English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113318/144297 (79%)
Visitors : 51079404      Online Users : 927
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/81119
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/81119


    Title: S&P500波動度的預測 - 考慮狀態轉換與指數風險中立偏態及VIX期貨之資訊內涵
    The Information Content of S&P 500 Risk-neutral Skewness and VIX Futures for S&P 500 Volatility Forecasting:Markov Switching Approach
    Authors: 黃郁傑
    Huang, Yu Jie
    Contributors: 陳威光
    林靖庭

    Chen, Wei Kuang
    Lin, Ching Ting

    黃郁傑
    Huang, Yu Jie
    Keywords: 波動度預測
    實現波動度
    風險中立偏態
    VIX期貨
    馬可夫狀態轉換模型
    Volatility forecasting
    Realized volatility
    Risk-neutral skewness
    VIX futures
    Markov regime-switching
    Date: 2016
    Issue Date: 2016-02-03 11:18:02 (UTC+8)
    Abstract: 本研究探討VIX 期貨價格所隱含的資訊對於S&P 500 指數波動度預測的解釋力。過去許多文獻主要運用線性預測模型探討歷史波動度、隱含波動度和風險中立偏態對於波動度預測的資訊內涵。然而過去研究顯示,波動度具有長期記憶與非線性的特性,因此本文主要研究非線性預測模型對於波動度預測的有效性。本篇論文特別著重在不同市場狀態下(高波動與低波動)的實現波動度及隱含波動度異質自我迴歸模型(HAR-RV-IV model)。因此,本研究以考慮馬可夫狀態轉化下的異質自我迴歸模型(MRS-HAR model)進行實證分析。
    本研究主要目的有以下三點: (1) 以VIX期貨價格所隱含的資訊提升S&P 500波動度預測的準確性。(2) 結合風險中立偏態與VIX期貨的資訊內涵,進一步提升S&P 500 波動度預測的準確性。(3) 考慮狀態轉換後的波動度預測模型是否優於過去文獻的線性迴歸模型。
    本研究實證結果發現: (1) 相對於過去的實現波動度及隱含波動度,VIX 期貨可以提供對於預測未來波動度的額外資訊。 (2) 與其他模型比較,加入風險中立偏態和VIX 期貨萃取出的隱含波動度之波動度預測模型,只顯著提高預測未來一天波動度的準確性。 (3) 考慮狀態轉換後的波動度預測模型優於線性迴歸模型。
    This paper explores whether the information implied from VIX futures prices has incremental explanatory power for future volatility in the S&P 500 index. Most of prior studies adopt linear forecasting models to investigate the usefulness of historical volatility, implied volatility and risk-neutral skewness for volatility forecasting. However, previous literatures find out the long-memory and nonlinear property in volatility. Therefore, this study focuses on the nonlinear forecasting models to examine the effectiveness for volatility forecasting. In particular, we concentrate on Heterogeneous Autoregressive model of Realized Volatility and Implied Volatility (HAR-RV-IV) under different market conditions (i.e., high and low volatility state).
    This study has three main goals: First, to investigate whether the information extracted from VIX futures prices could improve the accuracy for future volatility forecasting. Second, combining the information content of risk-neutral skewness and VIX futures to enhance the predictive power for future volatility forecasting. Last, to explore whether the nonlinear models are superior to the linear models.
    This study finds that VIX futures prices contain additional information for future volatility, relative to past realized volatilities and implied volatility. Out-of-sample analysis confirms that VIX futures improves significantly the accuracy for future volatility forecasting. However, the improvement in the accuracy of volatility forecasts is significant only at daily forecast horizon after incorporating the information of risk-neutral skewness and VIX futures prices into the volatility forecasting model. Last, the volatility forecasting models are superior after taking the regime-switching into account.
    Reference: Amisano, G., Giacomini, R., 2007. Comparing density forecasts via weighted likelihood ratio tests. Journal of Business and Economic Statistics 25, 177–190.

    Bakshi, G., Kapadia, N., Madan, D., 2003. Stock return characteristic, skew laws, and the differential pricing of individual equity options. Review of Financial Studies 16, 101–143.

    Bollerslev, T., Engle, R.F., Nelson, D.B., 1994. ARCH models, Handbook of Econometrics 4. Edited by R. F. Engle and D. L. McFadden, Elsevier, Amsterdam, 2959–3038.

    Boot, T., Pick, A., 2014. Optimal forecasts from Markov switching models, Working paper. DeNederlandscheBank.

    Busch, T., Christensen, B.J., Nielsen, M.Ø., 2011. The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics 160, 48–57.

