Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/23299
標題: 時間序列方法在風險管理的實證應用
Empirical Applications of Time Series Analysis on Risk Management
作者: 黃婉君
Huang, Wan-Chun
關鍵字: RBC;時間序列模型;ARMA-GARCH;VaR;Back-testing;風險值;回溯測試
出版社: 財務金融系所
引用: Alexander, C. (1996), “Volatility and correlation forecasting.” The handbook of risk management and analysis, John Wiley and Sons, 233-259 Berndt, E. K., B. H. Hall, R. E. Hall and J. A. Hausman (1974), “Estimation and inference in nonlinear structural models” Annals of Economic and Social Measurement 4, 653-665 Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, Vol.31, pp.307-327 Boothe, P. and P. D. Glassman (1987), “The statistical distribution of exchange rates” Journal of International Economics 22, 297-319 Boudokh, J., M. Richardson and R. Whitelaw, (1998) “The best of both worlds: A hybrid approach to calculating value at risk.” Risk, Vol. 11, No. 5, 64-67 Dowd, K. (1998), “Beyond value at risk: The new science of risk management.” New York: John Wiley and Sons Engle, R.F. (1982), “Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation.” Econometrica 50, 987-1007 Engle, R.F., C. H. Hong, A. Kane, and J. Noh (1993), “Arbitrage valuation of variance forecasts with simulated options.” Advances in Futures and Options Research, Vol. 6, 393-415 Ghose, D. and K. F. Kroner (1995), “The relationship between GARCH and symmetric stable distributions: Finding the source of fat tails in the financial data.” Journal of Empirical Finance 2, 225-251 Greene W. H. (2003), “Econometric analysis”, Fifth Edition, Prentice Hall International Hendricks, D. (1996), “Evaluation of value-at-risk models using historical data.” Federal Reserve Bank of New York Economic Policy Review 2 (April), 39-70 Jorion, P. (1997), “Value-at-Risk: The new benchmark for controlling market risk.” Chicago: Irwin. Publishing J.P. Morgan (1996) “RiskMetrics Technical Document”, Fourth Edition Kenneth D. W., H. J. Edison, D. Cho (1993), “A utility based comparison of some models of exchange rate volatility.” Journal of International Economics, vol. 35, 23-46 Levich, R. M. (1985), “Empirical studies of exchange rates: Price behavior, rate determination and market efficiency.” Handbook of Economics 6, 287-302 Loretan, M. and P. C. B. Phillips (1994), “Testing the covariance stationary of heavy-tailed time series.” Journal of Empirical Finance 1, 211-248 Mandelbrot, B. (1963a), “New methods in statistical economics.” Journal of Political Economy 71, 421-440 Mandelbrot, B. (1963b), “The variation of certain speculative prices.” Journal of Business 36, 394-419 McNeil, A. J. (1999), “Extreme value theory for risk managers.” Internal Modeling and CAD II, Risk Books, 93-113 Müller, U., M. Dacorogna, and O. V. Pictet (1996), “Heavy tails in high frequency financial data” Discussion Paper, Olsen and Associates, Zurich, Switzerland Tsay, R. S. (2001), “Analysis of financial time series.” New York, Wiley
摘要: 
由於我國於九十二年七月開始針對保險業者實施風險基礎資本制度(Risk Based Capital,簡稱RBC 制度),並於今年三月將開始對外公佈各保險公司的RBC值,且規定保險業申請提高國外投資額度除重大處分情事之外,還必須計算市場風險值並評估其風險並每週至少控管一次,因此風險值模型的建置優劣亦將會影響到申請國外投資額度的能否提升。
在計算風險值的過程中,常會遇到樣本大小不足的問題,我們利用時間序列中常用的ARMA-GARCH模型,來進行缺值的預測,以補足該資產價格資料不足的部分。當資料收集齊全後,便可進行風險值的計算,並進行回溯測試,以篩選適合的模型來作為計算風險值的方法。
本文中嘗試以簡單移動平均法、指數加權移動平均法以及GARCH-t模型等三種方法來進行市場風險值的計算,由於簡單移動平均法的回溯值相當平穩,因此對於市場上的價格波動沒有太大的捕捉效果;指數加權移動平均法與GARCH-t模型雖然都能有效的反應價格波動度的改變,且兩方法的回溯穿透率也差異不大,故保險公司可視其內部的需求來決定選擇何種模型。
雖然指數加權移動平均法與GARCH-t模型的回測值變動幅度較大,於實務上無法完全因應風險的變動而改變每日資本額的提列大小,但反言之,可藉由調整投資組合的部位以降低市場風險值,進而符合目前資本額的狀況。因此當市場上出現劇烈的變動時,此模型便可提醒公司注意所投資的部位,並進行調整以規避風險,方能避免公司遭遇到鉅額的損害。
URI: http://hdl.handle.net/11455/23299
其他識別: U0005-0502200811132700
Appears in Collections:財務金融學系所

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