本文選題：資產(chǎn)配置 + Black-Litterman模型�。� 參考：《山東大學(xué)》2017年碩士論文

【摘要】：隨著金融產(chǎn)品供給豐富、機(jī)構(gòu)和個人資產(chǎn)配置需求增加,如何進(jìn)行合理有效的資產(chǎn)分配成為人們面臨的重要議題,研究表明,資產(chǎn)選擇和資產(chǎn)配置可以解釋投資組合90%左右的業(yè)績變動,自從Markowitz提出投資組合理論以后,Black和Litterman提出的將市場數(shù)據(jù)與投資人觀點(diǎn)相結(jié)合的資產(chǎn)配置方法受到了廣泛關(guān)注。本文運(yùn)用Black和Litterman(1992)提出的Black-Litterman模型(BL模型),研究了基于2010年到2016年股票數(shù)據(jù)的行業(yè)配置策略,對前人的研究做了如下拓展:1、對BL模型逆優(yōu)化階段應(yīng)該采用何種輸入權(quán)重進(jìn)行了理論梳理,并用Elton,Gruber,Padberg(1976)提出的資產(chǎn)選擇模型(EGP模型)生成BL模型逆優(yōu)化階段的輸入權(quán)重。2、指出了前人在設(shè)定觀點(diǎn)方差時的一個錯誤。3、將回測期數(shù)延長,增加了倉位穩(wěn)定性指標(biāo)來評價(jià)策略。本文BL模型中的觀點(diǎn)和觀點(diǎn)方差一律用GARCH類模型生成,逆優(yōu)化階段所用的輸入權(quán)重用EGP模型來生成,與輸入權(quán)重直接采用市值權(quán)重的策略進(jìn)行了比較,在大幅震蕩和小幅震蕩兩種不同行情模式下,分別對1年的數(shù)據(jù)進(jìn)行了回測,發(fā)現(xiàn)用EGP模型生成輸入權(quán)重的策略在穩(wěn)定性、收益性上均優(yōu)于直接用市值權(quán)重的策略,為個人和機(jī)構(gòu)投資者的資產(chǎn)配置提供了思路。本文分為六章,第1章闡述研究的背景,并對研究思路和創(chuàng)新點(diǎn)做了簡要說明,指出了本文的研究意義。第2章對國內(nèi)國外文獻(xiàn)進(jìn)行綜述,簡要闡述了投資組合、資產(chǎn)定價(jià)理論,重點(diǎn)梳理了前人對資產(chǎn)配置、Black-Litterman模型的研究,并對其研究特點(diǎn)進(jìn)行了總結(jié),發(fā)現(xiàn)前人對BL模型逆優(yōu)化過程中的輸入權(quán)重討論較少,國內(nèi)研究的回測期數(shù)大都很短,而且在策略評價(jià)時對策略的穩(wěn)定性涉及不多,所以本文在這三方面進(jìn)行了拓展。第3、4章是理論基礎(chǔ),從均值方差模型、資本資產(chǎn)定價(jià)模型開始論述,隨后介紹了 Black-Litterman模型,從貝葉斯理論的角度給出了 BL模型的簡要推導(dǎo),并對各種輸入量的確定進(jìn)行了詳細(xì)論述,重點(diǎn)介紹了前人研究中逆優(yōu)化過程里的輸入權(quán)重、觀點(diǎn)及其方差的確定方法;對輸入權(quán)重的確定,本文總結(jié)他人的研究方法后提出了自己的意見,并指出了前人研究中觀點(diǎn)方差設(shè)定的錯誤;隨后介紹了本文用來確定逆優(yōu)化過程中輸入權(quán)重的資產(chǎn)選擇理論(EGP模型)。第4章介紹了本文衡量投資者觀點(diǎn)時所用到的GARCH類模型和該模型的估計(jì)及預(yù)測。第5章是實(shí)證,這一部分首先對數(shù)據(jù)選擇做了簡述,對數(shù)據(jù)的基本統(tǒng)計(jì)性質(zhì)進(jìn)行了分析,其后進(jìn)行BL模型輸入量的求解,首先根據(jù)EGP模型進(jìn)行了資產(chǎn)權(quán)重(作為BL模型的輸入權(quán)重)的計(jì)算,隨后確定各種行業(yè)指數(shù)的GARCH類模型形式,同時確定其模型系數(shù)并對模型進(jìn)行檢驗(yàn),然后對回測期的觀點(diǎn)和方差進(jìn)行預(yù)測,最終將這些輸入量代入BL模型公式,得到最終的資產(chǎn)配置權(quán)重。最后在兩種不同行情模式下,分別對1年的數(shù)據(jù)進(jìn)行了回測,統(tǒng)計(jì)了策略收益的各項(xiàng)評價(jià)指標(biāo),對策略在兩種環(huán)境下的表現(xiàn)及適用性進(jìn)行了分析,同時從權(quán)重累計(jì)變化的角度對策略的調(diào)倉特征進(jìn)行了評價(jià)。本文的主要結(jié)論如下:1、對于BL模型中的輸入權(quán)重,本文認(rèn)為應(yīng)該用以資本市場均衡為前提的模型求得,作為中性起始點(diǎn)。只考慮風(fēng)險(xiǎn)來構(gòu)建起始輸入權(quán)重這種途徑還沒有足夠的理論支撐。2、基于因素模型的資產(chǎn)選擇模型(EGP模型)生成的權(quán)重反映了樣本期內(nèi)各行業(yè)的概況,指數(shù)收益率與權(quán)重正相關(guān);綜合收益的求解過程中,模型賦予了引致均衡收益較大的權(quán)重,且后驗(yàn)BL收益率作為加權(quán)結(jié)果,其值處在觀點(diǎn)和引致均衡收益中間。3、橫盤小幅震蕩(2016年)與大幅牛熊震蕩(2015年)的環(huán)境中,用EGP模型生成的權(quán)重作為輸入權(quán)重的BL模型在回測區(qū)間累計(jì)收益、收益穩(wěn)定性、權(quán)重穩(wěn)定性上都優(yōu)于直接用市值權(quán)重作為輸入權(quán)重的BL模型。
[Abstract]:With the rich supply of financial products and the increasing demand for the allocation of institutions and individual assets, how to carry out reasonable and effective allocation of assets has become an important issue for people. The research shows that asset selection and asset allocation can explain the performance changes about 90% of the portfolio. Since Markowitz put forward the portfolio theory, Black and Litterman have been proposed. The method of asset allocation which combines market data with investor views has been widely concerned. This paper uses Black and Litterman (1992) Black-Litterman model (BL model) to study the industry configuration strategy based on stock data from 2010 to 2016, and extends the previous research to the following: 1, the inverse optimization stage of BL model What kind of input weight should be used to sort out the theory and use the asset selection model (EGP model) proposed by Elton, Gruber, Padberg (1976) to generate the input weight.2 of the BL model inverse optimization stage, and points out a wrong.3 in setting the point of view variance, prolonging the number of back test period and increasing the stability index of the position to evaluate the strategy. The viewpoint and point of view variance in the BL model are generated by the GARCH model. The input weight used in the inverse optimization stage is generated by the EGP model, and is compared with the strategy of the value weighting directly using the input weight. In the two different market modes of large concussion and small amplitude concussion, the data of 1 years are measured, and