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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/106398


    Title: 店頭市場匯率類衍生性商品集中結算之保證金模型研究
    Research on central clearing margin methodologies of OTC foreign exchange derivatives
    Authors: 奚亞楠
    Contributors: 林士貴
    奚亞楠
    Keywords: 保證金
    外匯遠期
    歷史模擬法
    指數加權移動平均
    一般化自我迴歸條件異質變異模型
    Margin
    Foreign Exchange Forwards
    Historical Simulation
    EWMA
    GARCH
    Date: 2017
    Issue Date: 2017-02-08 16:34:56 (UTC+8)
    Abstract: 2007年次貸危機後，店頭市場衍生性商品的標準化與集中結算成為各國主管機關關注的焦點。各國政府逐漸推動了金融改革方案，開始將店頭市場衍生性商品標準化並採取集中結算，並逐步擴大集中結算涵蓋的店頭商品種類，以有效減少市場上的曝險部位，於降低系統性風險。由於匯率的變動決定了外匯相關金融商品的價格變化，也同時連帶影響到企業盈與投資活動的獲利，因此匯率衍生性商品市場的活絡與風險管理更顯重要。本研究介紹了CME現行的計算店頭市場外匯類衍生性金融商品保證金的半參數模型，並從提高市場波動度預測能力入手，引入了自估參數的EWMA、GARCH與GJR-GARCH等波動度模型，進一步發展了該半參數模型。實證部分，本研究以歐元兌美元 (EUR/USD) 匯率遠期為例，對上述模型進行回溯測試。實證結果顯示，通過波動度調整歷史報酬率的半參數模型能夠改善一般歷史模擬法的缺陷，使保證金能夠跟隨市場波動而做出調整。而諸多半參數模型中，由GJR-GARCH預測波動度的歷史模擬法是表現最佳的模型，不僅能夠有效預估保證金水準，使得穿透率達到模型設定的要求，而且不論在買賣雙方還是在不同信心水準下都具備穩定性。
    After the subprime mortgage crisis in 2007, standardization and centralized settlement of OTC derivatives became the focus of attention of various national authorities. Governments began to promote such financial reform programs, getting OTC derivatives being stadarsized and centrally cleared, and gradually expand the scope of centralized settlement of OTC derivatives. The reform aims to effectively reduce the exposure positions on the market and then reduce systemic risk. Since the exchange rate changes determine the price changes of foreign exchange-related financial products, and also affect the profitability of corporate earnings and investment activities, risk management of the foreign exchange rate derivatives market is more important. This paper introduces the semi-parametric model for calculating CME`s current margin of OTC foreign exchange derivatives, and further developes the model by introducing different volatility models such as EWMA, GARCH and GJR-GARCH to improve the ability of volatility forecasting. In the empirical part, this paper takes EUR/USD exchange rate forwards for example to test back the candidate models. The empirical results show that the semi-parametric models can improve the defects of the simple historical simulation method, adjusting the margin to follow the market fluctuation. As for the semi-parametric models, the semi-parametric GJR-GARCH historical simulation model has the best performance. It can effectively predict the margin level, making the penetration rate to set the model requirements, and has the best stability as well no matter for the buyers or sellers or in different levels of confidence.
    Reference: Agarwal, V. and N. Naik., 2004. Risks and portfolio decisions involving hedge funds. Review of Financial Studies, 17(1), 63-98.
    Alexander, G. and A. Baptista., 2004. A comparison of VaR and CVaR constraints on portfolio selection with the mean-variance model. Management Science, 50(9), 1261-1273.
    Balkema, A. A., and L. de Haan, 1974. Residual life time at great age. The Annals of Probability, 2(5), 792-804.
    Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 307-327.
    Bollerslev, T., 1987. A conditionally heteroskedastic time series model for speculative prices and rates of return. Review of Economics & Statistics, 69(3): 542-47.
    Brooks, C., A. D. Clare, J. W. Dalle Molle, and G. Persand, 2005. A comparison of extreme value theory approaches for determining value at risk. Journal of Empirical Finance, 12, 339-352.
    Booth, Geoffrey, John, Broussard, Teppo, Martikainen, Vesa, and Puttonen, 1997. Predent Margin Levels in the Finnish Stock Index Futures Market. Management Science, 43(8): 1177-1188.
    Chen, H., 2002. Price Limits and Margin Requirements in Futures Markets. The Financial Review, 37: 105-121.
    Cotter, J., and K. Dowd, 2006. Extreme Special Risk Measures: An Application to Futures Clearinghouse Margin Requirements. Journal of Banking & Finance, 30: 3469-3485.
    Craine, R., 1992. Are Futures Margins Adequate? University of California Berkeley Working Paper, No. 92-192, April.
    Cotter, J., 2001. Margin exceedances for European stock index futures using extreme value theory. Journal of Banking & Finance, 25, 1475-1502.
    Da Silva, A., A. Beatriz, B. de Melo Mendes, 2003. Value-at-Risk and extreme returns in Asian stock markets. International Journal of Business, 8 (1), 17-40.
    De Haan, L. and S. I. Resnick, 1980. A simple asymptotic estimate for the index of a stable distribution. Journal of the Royal Statistical Society, 42(1), pp. 83-87.
