研究生: |
林俊佑 Lin, Chun-Yu |
---|---|
論文名稱: |
台灣期貨市場頻繁交易人對期貨市場報酬波動的影響 The Impact of Frequent Traders on Return Volatility in Taiwan Futures Market |
指導教授: |
蔡蒔銓
Tsai, Shih-Chuan |
學位類別: |
碩士 Master |
系所名稱: |
管理研究所 Graduate Institute of Management |
論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 中文 |
論文頁數: | 41 |
中文關鍵詞: | 頻繁交易 、預期與未預期成交量 、GARCH模型 、Component GARCH模型 |
英文關鍵詞: | Frequent Trade, Expected and Unexpected Volume, GARCH Model, Component GARCH Model |
DOI URL: | http://doi.org/10.6345/THE.NTNU.GIM.003.2018.F08 |
論文種類: | 學術論文 |
相關次數: | 點閱:181 下載:0 |
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本研究主要目的為探討台灣期貨市場中,頻繁交易人對於期貨市場報酬波動的影響。我們以台灣期貨市場中的大台指期貨與小台指期貨為研究標的,並根據成交量大以及持倉率低兩個特徵篩選期貨市場中的頻繁交易人。將頻繁交易人成交量區分為預期與未預期兩部分,以GARCH模型及不對稱Component GARCH模型探討其對市場報酬波動的影響。
實證結果發現預期成交量與市場報酬波動呈正向顯著關係,未預期成交量與市場報酬波動呈負向關係但不顯著,不同過去多數文獻發現市場波動為未預期成交量所解釋。長期波動與短期波動亦皆由預期成交量所解釋,呈正向顯著關係。此外,本研究發現預期成交量對於短期波動的影響大於長期波動。
The main purpose of this study is to examine the impact of frequent traders on the return volatility in Taiwan futures market. We use the TAIEX Futures (TX) and the Mini-TAIEX Futures (MTX) as our research topic. In this study, Frequent traders are identified by two characteristics: large trading volume and low position level. Decomposing frequent traders’trading volume into expected and unexpected components, we use GARCH and asymmetric Component GARCH model to investigate influence of expected and unexpected trading volume on the market return volatility.
The results suggest a positive relationship between the expected trading volume of frequent traders and market return volatility. But there is a negative but non-significant relationship between the unexpected trading volume of frequent traders and market return volatility. On the other hand, we apply asymmetric Component GARCH model to decompose market return volatility into the permanent component and the transitory component. The results suggest that the expected trading volume of frequent traders is positively correlated with the permanent component and the transitory component. Furthermore, we find that the expected trading volume of frequent traders has larger effect on the transitory component than the permanent component.
林華德, &王甡. (1995)台灣股市成交量對股價波動的影響1986-1994: GARCH修正模型的應用. 企銀季刊, 19(2), 40-58.
劉亞秋.(1996). 台灣與香港股市成交量對股票報酬及其波動性關係之研究. 管理科學學報, 13(2),331-352.
王毓敏. (2003). 台股認購權證交易對於標的股票波動性的影響. 台灣金融財務季刊, 4(2), 65-79.
Aldridge, I. (2009). High-frequency trading: a practical guide to algorithmic strategies and trading systems (Vol. 459). John Wiley and Sons.
Bessembinder, H., & Seguin, P. J. (1993). Price volatility, trading volume, and market depth: Evidence from futures markets. Journal of financial and Quantitative Analysis, 28(1), 21-39.
Black, F. (1976). Studies of stock price volatility changes,Proceedings of the 1976 Meetings of the Business and Economic Statistics Section. 177-181.
Boehmer, E., Fong, K., & Wu, J. (2014).International evidence on algorithmic trading.
Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Brailsford, T. J. (1996). The empirical relationship between trading volume, returns and volatility. Accounting & Finance, 36(1), 89-111.
Brogaard, J. (2010). High frequency trading and its impact on market quality. Northwestern University Kellogg School of Management Working Paper, 66.
Brooks, C. (2002). Introductory econometrics for finance.Cambridge university press.
Clark, P. K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 135-155.
Clark, D., Awan R., Dietrich J. ,NgA., & KarsgaardA. (2009). The Impact of High Frequency on the Canadian Market, Toronto, BMO Capital Markets.
Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of financial Economics, 10(4), 407-432.
Christoffersen, P., Jacobs, K., Ornthanalai, C., & Wang, Y. (2008).Option valuation with long-run and short-run volatility components. Journal of Financial Economics, 90(3), 272-297.
Dichev, I. D., Huang, K., & Zhou, D. (2014).The dark side of trading. Journal of Accounting, Auditing & Finance, 29(4), 492-518.
Enders, W. (2008). Applied econometric time series.John Wiley & Sons.
Engle, R. F., & Lee, G. (1999).A long-run and short-run component model of stock return volatility. Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive WJ Granger, 475-497.
Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105.
Foster, A. J. (1995). Volume‐volatility relationships for crude oil futures markets. Journal of Futures Markets, 15(8), 929-951.
French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987).Expected stock returns and volatility. Journal of financial Economics, 19(1), 3-29.
Gomber, P., & Haferkorn, M. (2013).High-frequency-trading. Business & Information Systems Engineering, 5(2), 97-99.
Grammatikos, T., & Saunders, A. (1986). Futures price variability: A test of maturity and volume effects. Journal of Business, 319-330.
Guo, H., & Neely, C. J. (2008).Investigating the intertemporal risk–return relation in international stock markets with the component GARCH model. Economics letters, 99(2), 371-374.
Hasbrouck, J., & Saar, G. (2013).Low-latency trading. Journal of Financial Markets, 16(4), 646-679.
Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity?. The Journal of Finance, 66(1), 1-33.
Jacobsen, B., & Dannenburg, D. (2003). Volatility clustering in monthly stock returns. Journal of Empirical Finance, 10(4), 479-503.
Jain, P. C., & Joh, G. H. (1988).The dependence between hourly prices and trading volume. Journal of Financial and Quantitative Analysis, 23(3), 269-283.
Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 22(1), 109-126.
Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2014). The flash crash: The impact of high frequency trading on an electronic market.
Lamoureux, C. G., & Lastrapes, W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. The journal of finance, 45(1), 221-229.
McCarthy, J., & Najand, M. (1993). State space modeling of price and volume dependence: evidence from currency futures. Journal of Futures Markets, 13(4), 335-344.
Najand, M., & Yung, K. (1994). Conditional heteroskedasticity and the weekend effect in S&P 500 index futures. Journal of Business Finance & Accounting, 21(4), 603-612.
Nelson, D. B. (1990a). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
Park, T. H., Switzer, L. N., & Bedrossian, R. (1999). The interactions between trading volume and volatility: evidence from the equity options markets. Applied Financial Economics, 9(6), 627-637.
Schwert, G. W. (1990a). Stock market volatility. Financial analysts journal, 46(3), 23-34.
Schwert, G. W. (1990b). Stock volatility and the crash of’87. The Review of Financial Studies, 3(1), 77-102.
Securities and Exchange Commission.(2010). Concept release on equity market structure. Federal Register, 75(13), 3594-3614.
Wood, R. A., McInish, T. H., & Ord, J. K. (1985).An investigation of transactions data for NYSE stocks. The Journal of Finance, 40(3), 723-739.
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.