EGP is found to be used. The strategy of model generating input weight is better than the strategy of directly using market value weight in stability and profit. It provides ideas for the asset allocation of individual and institutional investors. This paper is divided into six chapters. The first chapter expounds the background of the study, and gives a brief description of the research ideas and innovation points, and points out the significance of the study. The second chapter is to the country. The literature of internal and foreign countries is reviewed, and the portfolio and asset pricing theory are briefly expounded. The research on the assets allocation and Black-Litterman model has been combed, and the characteristics of the research are summarized. It is found that the predecessors have less discussion on the input weight in the inverse optimization process of the BL model, and the number of back test periods in the domestic research is mostly short, and The stability of strategy is not much involved in strategy evaluation, so this article has been expanded in these three aspects. Chapter 3,4 is the theoretical basis, from the mean variance model, capital asset pricing model, then the Black-Litterman model, from the perspective of Bayesian theory gives a brief derivation of the BL model, and a variety of input quantities. The determination is discussed in detail, and the input weight of the inverse optimization process in the previous study is introduced, the method of determining the point of view and its variance is introduced, and the input weight is determined. After summarizing the research methods of others, the author puts forward his own opinion and points out the error of the point of view variance setting in the previous study. In the fourth chapter, the fourth chapter introduces the GARCH model and the estimation and prediction of the investor's view. The fifth chapter is an empirical study. This part is a brief introduction to the data selection, analyses the basic statistical properties of the data, and then carries out the BL model. In order to solve the input, the weight of the assets (as the input weight of the BL model) is calculated according to the EGP model, and then the GARCH model of various industry indices is determined, and the model coefficient is determined and the model is tested. Then the viewpoint and the square difference of the back test period are predicted, and finally the input is replaced by the BL model public. Finally, we get the final asset allocation weight. Finally, under the two different market models, the data of 1 years are measured, the evaluation indexes of the strategy income are counted, the performance and applicability of the strategy under the two environment are analyzed. At the same time, the storehouse characteristics of the strategy are evaluated from the angle of cumulative weight change. The main conclusions of this paper are as follows: 1, for the input weight in the BL model, this paper thinks that the model should be obtained with the capital market equilibrium as the precondition, as a neutral starting point. There is not enough theory to support.2 and the right of the asset selection model based on the element model (EGP model). It reflects the general situation of each industry in the sample period, and the index return rate is positively correlated with the weight. In the process of solving the comprehensive income, the model gives the weight of the higher equilibrium income, and the posterior BL yield is the weighted result, and its value is in the middle.3 of the viewpoint and the induced equilibrium income, the horizontal shock (2016) and the big bull bear shock (2015) In the environment of the year), the BL model, which is generated by the weight of the EGP model as the input weight, has the cumulative income, the stability of the return and the stability of the weight, which is better than the BL model that directly uses the market value weight as the input weight.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51

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