本文選題：資產(chǎn)配置　切入點：參數(shù)不確定性　出處：《西南財經(jīng)大學》2013年碩士論文

【摘要】：隨著國內(nèi)資本市場的日益發(fā)展,市場上可投資的金融產(chǎn)品越來越多,而且機構(gòu)投資者的日益壯大,所管理的產(chǎn)品規(guī)模越來越大,同時持有大量的證券頭寸該是如何分布呢?在理論上,Markowitz(1952)提出的均值方差模型是關(guān)于資產(chǎn)配置“完美”的理論,原理是在一定風險控制下尋求收益率最大化的組合,或者是在一定收益率的要求下尋求風險最小化的組合。但是這個理論是“事后最優(yōu)的”,即當風險資產(chǎn)的收益率已經(jīng)實現(xiàn)了的時候去尋求的“事后”最優(yōu)的資產(chǎn)配置。所以在實際應用中,若使用均值方差模型進行資產(chǎn)配置就需要對風險資產(chǎn)的參數(shù)進行估計了,估計誤差就難以避免了。不幸的是,均值方差模型對估計誤差極其敏感,所估計的參數(shù)發(fā)生微小變化將導致權(quán)重發(fā)生劇烈的變化,同時還存在非直覺性以及誤差放大等缺陷,在沒有賣空限制條件下模型得到的權(quán)重發(fā)生權(quán)重過度集中的情形,這本身就違背了使用資產(chǎn)組合理論進行資產(chǎn)配置的初衷——通過構(gòu)建投資組合以分散非系統(tǒng)風險。所以,在實際投資應用中,甚少投資者使用均值方差模型。因此本文就立足于這樣的一個具體問題,嘗試給出一個有效的資產(chǎn)配置的量化方案。 本文首先通過實證方法證明了參數(shù)存在時變問題,利用歷史數(shù)據(jù)估計得到的參數(shù)并不能直接運用到傳統(tǒng)的均值方差模型中,發(fā)現(xiàn)期望收益率的不確定性程度越高,對組合的效率影響越大,而協(xié)方差矩陣的不確定性對模型的影響則不存在這樣的問題；若從夏普損失比率的角度來看,協(xié)方差矩陣的不確定對模型的影響要不收益率的要低。因此在實際應用中利用歷史數(shù)據(jù)估計波動率是相對可靠的。 其次,在以上的討論的基礎(chǔ)上,以均值-CVaR的框架下總結(jié)了以貝葉斯算法為核心的資產(chǎn)組合模型體系。通過利用蒙特卡洛模擬和壓力測試的方式,從損失、偏誤和有效性三個方面討論參數(shù)的估計精度問題,發(fā)現(xiàn)貝葉斯算法為核心的模型皆能提高對收益率的估計精度,但未能提高對波動率的估計精度；然后從夏普損失率函數(shù)角度討論了四種模型的效率問題,發(fā)現(xiàn)在同樣的經(jīng)驗意見以及置信度的情況下,魯棒法估計和全觀點估計性能相當優(yōu)越,非常適合實際投資需求。 最后,重點對全觀點模型進行回溯測試,利用“市場上漲時投資高β股票、市場下跌時投資低β股票”的投資邏輯,通過歷史數(shù)據(jù)估計股票的β值,分別在對經(jīng)驗意見的不同置信度下和對投資組合的不同CVaR下進行組合的構(gòu)建,發(fā)現(xiàn)全觀點模型均能獲得超額的收益。 因此,貝葉斯算法下的資產(chǎn)組合模型能夠在一定程度下避免參數(shù)不確定對資產(chǎn)組合的效率損失問題,能夠成為在實際投資中資產(chǎn)配置的有力工具。
[Abstract]:With the development of the domestic capital market, there are more and more financial products that can be invested in the market. With the growing of institutional investors, the scale of the products managed is becoming larger and larger. At the same time, how should we distribute a large number of securities positions? In theory, the mean variance model proposed by Markowitz (1952) is about the theory of "perfect" asset allocation, and the principle is to seek the combination of maximization of return rate under certain risk control. Or seek a combination of risk minimization under a certain rate of return. But this theory is "ex post optimal", that is, when the return rate of risk assets has been achieved, the "afterwards" optimal asset allocation. So in practice, If the mean variance model is used for asset allocation, it is necessary to estimate the parameters of the risk asset, and the estimation error is unavoidable. Unfortunately, the mean variance model is extremely sensitive to the estimation error. Small changes in the estimated parameters will lead to drastic changes in weights, and there are also some defects such as non-intuitionism and error amplification, and the weight of the model will be overconcentrated under the condition of no short selling restriction. This in itself goes against the original intention of using portfolio theory to allocate assets by building a portfolio to spread non-systemic risk. Very few investors use mean-variance model, so this paper tries to give an effective quantitative scheme of asset allocation based on such a specific problem. In this paper, we first prove that the parameters are time-varying by empirical method. The parameters estimated by historical data can not be directly applied to the traditional mean variance model, and the higher the degree of uncertainty of the expected return is, the higher the degree of uncertainty is. The greater the effect on the efficiency of the combination, the less the uncertainty of the covariance matrix has on the model; if viewed from the perspective of Sharp's loss ratio, The influence of uncertainty of covariance matrix on the model is less than that of the yield, so it is relatively reliable to estimate volatility by using historical data in practical application. Secondly, on the basis of the above discussion, the paper summarizes the portfolio model system with Bayesian algorithm as the core under the framework of mean value-CVaR. The estimation accuracy of parameters is discussed in three aspects: error and validity. It is found that the model with Bayesian algorithm as the core can improve the accuracy of the estimation of the return rate, but it can not improve the estimation accuracy of the volatility. Then the efficiency problems of the four models are discussed from the perspective of Sharpe loss rate function. It is found that under the same empirical opinion and confidence degree, the performance of robust estimation and full view estimation is quite superior, which is very suitable for the actual investment demand. Finally, this paper focuses on the backtracking test of the full view model, using the logic of "investing in high 尾 stocks when the market rises and investing in low 尾 stocks when the market falls", and estimating the 尾 value of stocks through historical data. Under the different confidence degree of the experience opinion and the different CVaR of the investment portfolio, it is found that the whole view model can obtain excess income. Therefore, the portfolio model based on Bayesian algorithm can avoid the loss of portfolio efficiency caused by parameter uncertainty to a certain extent, and become a powerful tool for asset allocation in real investment.
【學位授予單位】：西南財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.5;F224

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