    Efron, B., 1979. Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7, 1-26.
    Engle, R. F., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Economatrica, 50(4), 987-1008
    Favre, L. and J. Galeano., 2002. Mean-modified value-at-risk optimization with hedge funds. Journal of Alternative Investments, 5(2), 21-25.
    Fernandez, V., 2005. Risk management under extreme events. International Review of Financial Analysis, 14, 113-148.
    Fisher, R. and L. Goldberg, 1986. The effect of margins on trading in futures Markets. Journal of Futures Markets, 6(2), 261-271.
    Gay, G. D., W. C. Hunter, and R. W. Kolb, 1986. A Comparative Analysis of Futures Contract Margins. Journal of Futures Markets, 6: 307-324.
    Gencay, R., and F. Selcuk, 2004. Extreme value theory and Value-at-Risk: Relative performance in emerging markets. International Journal of Forecasting, 20, 287-303.
    Glosten, L., R. Jagannathan, and D. Runkle, 1993. On the relation between the expected value and the volatility on the nominal excess 95 returns on stocks. Journal of Finance, 48, 1779-1801.
    Hardouvelis, G. A., 1988. Margin requirements, volatility, and the transitory component of stock prices. Federal Reserve Bank of New York, Research Paper 8818.
    Hill, B. M., 1975. A simple general approach to Inference about the tail of a distribution. Annals of Statistics, 3, 1163-1174.
    Huisman, R., K. G. Koedijk, and R.A.J. Pownall, 1998, VaR-x: Fat tails in financial risk management? Journal of Risk, 1(1), 47-61.
    Huisman, R., K. G. Koedijk, C. J.M. Kool, and F. Palm, 2001. Tail-Index extimates in small samples. Journal of Business & Economic Statistics, 19(1), 208-216.
    Hull, J. and A. White, 1998. Incorporating volatility updating into the historical simulation method for value at risk. Journal of Risk, 5-19.
    Jorion, P., 2001. Value at Risk. Second edition. McGraw Hill, New York.
    Kladívko, K., 2010. The czech treasury yield curve from 1999 to the present. Czech Journal of Economics and Finance, 60(4): 307-335.
    Kupiec, P. H., 1995. Techniques for verifying the accuracy of risk measurement models. Social Science Electronic Publishing, 3(2): 73-84.
    Linsmeier, T. J., and N. D. Pearson, 1996. Risk measurement: an introduction to value at risk. Finance.
    Lam, K., C. Y. Sin, R. Leung,2004. A Theoretical Framework to Evaluate Different Margin-Setting Methodologies. Journal of Futures Markets, 24: 117-145.
    Li, D., 1999. Value at risk based on the volatility, skewness and kurtosis. Working paper, Riskmetrics Group, 1999.
    McNeil, A. and R. Frey, 2000. Estimation of tail-related risk measures for heteroscedastic financial times series: An extreme value approach. Journal of Empirical Finance, 7, 271-300.
    Nelson, D. B., 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370.
    Nieppola, O., 2009. Backtesting Value-at-Risk models. Master’s thesis in Economics, Helsinki School of Economics.
    Pickands, J., 1975. Statistical inference using extreme order statistics. Annals of Statistics, 3, 119-131.
    Rockafellar, R. T. and S. Uryasev., 2000. Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21-41.
    RiskMetrics Group, 1996. RiskMetrics-Technical Document, New York: J.P.Morgan/Reuters
    Svensson, L. E. O., 1994. Estimating and interpreting forward interest rates: Sweden 1992-1994, NBER Working Paper Series, 4871.
    Zangari, P., 1994. Estimating volatilities and correlations, RiskMetrics-Technical document, 2nd ed., New York: Morgan Guaranty, 43-66.
    Zivot, E., and J. Wang, 2003. Modeling financial times series with S-Plus. Seattle, WA 98109-3044: Insightful Corporation (Global Headquarters)
    戴光懿,畢鵬,任潔, 2014. 中美期貨保證金制度差異的探討. 期貨日報, (3).
    鮑建平, 2004. 國內外期貨市場保證金制度比較研究及其啟示. 世界經濟, 12: 65-69.
    鮑建平, 王乃生, 吳衝鋒, 2005. 上海期銅保證金水準設計的實證研究. 系統工程理論方法應用, 14(1): 33-36.
    蔣賢鋒, 史永東, 李慕春, 2007. 期貨市場保證金調整的市場風險控制作用及制度改革-來自大連商品交易所的實證分析. 金融研究, (2): 74-88.
    廖宗武, 2006. 期貨動態保證金結算制度研究. 碩士學位論文, 西南財經.
    王文龍, 程希駿, 2011. 基於普風險測度的期指保證金水準設定. 中國科學技術大學學報, 41(12): 1042-1046.
    劉德明, 戴良安, 2007. 股票衍生性商品組合保證金系統之建構與比較. 中山管理評論, 15: 780-817.
    黃瑋苓, 劉德明, 2013. 組合式保證金系統之改良與比較:以TAIFEX交易資料實證. 中山管理評論, 21: 11-53.
    Description: 碩士
    國立政治大學
    金融學系
    104352038
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104352038
    Data Type: thesis
    Appears in Collections:[Department of Money and Banking] Theses

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