本篇論文的目的在驗證台股期貨報酬率與其波動度之間的相對應關係是由槓桿效果或是波動度回饋效果之因果關係所驅動，並且分別以日資料以及高頻率日內資料進行實證。實證結果發現在高頻率日內資料的應用下，能夠比日資料揭露出更詳細的波動度資訊，將報酬率與波動度間的對應關係描繪得更加明瞭。且在大多數資料期間內，同期下，台股期貨報酬率與其波動度之間會呈現負相關性，而負相關的程度會隨著報酬率遞延期數越長而逐漸遞減，因此可以發現報酬率與其波動度間呈現一個經由報酬率進而影響波動度的對應關係，與槓桿效果的因果關係雷同。最後，本文亦採用了常見的波動度預測模型，歷史模擬法、GARCH(1,1)模型、EGARCH(1,1)模型以及GJR-GARCH(1,1)模型，觀察這些波動度模型所預測出之波動度是否含有上述驗證的資訊意涵，並比較各波動度模型的預測能力，結果發現GJR-GARCH模型於樣本外期間所預測之波動度，其與報酬率之間不但具有槓桿效果的因果關係，且預測能力亦於四個波動度模型中表現最佳。

時間序列分析 總體經濟與財務金融之應用，陳旭昇著
Anderson, T.G., Bollerslev, T., Diebold, F.X. and Ebens, H.(2001).”The Distribution of Stock Return Volatility.” , Journal of Financial Economics 61(1), 43-76
Anderson, T.G., Bollerslev, T., Diebold, F.X. and Labys, P.(2003)”Modeling and Forecasting Realized Volatility.”, Econometrica 71, 579-625.
Awartani, B.M.A.,and Corradi, V.(2005).” Predicting the Volatility of the S&P-500 Ttock Index via GARCH Models: The Role of Asymmetries.”, International Journal of Forecasting 21 , 167-183.
Bekaert, G., and G. Wu.(2000).”Asymmetric Volatility and Risk in Equity Markets.”, The Review of Financial Studies 13, 1-42.
Black, F.(1976).”Studies of Stock Price Volatility Changes.”, Proceeding of the 1976 Meetings of the American Statisrical Association, Business and Economic Statistics PP. 177-181.
Bollerslev, T.,(1986). ”Generalized Autoregressive Conditional Heteroskeasticity”, Journal of Econometrics 31, 307-327
Bollerslev, T., Litvinova, J., and Tauchen, G.(2006).”Leverage and Volatility Feedback Effects in High-Frequency Data.”, Journal of Financial Econometrics 4, 353-384.
Campbell, J.Y., and L. Hentscel.(1992).”No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns.” , Journal of Financial Economics 31, 281-331.
Christie, A.C.(1982).”The Stochastic Behavior of Common Stock Variances – Value, Leverage and Interest Rate Effects.” , Journal of Financial Economics 3, 145-166.
Duffee, G.R.(1995).”Stock Returns and Volatility , A Firm–level analysis.”, Journal of Financial Economics 37, 399-420.
Dufour, J.M., Garcia, R., and Taamouti, A.(2008)”Measuring Causality Between Volatility and Returns with High-Frequency Data.”, Working Paper, Department of Econometircs, Universidad Carlos III de Madrid.
Figlewski, S., and X. Wang.(2001).”Is the ‘Leverage Effect’ a Leverage Effect?”, Working paper, Department of Finance, New York University.
French, K.R., Schwert, G.W. and Stambaugh, R.F.(1987).”Expected Stock Returns and Volatility.” , Journal of Financial Economics 19, 3-30.
Ghysels, E., Santa-Clara, P. and Valkanov, R.(2005).”There is a Risk-Return Tradeoff After All.” , Journal of Financial Economics 76, 509-548.
Glosten, L.R., R. Jagannathan, and D.E. Runkle(1993).”On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.”, Journal of Finance 48, 1779-1801.
Guan, W.(2004).” Forecasting Volatility.”, Journal of Futures Markets 25, 465-490.
Kisinbay, T.(2003).” Predictive Ability of Asymmetric Volatility Models at Medium-Term Horizons.”, IMF Working Paper, 03/131.
Poon, S.H., and Granger, C.W.J.(2003).”Forecasting Volatility in Financial Markets: A Review.”, Journal of Economic Literature 41, 478-539.
Masset, P.(2008)”Properties of High Frequency DAX Returns: Intraday patterns, Jumps and their Impact on Subsequent Volatility.”, Working Paper, Department of Finance and Accounting, University of Fribourg.
Mcmillan, D.G., and Speight, A.E.H.(2003).” Asymmetric Volatility Dynamics in High Frequency FTSE-100 Stock Index Futures.”, Applied Financial Economics 13, 599-607
Nelson, Daniel B.(1991).”Conditional Heteroskedasticity in Asset Returns: A New Approach.”, Econometrica 59, 347-370.
Wu, G.(2001).”The Determinants of Asymmetric Volatility.”, Review of Financial Studies 14,837-859.