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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/67100


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/67100


    Title: 馬可夫狀態轉換下最低條件風險值集中度投資組合之建構
    On the Construction of Minimum CVaR Concentration Portfolios under Markovian Regime Shifts
    Authors: 賴韋志
    Contributors: 江彌修
    賴韋志
    Keywords: 最低條件風險值集中度投資組合
    等值風險權重投資組合
    馬可夫狀態 轉換模型
    Risk Parity Portfolios
    Minimum CVaR Concentration Portfolios
    Markov regime-switching model
    Date: 2013
    Issue Date: 2014-07-01 12:06:55 (UTC+8)
    Abstract: 本文主要研究的投資組合策略為Boudt et al. (2013)所提出的最低條件風險值集中度(Minimum CVaR Concentration，簡稱MCC)投資組合，並且延伸Kritzman et al. (2012)以及Wang et al. (2012)將馬可夫狀態轉換模型應用於資產配置的方法，在市場狀態為狀態一(熊市)之下，將MCC投資組合下方風險控制在3.00%之下，建構一狀態相關(regime-dependent)MCC投資組合。
    綜合本研究之實證結果，發現MCC投資組合在市場狀態為狀態一(熊市)之下，表現較狀態二(正常市場)差，主要原因為MCC投資組合在狀態一(熊市)時仍以達到均衡風險分散為主要目標，卻忽略了投資組合下方風險上升。而本研究所建構的狀態相關MCC投資組合，在熊市時的確能提升平均報酬率，而且降低平均報酬率的標準差、95%平均下方風險(CVaR)以及每月最大損失等風險。
    The main portfolio strategy exploited in this paper is the Minimum CVaR Concentration (MCC) introduced by Boudt et al. (2013). Our paper is closely related to recent literature on drawing inference of asset allocation strategy from Markov regime-switching model, for instance, Kritzman et al. (2012) and Wang et al. (2012). We built a regime-dependent MCC portfolio under a bearish market condition by fixing the downside risk at a maximum of 3.00%.
    From the empirical evidence, we conclude that the main reason MCC portfolio performs better under normal market condition (condition 2) than under bearish market condition (condition 1) is because under condition 1, MCC portfolio strives to achieve risk diversification and ignores the increase of downside risk. While the regime-dependent MCC we propose can effectively increase average return, and lower average standard deviation, CVaR (95%), and biggest monthly loss.
    Reference: 1. Ang, A., and Bekaert, G. (2002), “International Asset Allocation with Regime Shifts,” Review of Financial Studies, Vol. 15, pp.1137–1187.
    2. Ang, A., and Bekaert, G. (2004), “How Regimes Affect Asset Allocation,” Review of Financial Studies, Vol. 60, No. 2, pp. 86-99.
    3. Ang, A., and Chen, J. (2002), “Asymmetric Correlations of Equity Portfolios,” Journal of Financial Economics, Vol. 63, pp.443–494.
    4. Alankar, A., DePalma, M., and Scholes, M. (2012), “An Introduction to Tail Risk Parity,” ALLIANCE BERNSTEIN.
    5. Artzner, P., F. Delbaen, and J. Eber, D. Heath (1999), “Coherent Measure of Risk,” Mathematical Finance, Vol. 9, No. 3, pp.203-228.
    6. Boudt, K., Carl, P., and Peterson, B. G. (2013), “Asset Allocation with Conditional Value-at-Risk Budgets,” Journal of Risk Vol. 15, No. 3, pp.39-68.
    7. Butler, K. C., and Joaquin, D. C. (2002), “Are the Gains from International Portfolio Diversification Exaggerated? The Influence of Downside Risk in Bear Markets,” Journal of International Money and Finance, Vol. 21, pp. 981-1011.
    8. Boudt, K., Peterson, B.G., and Croux,C. (2008), “Estimation and Decomposition of Downside Risk for Portfolios with Non-Normal Returns,” The Journal of Risk Vol. 11, No. 2, pp.79–103.
    9. Denault, M. (2001), “Coherent Allocation of Risk Capital,” The Journal of Risk Vol. 4, No. 1, pp.1–33.
    10. Ellis, J. (2005), “Ahead of the Curve: A Commonsense Guide to Forecasting Business and Market Cycles,” Harvard Business Press, Cambridge, MA.
    11. Goldfeld, Stephen, M., and Richard, E. Quandt (1973), “A Markov Model for Switching Regressions,” Journal of Econometrics, Vol. 1, pp. 3-16.
    12. Hamilton, J. D. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, Vol. 57, pp. 357-384.
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    14. Hess, M. K. (2010), “Timing and Diversification: A State-Dependent Asset Allocation Approach” The European Journal of Finance, Vol. 12, No. 3, pp.189-204.
    15. Jorion, P. (1996), “Value at Risk: The New Benchmark for Controlling Derivatives Risk,” McGraw-Hill.
    16. Kritzman, M., and Li, Y. (2010), “Skulls, Financial Turbulence, and Risk Management,” Financial Analysts Journal, Vol. 66, No. 5, pp. 30-41.
    17. Kritzman, M., Page, S., and Turkington, D. (2012), “Regime Shifts: Implications for Dynamic Strategies,” Financial Analysts Journal, Vol. 68, No. 3, pp. 22-39.
    18. Markowitz, H. (1952), “Portfolio Selection,” Journal of Finance, Vol. 7, pp.77-91.
    19. Maillard, S., Roncalli, T., and Teiletche, J. (2010), “On the Properties of Equally-Weighted Risk Contributions Portfolios,” Journal of Portfolio Management Vol. 36, No. 4, pp.60–70.
    20. Pflug, G. Ch. (2000), “Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk,” In. Uryasev S. (Ed.) Probabilistic Constrained Optimization: Methodology and Applications, Kluwer Academic Publishers.
    21. Qian, E. (2005), “Risk Parity Portfolios: Efficient Portfolios Through True Diversification of Risk,” Report, Panagora Asset Management.
    22. Qian, E. (2006), “On the Financial Interpretation of Risk Contributions: Risk Budgets Do Add Up,” Journal of Investment Management, Fourth Quarter.
    23. Rockafellar, R. T. and S. Uryasev (2000), “Optimization of Conditional Value-at-Risk,” Journal of Risk, Vol. 2, pp.21-41.
    24. Wang, P., Sullivan, N. Rodney, and Ge, Y. (2012), “Risk-Based Dynamic Asset Allocation with Extreme Tails and Correlations,” The Journal of Portfolio Management, Vol. 38, No. 4, pp. 26-42.
    Description: 碩士
    國立政治大學
    金融研究所
    101352009
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101352009
    Data Type: thesis
    Appears in Collections:[金融學系] 學位論文

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