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題名 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
作者 賴韋志
貢獻者 江彌修
賴韋志
關鍵詞 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
日期 2013
上傳時間 1-Jul-2014 12:06:55 (UTC+8)
摘要 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.
參考文獻 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.
13. Hamilton, J. D., and R. Susmel (1994), “Autoregressive Conditional Heteroskedasticity and Changes in Regime,” Journal of Econometrics, Vol. 64, pp. 307-333.
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.
描述 碩士
國立政治大學
金融研究所
101352009
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101352009
資料類型 thesis
dc.contributor.advisor 江彌修zh_TW
dc.contributor.author (Authors) 賴韋志zh_TW
dc.creator (作者) 賴韋志zh_TW
dc.date (日期) 2013en_US
dc.date.accessioned 1-Jul-2014 12:06:55 (UTC+8)-
dc.date.available 1-Jul-2014 12:06:55 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2014 12:06:55 (UTC+8)-
dc.identifier (Other Identifiers) G0101352009en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/67100-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融研究所zh_TW
dc.description (描述) 101352009zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 第壹章、研究背景與動機 1
第貳章、文獻回顧 4
一、資產配置 4
二、下方風險資產配置 5
三、投資組合風險分散 6
四、馬可夫狀態轉換模型於資產配置上的應用 7
第參章、研究方法 10
一、風險預算投資組合 10
(一)定義邊際以及總風險貢獻 10
(二)等值風險權重策略的理論特性 11
(三)等值風險權重策略數值解法 12
二、下方風險預算投資組合 14
(一)風險值 14
(二)定義投資組合下方風險與資產下方風險貢獻 15
(三)建構MCC投資組合 18
三、馬可夫狀態轉換模型 19
(一)馬氏距離(Mahalanobis distance) 19
(二)馬可夫狀態轉換模型參數校正 20
四、實證方法 22
第肆章、實證結果 24
一、資料描述 24
二、MCC投資組合實證結果分析 26
(一)不同市場之下的MCC投資組合表現 26
(二) MCC投資組合下方風險與資產下方風險貢獻集中度分析 28
三、馬可夫狀態轉換結果分析 29
(一)馬氏距離 29
(二)馬可夫狀態轉換判斷結果 30
四、投資組合結果比較與分析 32
(一)投資組合績效比較 32
(二)下方風險、權重以及資產下方風險貢獻集中度分析 36
(三)投資組合權重分配以及資產下方風險分配分析 38
(四)投資組合累積報酬率比較 41
參考文獻 45		zh_TW
dc.format.extent 612070 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101352009en_US
dc.subject (關鍵詞) 最低條件風險值集中度投資組合zh_TW
dc.subject (關鍵詞) 等值風險權重投資組合zh_TW
dc.subject (關鍵詞) 馬可夫狀態 轉換模型zh_TW
dc.subject (關鍵詞) Risk Parity Portfoliosen_US
dc.subject (關鍵詞) Minimum CVaR Concentration Portfoliosen_US
dc.subject (關鍵詞) Markov regime-switching modelen_US
dc.title (題名) 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zh_TW
dc.title (題名) On the Construction of Minimum CVaR Concentration Portfolios under Markovian Regime Shiftsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 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.
13. Hamilton, J. D., and R. Susmel (1994), “Autoregressive Conditional Heteroskedasticity and Changes in Regime,” Journal of Econometrics, Vol. 64, pp. 307-333.
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.		zh_TW