張玲
摘 要 在具有可觀測和不可觀測狀態的金融市場中,利用隱馬爾可夫鏈描述不可觀測狀態的動態過程,研究了不完全信息市場中的多階段最優投資組合選擇問題.通過構造充分統計量,不完全信息下的投資組合優化問題轉化為完全信息下的投資組合優化問題,利用動態規劃方法求得了最優投資組合策略和最優值函數的解析解.作為特例,還給出了市場狀態完全可觀測時的最優投資組合策略和最優值函數.
關鍵詞 不完全信息;隱馬爾可夫鏈;充分統計量;基準準則;動態規劃
中圖分類號 F830.59, F224.3 文獻標識碼 A
1 引 言
多階段最優投資組合選擇問題的研究中,通常假定風險資產收益獨立同分布且資產收益與市場狀態無關[1].然而大量實證研究卻發現,風險資產在各階段的收益序列是相關的,且資產收益的這種相關性通過市場參數得以實現.金融市場中,股票價格不能獨立于宏觀經濟之外,眾多股票價格的時間序列表現出較強的相關性和較大的跳躍,且這些跳躍通常同一些事件聯系相關,如牛市和熊市、金融危機、政府新的金融或經濟政策等.Hardy[2]發現Markov鏈能顯著地擬合這類金融市場狀態的變化過程,且股票收益具有較強的Markov機制轉移性質.此后,利用Markov鏈刻畫金融市場狀態變化過程成為經濟和金融領域的一個研究熱點.在資產組合選擇問題的研究方面,Zhou和Yin[3]研究了Markov機制轉移下的連續時間均值方差最優投資組合選擇問題.Cakmak和zekici[4]用時齊的Markov過程描述市場狀態的變化過程,得到了多階段最優投資問題的最優投資策略的解析解.在文獻[4]的基礎上,Wei和Ye[5]引入了破產風險控制,Wu和Li[6]進一步考慮了投資終止時間不確定對最優投資決策的影響.Costa和Araujo[7] 研究了參數受Markov機制轉移調制的多階段均值方差資產組合選擇問題,并將結果應用到了動態資產組合選擇問題的破產風險控制中.Xie[8] 研究了風險資產價格和負債都是受到Markov機制轉移調制的連續時間資產負債管理問題.在上述馬爾科夫機制轉移模型中,有一個基本的假設:Markov鏈的狀態是完全可觀測的,且狀態轉移矩陣是定常的.
然而,金融市場中不僅存在投資者可以觀測到的狀態信息(如利率、通貨膨脹率、匯率等),還存在投資者無法觀測到的市場狀態信息,正是這些不可觀測的金融市場狀態信息導致了資產收益的變化,陳國華等[9] 研究了資產收益率為模糊數的投資組合選擇問題.事實上,絕大多數投資者僅能依據從市場中觀測得到的信息而非市場上全部的信息做出投資決策,這導致了不完全信息下的投資決策問題,隱馬爾科夫鏈( hidden Markov chain)常用來刻畫不可觀測狀態的變化過程.Sass和Haussmann[10]、Rieder和Buerle[11]、Putschgl 和Sass等[12]考慮了不可觀測狀態由隱馬爾科夫鏈刻畫的連續時間最優投資決策問題,分析了不可觀測狀態對最優投資策略的影響.基于優化問題的可解性,以往有關不完全信息下最優投資問題的研究集中于連續時間情形,離散時間最優投資組合選擇問題的研究還很少.雖然Canakolu和zekici[13]考慮了隱Markov機制轉移市場中的多階段HARA效用最大化問題,但沒有得到最優策略的解析解.連續時間模型在求解最優投資組合選擇問題中具有非常好的便利性,然而離散時間多階段模型更符合金融市場實際決策,且連續時間投資組合選擇問題的實現依然需要借助于離散化的工具.所以,在具有不可觀測市場狀態的金融市場中,離散時間多階段最優資產組合選擇問題的研究具有重要的現實和理論意義.
