futures (TX) is greater than that of the less actively traded futures (TF).

目錄	
第一章 緒論   ............................................1
第一節 研究背景與動機.....................................1
第二節 研究目的...........................................5
第三節 研究架構...........................................6
第二章 文獻回顧...........................................8
第一節 訊息之定義與屬性...................................9
第二節 市場訊息揭露傳遞與資訊不對稱......................12
第三節 訊息衝擊之傳遞與價量間關係........................15
第三章 研究方法..........................................17
第一節 單根檢定..........................................18
第二節 BMAR,BVAR模型.....................................20
第三節 衝擊反應函數......................................25
第四節 預測變異數分解....................................25
第四章 資料選取與敘述統計................................26
第一節 資料與樣本選取....................................26
第二節 基本敘述統計分析..................................28
第五章 實證結果與分析....................................30
第一節 去除趨勢化(detrend)之成交量結果分析...............30
第二節 單根檢定結果分析..................................32
第三節 預測變異數分解結果分析............................34
第四節 衝擊反應函數結果與分析............................40
第六章 結論與建議........................................48
第一節 結論..............................................48
第二節 建議..............................................49
參考文獻.................................................50

表次	
【表4-1】樣本說明........................................27
【表4-2】台灣指數期貨市場統計資料表......................29
【表5-1】期貨成交量趨勢定態結果檢定表....................31
【表5-2】期貨報酬率單根檢定結果..........................32
【表5-3】期貨成交量與趨勢定態後之單根檢定結..............33
【表5-4】大型台指期貨成交量與報酬率變異數分解結果........36
【表5-5】小型台指期貨成交量與報酬率變異數分解結果........37
【表5-6】電子指數期貨成交量與報酬率變異數分解結果........38
【表5-7】金融指數期貨成交量與報酬率變異數分解結果........39	
	
圖次	
【圖1-1】研究架構圖.......................................7
【圖5-1】大型台指期貨資訊性/非資訊性之衝擊反應分析圖.....40
【圖5-2】大型台指期貨恆久性/短暫性之衝擊反應分析圖.......41
【圖5-3】小型台指期貨資訊性/非資訊性之衝擊反應分析圖.....42
【圖5-4】小型台指期貨恆久性/短暫性之衝擊反應分析圖.......43
【圖5-5】電子指數期貨資訊性/非資訊性之衝擊反應分析圖.....44
【圖5-6】電子指數期貨恆久性/短暫性之衝擊反應分析圖.......45
【圖5-7】金融指數期貨資訊性/非資訊性之衝擊反應分析圖.....46
【圖5-8】金融指數期貨恆久性/短暫性之衝擊反應分析圖.......47

參考文獻

英文文獻

Amihud, Y., & H. Mendelson (1987). Trading mechanisms and stock returns: an empirical investigation. Journal of Finance, 42, 533-553.

Admati, A., & Paul Pfleiderer (1988). Selling and trading on information in financial markets. The American Economic Review, Vol.78, No.2, 96-103

Bagehot, W.(1971). The only game in Town. Financial Analysts Journal, March-April, 12-22.

Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79, 655-673.

Campbell, J. Y., S. J. Grossman and J. Wang. (1993). Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics, Vol.108, No.4, pp905-939

Chung, H., & Lee, B. S. (1998). Fundamental and nonfundamental components in stock prices of Pacific-Rim countries. Pacific-Basin Finance Journal, 6, 321–346.

Copeland, T. E. (1976). A model of asset trading under the assumption of the sequential information arrive. Journal of Finance, 31, 1149-1168.

DeLong, J. A., Shleifer, L.S., &Waldmann, B. (1990). Positive feeback, investment strategies and destabilizing rational speculation. Journal of Finance, 45, 379-395

Fama, E. F., & French, K. (1988). Permanent and temporary components of stock prices. 
Journal of Political Economy, 96, 246–273.

French, K., & Roll, R. (1986). Stock return variances: The arrival of information and the reaction of traders. Journal of Financial Economics, 17, 5–26.

Gallent, A. R., Rossi, P. E., & Tachen, G. (1992). Stock prices and volume. Review of Financial Studies, 5, 199-242

Chen, G., Michael F., & Xin, Y. (2005). The response of volume and returns to the information shocks in China’s commodity futures markets. 
Journal of Futures Markets, Vol.25, No.9, 893-916

Grossman, S. J (1976). On the Efficiency of Competitive Stock Markets where Trades have Diverse Information. Journal of Finance, Vol.31, No.2, 573-585

Grossman, S. J. and Stiglity, J. E(1980). On the Impossibility of Informationally Efficient Markets. The American Economic Review, 393-408

Harvey, C. R., & Huang, R. D.(1991). Volatility in foreign currency futures markets. Review of Financial Studies, 4, 543-569.

Jennings, R. H., Starks, L. T., and Fellingham, J. C. (1981). An equilibrium model of asset trading with sequential information arrival. Journal of Finanace, 36, 143-161.

Jones, C. M., Kaul, G., & Lipson, M, L.(1994). Information, trading, and volatility. Journal of Financial Economics, 36, 127-154.

Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22,109–126.

Kyle, Albert S., (1985). Continuous auctions and insider trading. Econometrica, Vol.53, 1315-1335

Larry H.P.Lang; Robert H. Litzenberger;& Vicente Madrigal (1992). Testing Financial Market Equilibrium under Asymmetric Information. The Journal of Political Economy, Vol.100, No.2, 317-348.

Lee, B. S. (1995). The response of stock prices to permanent and temporary shocks to dividends. Journal of Financial and Quantitative Analysis,30, 1–22.

Lee, B. S., & Rui, O. M. (2001). Empirical identification of non-informational trades using trading volume data. Review of Quantitative Finance and Accounting, 17, 327-350.

Pfleiderer, P. (1984). The Volume of trade and the variability of prices: A framework for analysis in noisy rational expectations equilibria. Working Paper, Stanford Univ.

Poterba, J., & Summers, L. (1988). Mean reversion in stock returns: Evidence and implications. Journal of Financial Economics, 22, 27-60.

Quah, D. (1992). The relative importance of permanent and transitory components: Identification and some theoretical bounds. Econometrica, 60,107–118.

Wang, J., (1994). A Model of Competitive Stock Trading Volume. Journal of Political Economy, 102, 127-168.

中文文獻

陳正博，「訊息不對稱對股票報酬影響之研究-以台灣股票市場為例」，逢甲大學經濟學研究所碩士論文，民國92年