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

【摘要】：股票投資組合策略長期以來一直是人們非常關(guān)注的話題,無論是理論界還是實(shí)踐中,人們都對如何在眾多股票中選擇一個在既定風(fēng)險下能給他們帶來最高回報的資產(chǎn)組合抱有濃厚的興趣。早在1952年馬克維茨(Markowitz)就提出了一個震驚學(xué)術(shù)界的均值方差資產(chǎn)組合模型。這個模型在理論上確實(shí)是一個巨大的成功,Markowitz還因此獲得了諾貝爾經(jīng)濟(jì)學(xué)獎。 但是在實(shí)踐中越來越多的人反應(yīng),用Markowitz這個模型選擇的投資組合表現(xiàn)有時不盡如人意,極有可能會產(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他們的協(xié)方差矩陣息息相關(guān)。當(dāng)資產(chǎn)的個數(shù)較大時,所對應(yīng)的協(xié)方差矩陣是高維的,如果用傳統(tǒng)的樣本估計法來估計協(xié)方差就會產(chǎn)生很大的誤差,從而導(dǎo)致相應(yīng)計算所得到的投資組合也會產(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)也變得越來越容易,但是我們不能一味地想通過擴(kuò)大樣本量來減少估計誤差,因為在時間序列分析領(lǐng)域存在一個數(shù)據(jù)穩(wěn)定性問題,如果樣本量太大,時間跨度太長就會影響數(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)方差估計方法,例如Fan, Fan and Lv (2008)[1],就引入三因素模型估計法。但是相比之下,較少有人將這些估計方法應(yīng)用到國內(nèi)數(shù)據(jù)的研究中。隨著中國資本市場越來越完善,越來越多的投資者參與投資,一個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滬深主板市場A股股票的交易數(shù)據(jù)為基礎(chǔ),來研究利用Fama-French三因素模型估計高維協(xié)方差矩陣較傳統(tǒng)樣本估計法的優(yōu)越性,以及利用此方法研究中國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】：廈門大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 鄧長榮,馬永開;三因素模型在中國證券市場的實(shí)證研究[J];管理學(xué)報;2005年05期

2 范龍振,單耀文;交易額、A股比例、勢效應(yīng)和三因子模型[J];管理科學(xué)學(xué)報;2004年03期

3 吳世農(nóng),許年行;資產(chǎn)的理性定價模型和非理性定價模型的比較研究——基于中國股市的實(shí)證分析[J];經(jīng)濟(jì)研究;2004年06期

4 楊p，

本文編號：1919518