Please use this identifier to cite or link to this item: `https://ah.nccu.edu.tw/handle/140.119/55004`

 Title: 以狀態轉換之Copula模型做動態資產配置Dynamic asset allocation with regime-switching Copula Authors: 孫博辰Sun, Po Cheng Contributors: 黃泓智王昭文Huang, Hong ChihWang, Chou Wen孫博辰Sun, Po Cheng Keywords: 資產配置多元cpula狀態轉換asset allocationmultivariate copularegime-switching Date: 2011 Issue Date: 2012-10-30 15:19:39 (UTC+8) Abstract: 在國際間的股票市場中，股票報酬常存在有不對稱的相關結構，而其會造成許多極度地尾端風險。Copula函數常被用來描述多變數之間的聯合相關程度。多數的文獻均以二元copula函數為架構，去描述多種不同資產，像是股票、債券、匯率等之間的關係。我們討論多元copula的應用，本文以四元copula為主軸，並輔以狀態轉換 (regime-switching) 之機率過程，建構出四資產的投資組合之相關結構模型。考慮了狀態轉換之copula的配適性後，我們以此模型來做資產投資策略。在模擬過程中，我們嘗試根據不同的未來目標做出最佳的投資組合權重，並採用動態預期模型 (dynamic anticipative model) 來藉由資訊的不斷更新，重新估計模型的參數來做資產評估。實證結果上，我們發現考慮狀態轉換之copula模型可以捕捉到更多股票報酬波動的情形，因此能減少在股市共跌時造成的重大損失。The correlation of returns in international stock markets exist asymmetric structure, which cause extremely tail dependence. The copula functions are commonly used to describe the dependence between random variables. Most literatures use basic pair-copulas to model the dependence of two variables, like stocks, bonds and exchange rates. This article try to use multivariate copulas, mainly 4-copula, and regime-switching method to construct a portfolio dependence, and extend to asset allocation.Given the fitting regime-switching copula, we use the model to decide investment strategy. We try to select the optimal weights of portfolio by different objective function, and we adapt a dynamic anticipative model, which can take all new information for parameters estimation. Empirically, we find that the copula-based model with regime-switching can capture more variation, and decrease the return loss from downside co-movement. Reference: 1. Ang, A. & Chen, J., 2002. Asymmetric correlations of equity portfolios, Journal of Financial Economics 63(3), 443-94.2. Blake, D., Cairns, A. J. G., Dowd, K., 2001. Pensionmetrics: stochastic pension plan design and value-at-risk during the accumulation phase. Insurance: Mathematics and Economics 29, 187-215.3. Blake, D., Cairns, A.J.G. & Dowd, K., 2003. Pensionmetrics II: Stochastic pension plan design during the distribution phase, Insurance: Mathematics and Economics 33, 29-47.4. O. Candido, F. A. Ziegelmann, J. Duekerc, 2012. Modelling the Dependence Dynamics through Copulas with Regime Switching, Insurance: Mathematics and Economics 50, 346-3565. U. Cherubini, E. Luciano, W. Vecchiato, 2004. Copula Methods in Finence, The Wiley Finance Series.6. U. Cherubini, S. Mulinacci, F. Gobbi, S Romagnoli, 2012. Dynamic Copula Methods in Finance, The Wiley Finance Series.7. L. Chollete, A. Heinen, and A. Valdesogo, 2009. Modeling international financial returns with a multivariate regime switching copula, Journal of Financial Econometrics 7, 437-480.8. Garcia R. & Tsafack G., 2011. Dependence structure and extreme comovements in international equity and bond markets, Journal of Banking & Finance Volume 35, 1954-1970. 9. Haberman, S., Vigna, E., 2002. Optimal investment strategy and risk measures in defined contribution pension schemes, Insurance: Mathematics and Economics 31, 35-69.10. Emms P., Haberman, S., 2007. Asymptotic and numerical analysis of the optimal investment strategy for an insurermInsurance: Mathematics and Economics 40, 113–134.11. Huang, H.C., 2010. Optimal Multi-Period Asset Allocation: Matching Assets to Liabilities in a Discrete Model, Journal of Risk and Insurance 77(2).12. Huang, H.C. & Lee, Y.T., 2010. Optimal Asset Allocation for a General Portfolio of Life Insurance Policies, Insurance: Mathematics and Economics 46, 271-280.13. Jangmin O., J. Lee, J. Lee, B. Zhang, 2006. Adaptive stock trading with dynamic asset allocation using reinforcement learning, Information Sciences 176, 2121–2147.14. E. Jondeau and M. Rockinger, 2006. The copula-garch model of conditional dependencies: An international stock market application, Journal of International Money and Finance 25, 827-853.15. E. Luciano & W. Schoutens, 2006. A multivariate jump-driven financial asset model, Quantitative Finance 6(5), 385-402.16. H. Manner & O. Reznikova, 2012. A Survey on Time-Varying Copulas: Specification, Simulations, and Application, Econometric Reviews 31(6), 654-68717. A. Patton, 2004. On the out-of-sample importance of skewness and asymmetric dependence for asset allocation, Journal of Financial Econometrics 2, 130-168.18. A. Patton, 2006. Modelling Asymmetric Exchange Rate Dependence, International Economic Review 47, 527-55619. A. Patton, 2009. Copula-based models for financial time series. In T. G. Andersen, R. A. Davis, J.P. Kreiss, and T. Mikosch, editors, Handbook of Financial Time Series. Springer Verlag, 2009.20. D. Pelletier, 2006. Regime switching for dynamic correlations, Journal of Econometrics 131, 445-473.21. Ribeiro, R. & P. VeronesiI, 2002. The Excess Comovement of International Stock Returns in Bad Times: A Rational Expectations Equilibrium Model, Working Paper.22. T. Okimoto, 2008. New Evidence of Asymmetric DependenceStructures in International Equity Markets, Journal of Financial and Quantitative Analysis 43, 787–81623. V. Zakamouline, S. Koekebakker, 2009. Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance, Journal of Banking & Finance 33, 1242–1254. Description: 碩士國立政治大學風險管理與保險研究所99358021100 Source URI: http://thesis.lib.nccu.edu.tw/record/#G0099358021 Data Type: thesis Appears in Collections: [風險管理與保險學系 ] 學位論文

Files in This Item:

File SizeFormat