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      基于隱Markov模型的最優(yōu)資產(chǎn)組合選擇

      2014-08-12 23:09張玲
      經(jīng)濟(jì)數(shù)學(xué) 2014年2期
      關(guān)鍵詞:動(dòng)態(tài)規(guī)劃

      張玲

      摘 要 在具有可觀測(cè)和不可觀測(cè)狀態(tài)的金融市場(chǎng)中,利用隱馬爾可夫鏈描述不可觀測(cè)狀態(tài)的動(dòng)態(tài)過(guò)程,研究了不完全信息市場(chǎng)中的多階段最優(yōu)投資組合選擇問(wèn)題.通過(guò)構(gòu)造充分統(tǒng)計(jì)量,不完全信息下的投資組合優(yōu)化問(wèn)題轉(zhuǎn)化為完全信息下的投資組合優(yōu)化問(wèn)題,利用動(dòng)態(tài)規(guī)劃方法求得了最優(yōu)投資組合策略和最優(yōu)值函數(shù)的解析解.作為特例,還給出了市場(chǎng)狀態(tài)完全可觀測(cè)時(shí)的最優(yōu)投資組合策略和最優(yōu)值函數(shù).

      關(guān)鍵詞 不完全信息;隱馬爾可夫鏈;充分統(tǒng)計(jì)量;基準(zhǔn)準(zhǔn)則;動(dòng)態(tài)規(guī)劃

      中圖分類(lèi)號(hào) F830.59, F224.3 文獻(xiàn)標(biāo)識(shí)碼 A

      1 引 言

      多階段最優(yōu)投資組合選擇問(wèn)題的研究中,通常假定風(fēng)險(xiǎn)資產(chǎn)收益獨(dú)立同分布且資產(chǎn)收益與市場(chǎng)狀態(tài)無(wú)關(guān)[1].然而大量實(shí)證研究卻發(fā)現(xiàn),風(fēng)險(xiǎn)資產(chǎn)在各階段的收益序列是相關(guān)的,且資產(chǎn)收益的這種相關(guān)性通過(guò)市場(chǎng)參數(shù)得以實(shí)現(xiàn).金融市場(chǎng)中,股票價(jià)格不能獨(dú)立于宏觀經(jīng)濟(jì)之外,眾多股票價(jià)格的時(shí)間序列表現(xiàn)出較強(qiáng)的相關(guān)性和較大的跳躍,且這些跳躍通常同一些事件聯(lián)系相關(guān),如牛市和熊市、金融危機(jī)、政府新的金融或經(jīng)濟(jì)政策等.Hardy[2]發(fā)現(xiàn)Markov鏈能顯著地?cái)M合這類(lèi)金融市場(chǎng)狀態(tài)的變化過(guò)程,且股票收益具有較強(qiáng)的Markov機(jī)制轉(zhuǎn)移性質(zhì).此后,利用Markov鏈刻畫(huà)金融市場(chǎng)狀態(tài)變化過(guò)程成為經(jīng)濟(jì)和金融領(lǐng)域的一個(gè)研究熱點(diǎn).在資產(chǎn)組合選擇問(wèn)題的研究方面,Zhou和Yin[3]研究了Markov機(jī)制轉(zhuǎn)移下的連續(xù)時(shí)間均值方差最優(yōu)投資組合選擇問(wèn)題.Cakmak和zekici[4]用時(shí)齊的Markov過(guò)程描述市場(chǎng)狀態(tài)的變化過(guò)程,得到了多階段最優(yōu)投資問(wèn)題的最優(yōu)投資策略的解析解.在文獻(xiàn)[4]的基礎(chǔ)上,Wei和Ye[5]引入了破產(chǎn)風(fēng)險(xiǎn)控制,Wu和Li[6]進(jìn)一步考慮了投資終止時(shí)間不確定對(duì)最優(yōu)投資決策的影響.Costa和Araujo[7] 研究了參數(shù)受Markov機(jī)制轉(zhuǎn)移調(diào)制的多階段均值方差資產(chǎn)組合選擇問(wèn)題,并將結(jié)果應(yīng)用到了動(dòng)態(tài)資產(chǎn)組合選擇問(wèn)題的破產(chǎn)風(fēng)險(xiǎn)控制中.Xie[8] 研究了風(fēng)險(xiǎn)資產(chǎn)價(jià)格和負(fù)債都是受到Markov機(jī)制轉(zhuǎn)移調(diào)制的連續(xù)時(shí)間資產(chǎn)負(fù)債管理問(wèn)題.在上述馬爾科夫機(jī)制轉(zhuǎn)移模型中,有一個(gè)基本的假設(shè):Markov鏈的狀態(tài)是完全可觀測(cè)的,且狀態(tài)轉(zhuǎn)移矩陣是定常的.

