研究生: |
陳盈甫 Zing-Fu Chen |
---|---|
論文名稱: |
基於選擇權定價、效用函數、風險值預測和概似函數,比較多變量波動率模型 |
指導教授: |
徐南蓉
Nan-Jung Hsu |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
理學院 - 統計學研究所 Institute of Statistics |
論文出版年: | 2008 |
畢業學年度: | 96 |
語文別: | 中文 |
論文頁數: | 44 |
中文關鍵詞: | 波動率 、波動率模式 、損失函數 |
相關次數: | 點閱:3 下載:0 |
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金融市場可提供投資的管道日新月異,投資人可依照自己的喜好,選擇市場上不同的商品進行投資。投資人在選擇投資標的前,大多以觀察其標的資產過去資料的變化趨勢,以預測未來可能的變化走勢,進而估算能獲得的投資報酬。
對於未來資產價格的預測,資產波動率(volatility)扮演著重要的角色。資產波動率指的就是資產在一定時間內的變化程度,因此若能準確預測資產波動率,則可以正確評估投資風險,進而做出決策,得到較高的投資收益,因此波動率的研究在處理計量經濟的議題上有著極大的重要性。
有鑒於以往的文獻中,少有比較波動率模式之間的優劣,因此本文考慮不同的資產,以Gloria, Lee and Santosh (2004)所提出的四種單變量損失函數為基準,擴展到四種多資產的損失函數(預測選擇權價格的準確度、預測效用函數的高低、預測風險值的準確度和預測概似函數的高低),來評估多變量波動率模式在不同考量目標下的優劣;此外也探討在單一資產下,單變量波動率模式和多變量波動率模式在不同考量目標下之間的優劣,提供投資大眾在選擇模式上的一些具體建議。
Akgiray, V. (1989). Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts. Journal of Business, 62, 55-80.
Berg, A., Meyer, R. and Yu, J. (2004). Deviance information criterion for comparing stochastic volatility models. Journal of Business Econometrics Statistics, 22, 107-120.
Bollerslev, T. (1990). Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Review of Economics and Statistics, 72, 498-505.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327.
Bollerslev, T., Engle, R.-F. and Wooldridge, JM. (1988). A capital asset pricing model with time varying covariances. Journal of Political Economy, 96, 116-131.
Brooks, C. and Persand, G. (2003). Volatility forecasting for risk management. Journal of Forecasting, 22, 1-22.
Engle, R.-F. (1982). Autoregressive conditional heteroscedasticitywith estimates of the variance of UK inflation. Econometrica,50, 987–1008.
Engle, R.-F., Kane, A. and Noh, J. (1997). Index-option pricing with stochastic volatility and the value of accurate variance forecasts. Review of Derivatives Research, 1, 139-157.
Engle, R. and Kroner, F.-K. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11, 122-150.
Gloria, G.-R., Lee, T.-H. and Santosh, M. (2004). Forecasting volatility: a reality check based on option pricing, utility function, Value-at-Risk, and predictive likelihood. International Journal of Forecasting, 20, 629-645.
Glosten, L.-R., Jaganathan, R. and Runkle, D. (1993). On the relationship between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48,1779-1801.
Harvey A.-C., Ruiz E. and Shephard N. (1994). Multivariate stochastic variance models. Review of Economic Studies, 61, 247-264.
Koenker, R. and Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33-50.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 77-91.
Nelson, D.-B. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econometrica, 59, 347- 370.
Noh, J., Engle, R.-F. and Kane, A. (1994). Forecasting volatility and option prices of the S&P 500 index. Journal of Derivatives, 17-30.
Spiegelhalter, D., Best, N., Carlin, B. and van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal Royal Statistics Society series B, 583-639.
Taylor, S.-J. (1986). Modelling financial time series. New York : Wiley.
Von Neumann, J. and Morgenstern, O. ( 1944). Theory of Games and Economic Behavior. Princeton : Princeton University Press.
Wei, Y. and Wang, P. (2008). Forecasting volatility of SSEC in Chinese stock market usingmultifractal analysis. Physica a-statistical mechanics and applications, 387, 1585-1592.
West, K.-D., Edison, H.-J. and Cho, D. (1993). A utility based comparison of some models of exchange rate volatility. Journal of International Economics, 35, 23- 45.
Yumlu, S., Gurgen, F.-S. and Okay, N. (2005). A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction. Pattern recognition letters, 26, 2093-2103.
李榮冠 (2004),台股指數報酬率與波動性之關聯性-不同波動性估計模型的比較,國立中正大學財務金融研究所碩士論文。