基于以上研究和思考,本文利用離散時間有限狀態隱Markov鏈刻畫金融市場上不可觀測狀態的變化過程,建立了不完全信息下多階段最優投資組合選擇問題的基準準則模型.通過擴大狀態空間和構造充分統計量,不完全信息下的優化問題轉化為完全信息下的優化問題,利用動態規劃方法求得了最優投資組合策略和最優值函數的解析表達式.
5 總 結
本文在具有可觀測狀態和不可觀測狀態的金融市場中,利用隱Markov鏈模擬不可觀測市場狀態的變化過程,研究了不完全信息市場中的多階段最優資產組合選擇問題.通過構造充分統計量,不完全信息下的最優資產組合選擇問題轉變為完全信息下的最優資產組合選擇問題,采用動態規劃方法求解優化問題,得到了各階段最優資產組合策略和最優值函數的解析表達式.同時,給出了市場狀態完全可觀測時最優資產組合選擇問題的最優組合策略和最優值函數.本文中所建立的模型還未考慮金融市場存在的各種約束,以后的工作中可以進一步考慮不完全信息市場中存在各種摩擦時的最優資產組合選擇問題.
參考文獻
[1] D LI, W L NG. Optimal dynamic portfolio selection: multiperiod mean-variance formulation [J]. Mathematical Finance, 2000, 10(3):387-406.
[2] M R HARDY. A regime-switching model of long-term stock return [J]. North American Actuarial Journal, 2001, 5(2):41-53.
[3] X Y ZHOU, G YIN. Markowitz's mean-variance portfolio selection with regime switching: a continuous-time model [J]. SIAM Journal on Control and Optimization, 2003, 42(4):1466-1482.
[4] U CAKMAK, S ZEKICI. Portfolio optimization in stochastic markets [J]. Mathematical Methods of Operations Research, 2006, 63(1):151-168.
[5] S Z WEI, Z X YE. Multi-period optimization portfolio with bankruptcy control in stochastic market [J]. Applied Mathematics and Computation, 2007, 186(1):414-425.
[6] H L WU, Z F LI. Multi-period mean-variance portfolio selection with Markov regime switching and uncertain time horizon [J]. Journal of System Science and Complexity, 2011, 24(1):140-155.
[7] O L V COSTA, M V ARAUJO. A generalized multi-period mean-variance portfolio optimization with Markov switching parameters [J]. Automatica, 2008, 44(10):2487-2497.
[8] S X XIE. Continuous-time mean-variance portfolio selection with liability and regime switching [J]. Insurance: Mathematics and Economics, 2009, 45(1):148-155.
[9] 陳國華,陳收,房勇,汪壽陽. 基于模糊收益率的組合投資模型 [J]. 經濟數學,2006,23(1):19-25.
[10]J SASS, U G HAUSSMANN. Optimizing the terminal wealth under partial information: the drift process as a continuous-time Markov chain [J]. Finance and Stochastics, 2004, 8(4):553-577.
[11]U RIEDER, N BUERLE. Portfolio optimization with unobservable Markov-modulated drift process [J]. Journal of Applied Probability, 2005, 42(2):362-378.
[12]W PUTSCHGL, J SASS. Optimal investment under dynamic risk constraints and partial information [J]. Quantitative Finance, 2011, 11(10): 1547-1564.
[13]E CANAKLU, S ZEKICI. Portfolio selection with imperfect information: a hidden Markov model [J]. Applied Stochastic Models in Business and Industry, 2011, 27(2):95-114.
[14]D P BERTSEKAS. Dynamic programming: deterministic and stochastic models [M]. Prentice-Hall, Englewood Cliffs, NJ, 1987.
[15]G E MONAHAN. A survey of incompletely observable Markov decision processes: theory, models and algorithms [J]. Management Science, 1982, 28(1): 1-16.endprint
[4] U CAKMAK, S ZEKICI. Portfolio optimization in stochastic markets [J]. Mathematical Methods of Operations Research, 2006, 63(1):151-168.