      然而,金融市場(chǎng)中不僅存在投資者可以觀測(cè)到的狀態(tài)信息(如利率、通貨膨脹率、匯率等),還存在投資者無(wú)法觀測(cè)到的市場(chǎng)狀態(tài)信息,正是這些不可觀測(cè)的金融市場(chǎng)狀態(tài)信息導(dǎo)致了資產(chǎn)收益的變化,陳國(guó)華等[9] 研究了資產(chǎn)收益率為模糊數(shù)的投資組合選擇問(wèn)題.事實(shí)上,絕大多數(shù)投資者僅能依據(jù)從市場(chǎng)中觀測(cè)得到的信息而非市場(chǎng)上全部的信息做出投資決策,這導(dǎo)致了不完全信息下的投資決策問(wèn)題,隱馬爾科夫鏈( hidden Markov chain)常用來(lái)刻畫(huà)不可觀測(cè)狀態(tài)的變化過(guò)程.Sass和Haussmann[10]、Rieder和Buerle[11]、Putschgl 和Sass等[12]考慮了不可觀測(cè)狀態(tài)由隱馬爾科夫鏈刻畫(huà)的連續(xù)時(shí)間最優(yōu)投資決策問(wèn)題,分析了不可觀測(cè)狀態(tài)對(duì)最優(yōu)投資策略的影響.基于優(yōu)化問(wèn)題的可解性,以往有關(guān)不完全信息下最優(yōu)投資問(wèn)題的研究集中于連續(xù)時(shí)間情形,離散時(shí)間最優(yōu)投資組合選擇問(wèn)題的研究還很少.雖然Canakolu和zekici[13]考慮了隱Markov機(jī)制轉(zhuǎn)移市場(chǎng)中的多階段HARA效用最大化問(wèn)題,但沒(méi)有得到最優(yōu)策略的解析解.連續(xù)時(shí)間模型在求解最優(yōu)投資組合選擇問(wèn)題中具有非常好的便利性,然而離散時(shí)間多階段模型更符合金融市場(chǎng)實(shí)際決策,且連續(xù)時(shí)間投資組合選擇問(wèn)題的實(shí)現(xiàn)依然需要借助于離散化的工具.所以,在具有不可觀測(cè)市場(chǎng)狀態(tài)的金融市場(chǎng)中,離散時(shí)間多階段最優(yōu)資產(chǎn)組合選擇問(wèn)題的研究具有重要的現(xiàn)實(shí)和理論意義.

      基于以上研究和思考,本文利用離散時(shí)間有限狀態(tài)隱Markov鏈刻畫(huà)金融市場(chǎng)上不可觀測(cè)狀態(tài)的變化過(guò)程,建立了不完全信息下多階段最優(yōu)投資組合選擇問(wèn)題的基準(zhǔn)準(zhǔn)則模型.通過(guò)擴(kuò)大狀態(tài)空間和構(gòu)造充分統(tǒng)計(jì)量,不完全信息下的優(yōu)化問(wèn)題轉(zhuǎn)化為完全信息下的優(yōu)化問(wèn)題,利用動(dòng)態(tài)規(guī)劃方法求得了最優(yōu)投資組合策略和最優(yōu)值函數(shù)的解析表達(dá)式.

      5 總 結(jié)

      本文在具有可觀測(cè)狀態(tài)和不可觀測(cè)狀態(tài)的金融市場(chǎng)中,利用隱Markov鏈模擬不可觀測(cè)市場(chǎng)狀態(tài)的變化過(guò)程,研究了不完全信息市場(chǎng)中的多階段最優(yōu)資產(chǎn)組合選擇問(wèn)題.通過(guò)構(gòu)造充分統(tǒng)計(jì)量,不完全信息下的最優(yōu)資產(chǎn)組合選擇問(wèn)題轉(zhuǎn)變?yōu)橥耆畔⑾碌淖顑?yōu)資產(chǎn)組合選擇問(wèn)題,采用動(dòng)態(tài)規(guī)劃方法求解優(yōu)化問(wèn)題,得到了各階段最優(yōu)資產(chǎn)組合策略和最優(yōu)值函數(shù)的解析表達(dá)式.同時(shí),給出了市場(chǎng)狀態(tài)完全可觀測(cè)時(shí)最優(yōu)資產(chǎn)組合選擇問(wèn)題的最優(yōu)組合策略和最優(yōu)值函數(shù).本文中所建立的模型還未考慮金融市場(chǎng)存在的各種約束,以后的工作中可以進(jìn)一步考慮不完全信息市場(chǎng)中存在各種摩擦?xí)r的最優(yōu)資產(chǎn)組合選擇問(wèn)題.

      參考文獻(xiàn)

      [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(guó)華,陳收,房勇,汪壽陽(yáng). 基于模糊收益率的組合投資模型 [J]. 經(jīng)濟(jì)數(shù)學(xué),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(guó)華,陳收,房勇,汪壽陽(yáng). 基于模糊收益率的組合投資模型 [J]. 經(jīng)濟(jì)數(shù)學(xué),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(guó)華,陳收,房勇,汪壽陽(yáng). 基于模糊收益率的組合投資模型 [J]. 經(jīng)濟(jì)數(shù)學(xué),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

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