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    題名: 以實現波動率估計投資組合風險值
    Value at Risk of Portfolio with Realized Volatility
    作者: 李承儒
    貢獻者: 林信助
    李承儒
    關鍵詞: 風險值
    多變量GARCH模型
    實現波動率
    value at risk
    multivariate garch
    realized volatility
    日期: 2005
    上傳時間: 2009-09-11 17:06:47 (UTC+8)
    摘要: 利用風險值作為投資組合的風險管理工具，必須考慮金融資產報酬率通常具有厚尾、高峰、波動叢聚以及資產間訊息與波動性的變化也會交互影響等現象；因此實證上通常以多變量GARCH模型作為估計投資組合變異數矩陣的方法。然而多變量GARCH模型卻存在有維度上的詛咒，當投資組合包含資產數增加時會加重參數估計上的困難度。另一種估計波動率的方法，稱為實現波動率，能比多變量GARCH模型更簡易地處理投資組合高維度的問題。本文即以實現波動率、BEKK多變量GARCH模型與CCC模型，並以中鋼、台積電、國泰金為研究對象，比較三種方法估計風險值的表現。而實證結果得到利用實現波動率確實適合應用在風險值的估計上，且在表現上有略勝一籌的現象。
    參考文獻: 中文部分
    洪幸資，2003，「控制風險值下的最適投資組合」，國立政治大學金融研究所，碩士論文。
    高櫻芬、謝家和，2002。「涉險值之衡量—多變量GARCH模型之應用」，經濟論文叢刊，30，273-312。
    許傑翔，2004，「多變量財務時間數列模型之風險值計算」，東吳大學商用數學所，碩士論文。
    翁偉哲，2004，「風險值偏誤之衡量：以台灣期貨交易所之股價期貨為例」，國立高雄第一科技大學金融營運所，碩士論文。
    英文部分
    Alexander, C.O., and C. T. Leight, (1997). “On the Covariance Metrics Used in Value at Risk Models,” Journal of Derivatives, 4, 50-62.
    Andersen, T., T. Bollerslev, F. X. Diebold, and H. Ebens, (2002). “The Distribution of Realized Stock Return Volatility,” Journal of Financial Economics, 61, 43-76.
    Andersen, T., T. Bollerslev, F. X. Diebold, and P. Labys, (2000). “Exchange Rate Return Standardized by Realized Volatility Are (Nearly) Gaussian,” Multinational Finance Journal, 4, 159-179.
    Andersen, T., T. Bollerslev, F. X. Diebold, and P. Labys, (2001). “The Distribution of Realized Exchange Rate Volatility,” Journal of the American Statistical Association, 96, 42-55.
    Andersen, T., T. Bollerslev, F. X. Diebold, and P. Labys, (2003). “Modeling and Forecasting Realized Volatility,” Econometrica, 71, 579-626.
    Baba, Y., R. F. Engle, D. F. Kraft and K. Kroner, (1989). “Multivariate simultaneous generalized ARCH,” manuscript.
    Bollerslev, T., (1986). “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, 31, 307-327.
    Boudoukh, J., M. Richardson, and R. Whitelow, (1998). “The Best of Both Worlds,” Risk, 11, 64-67.
    Christoffersen, P., (1998). “Evaluating interval forecasts,” International Economic Review, 39, 841-862.
    Engle, R.F., (1982). “Autoregressive conditional heteroskedasticity with estimates of variance of the united kingdom inflation,” Econometrica, 50, 987-1001.
    Engle, R.F., and K. Kroner, (1995). “Multivariate simultaneous generalized ARCH,” Econometrics Theory, 11, 122-150.
    Hau, H., (2002). “The Role of Transaction Costs for Financial Volatility: Evidence from the Paris Bourse,” working paper.
    Hoppe, R., (1998). “VaR and the unreal world,” Risk, 11, 45-50.
    Jorion, P., (2000). Value at Risk—The New Benchmark for Controlling Market Risk, McGraw-Hill, New York.
    Giot, P., (2005). “Implied Volatility Indexes and Daily Value at Risk Models,” Journal of Derivatives , 12, 54-64.
    Giot, P., and S. Laurent, (2004). “Modelling daily Value-at-Risk using realized volatility and ARCH type models,” Journal of Empirical Finance, 11, 379-398.
    Koopman, S. J., B. Jungbacker, and E. Hol, (2005). “Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility measurements,” Journal of Empirical Finance, 12, 445-475.
    Kupiec, P.H., (1995). “Techniques for Verifying the Accuracy of Risk Measurement Models,” Journal of Derivatives, 3, 73-84.
    Palandri, A., (2005). “Sequential Conditional Correlations: Inference and Evaluation,” working paper.
    Taylor, S. J., and X. Xu, (1997). “The Incremental Volatility Information in One Million Foreign Exchange Quotations,” Journal of Empirical Finance, 4, 317-340.
    描述: 碩士
    國立政治大學
    國際經營與貿易研究所
    93351037
    94
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0093351037
    資料類型: thesis
    顯示於類別:[國際經營與貿易學系 ] 學位論文

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