本文選題：三因素模型 + 高維協(xié)方差　； 參考：《廈門(mén)大學(xué)》2014年碩士論文

【摘要】：股票投資組合策略長(zhǎng)期以來(lái)一直是人們非常關(guān)注的話題,無(wú)論是理論界還是實(shí)踐中,人們都對(duì)如何在眾多股票中選擇一個(gè)在既定風(fēng)險(xiǎn)下能給他們帶來(lái)最高回報(bào)的資產(chǎn)組合抱有濃厚的興趣。早在1952年馬克維茨(Markowitz)就提出了一個(gè)震驚學(xué)術(shù)界的均值方差資產(chǎn)組合模型。這個(gè)模型在理論上確實(shí)是一個(gè)巨大的成功,Markowitz還因此獲得了諾貝爾經(jīng)濟(jì)學(xué)獎(jiǎng)。 但是在實(shí)踐中越來(lái)越多的人反應(yīng),用Markowitz這個(gè)模型選擇的投資組合表現(xiàn)有時(shí)不盡如人意,極有可能會(huì)產(chǎn)生很大的誤差。背后的原因可能是多方面的,但其中不可忽視的是,投資組合表現(xiàn)與能否準(zhǔn)確刻畫(huà)資產(chǎn)組合里面各資產(chǎn)的關(guān)聯(lián)程度,尤其是能否有效地估計(jì)他們的協(xié)方差矩陣息息相關(guān)。當(dāng)資產(chǎn)的個(gè)數(shù)較大時(shí),所對(duì)應(yīng)的協(xié)方差矩陣是高維的,如果用傳統(tǒng)的樣本估計(jì)法來(lái)估計(jì)協(xié)方差就會(huì)產(chǎn)生很大的誤差,從而導(dǎo)致相應(yīng)計(jì)算所得到的投資組合也會(huì)產(chǎn)生較大的誤差。 雖然由于信息科技的發(fā)達(dá),獲取更多的數(shù)據(jù)也變得越來(lái)越容易,但是我們不能一味地想通過(guò)擴(kuò)大樣本量來(lái)減少估計(jì)誤差,因?yàn)樵跁r(shí)間序列分析領(lǐng)域存在一個(gè)數(shù)據(jù)穩(wěn)定性問(wèn)題,如果樣本量太大,時(shí)間跨度太長(zhǎng)就會(huì)影響數(shù)據(jù)的穩(wěn)定性,因此如何構(gòu)建協(xié)方差矩陣,是理論界一大熱點(diǎn),該研究對(duì)實(shí)際數(shù)據(jù)分析,尤其在金融領(lǐng)域的應(yīng)用有舉足輕重的作用。 目前學(xué)術(shù)界已經(jīng)探討出了很多在不同假設(shè)前提下的高維協(xié)方差估計(jì)方法,例如Fan, Fan and Lv (2008)[1],就引入三因素模型估計(jì)法。但是相比之下,較少有人將這些估計(jì)方法應(yīng)用到國(guó)內(nèi)數(shù)據(jù)的研究中。隨著中國(guó)資本市場(chǎng)越來(lái)越完善,越來(lái)越多的投資者參與投資,一個(gè)更合理可靠的股票投資組合就顯得尤為重要。所以本論文決定以中國(guó)滬深主板市場(chǎng)A股股票的交易數(shù)據(jù)為基礎(chǔ),來(lái)研究利用Fama-French三因素模型估計(jì)高維協(xié)方差矩陣較傳統(tǒng)樣本估計(jì)法的優(yōu)越性,以及利用此方法研究中國(guó)股票投資組合是否具有良好的效果。
[Abstract]:The stock portfolio strategy has long been a topic of great concern. Both in theory and in practice, people have a strong interest in how to choose a portfolio of assets that can bring them the highest returns at a given risk. In 1952, Markowitz put forward an earthquake. Startled the academia of the mean variance portfolio model. This model was indeed a great success in theory. Markowitz also won the Nobel prize in economics.
But in practice, more and more people respond, the investment portfolio selected by the Markowitz model is sometimes unsatisfactory, and it is likely to produce great errors. The reasons behind it may be multifaceted, but what can not be ignored is whether the portfolio performance and the accuracy of the relationship between the assets in the portfolio can be accurately depicted. In particular, whether their covariance matrix can be effectively estimated is closely related. When the number of assets is larger, the corresponding covariance matrix is high dimension. If the covariance is estimated by the traditional sample estimation method, it will produce a large error, which leads to the larger error in the corresponding calculation.
Although it is more and more easy to obtain more data because of the development of information technology, we can not blindly reduce the estimation error by expanding the sample size, because there is a data stability problem in the domain of time series analysis. If the sample size is too large and the time span is too long, it will affect the stability of the data. How to construct the covariance matrix is a hot topic in the theoretical field. This research plays a decisive role in the actual data analysis, especially in the financial field.
At present, there have been many high dimensional covariance estimation methods under different assumptions, such as Fan, Fan and Lv (2008) [1], and the introduction of three factor model estimation. However, few people apply these estimation methods to domestic data research. As China's capital market is more and more perfect and more and more, more and more The investor participates in the investment, a more reasonable and reliable stock portfolio is particularly important. Therefore, based on the transaction data of A shares in the Shanghai and Shenzhen stock market, this paper studies the superiority of using the Fama-French three factor model to estimate the higher dimension covariance matrix than the traditional sample estimation method, and uses this method to make use of this method. Study whether China's stock portfolio has a good effect.
【學(xué)位授予單位】：廈門(mén)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F224;F832.51

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本文編號(hào)：1919518