[5] S Z WEI, Z X YE. Multi-period optimization portfolio with bankruptcy control in stochastic market [J]. Applied Mathematics and Computation, 2007, 186(1):414-425.
[6] H L WU, Z F LI. Multi-period mean-variance portfolio selection with Markov regime switching and uncertain time horizon [J]. Journal of System Science and Complexity, 2011, 24(1):140-155.
[7] O L V COSTA, M V ARAUJO. A generalized multi-period mean-variance portfolio optimization with Markov switching parameters [J]. Automatica, 2008, 44(10):2487-2497.
[8] S X XIE. Continuous-time mean-variance portfolio selection with liability and regime switching [J]. Insurance: Mathematics and Economics, 2009, 45(1):148-155.
[9] 陳國華,陳收,房勇,汪壽陽. 基于模糊收益率的組合投資模型 [J]. 經濟數學,2006,23(1):19-25.
[10]J SASS, U G HAUSSMANN. Optimizing the terminal wealth under partial information: the drift process as a continuous-time Markov chain [J]. Finance and Stochastics, 2004, 8(4):553-577.
[11]U RIEDER, N BUERLE. Portfolio optimization with unobservable Markov-modulated drift process [J]. Journal of Applied Probability, 2005, 42(2):362-378.
[12]W PUTSCHGL, J SASS. Optimal investment under dynamic risk constraints and partial information [J]. Quantitative Finance, 2011, 11(10): 1547-1564.
[13]E CANAKLU, S ZEKICI. Portfolio selection with imperfect information: a hidden Markov model [J]. Applied Stochastic Models in Business and Industry, 2011, 27(2):95-114.
[14]D P BERTSEKAS. Dynamic programming: deterministic and stochastic models [M]. Prentice-Hall, Englewood Cliffs, NJ, 1987.
[15]G E MONAHAN. A survey of incompletely observable Markov decision processes: theory, models and algorithms [J]. Management Science, 1982, 28(1): 1-16.endprint
[4] U CAKMAK, S ZEKICI. Portfolio optimization in stochastic markets [J]. Mathematical Methods of Operations Research, 2006, 63(1):151-168.
[5] S Z WEI, Z X YE. Multi-period optimization portfolio with bankruptcy control in stochastic market [J]. Applied Mathematics and Computation, 2007, 186(1):414-425.
[6] H L WU, Z F LI. Multi-period mean-variance portfolio selection with Markov regime switching and uncertain time horizon [J]. Journal of System Science and Complexity, 2011, 24(1):140-155.
[7] O L V COSTA, M V ARAUJO. A generalized multi-period mean-variance portfolio optimization with Markov switching parameters [J]. Automatica, 2008, 44(10):2487-2497.
[8] S X XIE. Continuous-time mean-variance portfolio selection with liability and regime switching [J]. Insurance: Mathematics and Economics, 2009, 45(1):148-155.
[9] 陳國華,陳收,房勇,汪壽陽. 基于模糊收益率的組合投資模型 [J]. 經濟數學,2006,23(1):19-25.
[10]J SASS, U G HAUSSMANN. Optimizing the terminal wealth under partial information: the drift process as a continuous-time Markov chain [J]. Finance and Stochastics, 2004, 8(4):553-577.
[11]U RIEDER, N BUERLE. Portfolio optimization with unobservable Markov-modulated drift process [J]. Journal of Applied Probability, 2005, 42(2):362-378.
[12]W PUTSCHGL, J SASS. Optimal investment under dynamic risk constraints and partial information [J]. Quantitative Finance, 2011, 11(10): 1547-1564.
[13]E CANAKLU, S ZEKICI. Portfolio selection with imperfect information: a hidden Markov model [J]. Applied Stochastic Models in Business and Industry, 2011, 27(2):95-114.
[14]D P BERTSEKAS. Dynamic programming: deterministic and stochastic models [M]. Prentice-Hall, Englewood Cliffs, NJ, 1987.
[15]G E MONAHAN. A survey of incompletely observable Markov decision processes: theory, models and algorithms [J]. Management Science, 1982, 28(1): 1-16.endprint