    Byun, S.J., Kim, J.S., 2013. The information content of risk-neutral skewness for volatility forecasting. Journal of Empirical Finance 23, 142–161.

    CBOE, 2011. SKEW: The CBOE SKEW Index. White Paper. Chicago Board Options Exchange. (Available at www.cboe.com/SKEW.)

    Chung, S.-L., Tsai, W.-C., Wang, Y.-H., and Weng, P.-S., 2011. The information content of the S&P 500 index and VIX options on the dynamics of the S&P 500 index. Journal of Futures Markets 31, 1170–1201.

    Corsi, F., 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Economics 7, 174–196.

    Dacco, R., Satchell, S., 1999. Why Do Regime-Switching Models Forecast so Badly? Journal of Forecasting 18, 1–16.

    Dennis, P., Mayhew, S., 2002. Risk-neutral skewness: evidence from stock options. Journal of Financial and Quantitative Analysis 37, 471–493.

    Diebold, F.X., Gunther, T.A., and Tay, A.S., 1998. Evaluating Density Forecasts with Applications to Financial Risk Management. International Economic Review 39, 863–883.

    Diebold, F.X., Mariano, R.S., 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13, 134–144.

    Doran, J.S., Peterson, D.R., Tarrant, B.C., 2007. Is there information in the volatility skew? Journal of Futures Markets 27, 921–959.

    Fleming, J., 1998. The quality of market volatility forecasts implied by S&P 100 index option prices. Journal of Empirical Finance 5, 317–345.

    Frijns, B., Tourani-Rad, A., Webb, R., 2013. On the Intraday Relation between the VIX and its Futures. Working paper. Auckland University of Technology.

    Goldman, E., Nam, J., Tsurumi, H., and Wang, J., 2013. Regimes and long memory in realized volatility. Studies in nonlinear dynamics and econometrics 17, 521–549.

    Granger, C., Ding, Z., 1996. Varieties of long memory models. Journal of Econometrics 73, 61–77.

    Gray, S., 1996. Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process. Journal of Financial Economics 42, 27–62.

    Hamilton, J.D., 1989. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica 57, 357–384.
    ———— 1993. Estimation, Inference, and Forecasting of Time Series Subject to Changes in Regime. Handbook of Statistics 11. Edited by G.S. Maddala, C.R. Rao, and H.D. Vinod, North-Holland, 231–260.
    ———— 1994. Time Series Analysis. Princeton, Princeton University Press.

    Jiang, G.J., Tian, Y.S., 2005. The model-free implied volatility and its information content. Review of Financial Studies 18, 1305–1342.

    Kim, C.J., Nelson, C., 1999. State Space Models With Regime Switching. Cambridge, MA: MIT Press.

    Konstantinidi, E., Skiadopoulos, G., 2011. Are VIX futures prices predictable? An empirical investigation. International Journal of Forecasting 27, 543–560.

    Latane, H.A., Rendleman, R.J., 1976. Standard deviations of stock price ratios implied in option prices. The Journal of Finance 31, 369–381.

    Longin, F., 1997. The threshold effect in expected volatility: a model based on asymmetric information. Review of Financial Studies 10, 837–869.

    Marcucci, J., 2005. Forecasting Stock Market Volatility with Regime-Switching GARCH Models. Studies in Nonlinear Dynamics and Econometrics 9, Article 6.

    Newey, W.K., West, K.D., 1987. A Simple, Positive Semidefinite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica 55, 703–708.

    Nossman, M., Wilhelmsson, A., 2009. Is the VIX futures market able to predict the VIX Index? A test of the expectations hypothesis. Journal of Alternative Investments 12, 54–67.

    Poon, S., Granger, C.W.J., 2003. Forecasting financial market volatility: A review. Journal of Economic Literature 41, 478–539.

    Raggi, D., Bordignon, S., 2012. Long memory and nonlinearities in realized volatilities: A Markov switching approach. Computational Statistics and Data Analysis 56, 3730–3742.

    Seo, S.W., Kim, J.K., 2015. The information content of option-implied information for volatility forecasting with investor sentiment. Journal of Banking and Finance 50, 106–120.

    Shu, J., Zhang, J., 2012. Causality in the VIX futures markets. Journal of Futures Markets 32, 24–46.

    Szado, E., 2009. VIX futures and options: A case study of portfolio diversification. Journal of Alternative Investments Fall, 68–85.
    Description: 碩士
    國立政治大學
    金融研究所
    102352034
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0102352034
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

    Files in This Item:

    File SizeFormat
    203401.pdf763KbAdobe PDF2609View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback