本文選題：凈資產(chǎn)收益率 + 波動(dòng)率��； 參考：《成都理工大學(xué)》2014年碩士論文

【摘要】：凈資產(chǎn)收益率是一個(gè)綜合性極強(qiáng)的財(cái)務(wù)指標(biāo)，直接反應(yīng)了一個(gè)公司的盈利能力，是現(xiàn)代成熟市場(chǎng)中廣大投資者最為倚重的參考指標(biāo)，在投資領(lǐng)域一直備受各界關(guān)注。本文借助已有研究成果，選取44家中國(guó)上市公司1994年--2013年凈資產(chǎn)收益率數(shù)據(jù)為研究對(duì)象，對(duì)凈資產(chǎn)收益率波動(dòng)情況進(jìn)行了實(shí)證研究，并分析了凈資產(chǎn)收益率波動(dòng)率與現(xiàn)金流相互之間的動(dòng)態(tài)關(guān)系，為投資者、經(jīng)營(yíng)管理者以及后續(xù)研究提供實(shí)證經(jīng)驗(yàn)和建議。 本文主要研究?jī)?nèi)容有： （1）引言。該部分首先闡述了本文的研究背景、意義，包括問題的提出，并明確界定了相關(guān)概念，然后介紹了本文的研究思路及方法，最后是本文的創(chuàng)新點(diǎn)及不足。 （2）文獻(xiàn)綜述。該部分主要是對(duì)國(guó)內(nèi)外關(guān)于凈資產(chǎn)收益率、波動(dòng)率模型以及向量自回歸模型的文獻(xiàn)的研究，并歸納其主要觀點(diǎn)和研究方法，為本文實(shí)證研究指明方向。 （3）波動(dòng)率模型概述。該部分詳細(xì)介紹了各種廣泛應(yīng)用于金融領(lǐng)域的主流波動(dòng)率模型，并通過分析比較，評(píng)選出最適合本文建模的類型。 （4）凈資產(chǎn)收益率波動(dòng)率實(shí)證分析。該部分建立了多個(gè)波動(dòng)率模型，并對(duì)其進(jìn)行綜合評(píng)價(jià)，找出相對(duì)較優(yōu)的模型，用以凈資產(chǎn)收益率波動(dòng)率預(yù)測(cè)以及檢驗(yàn)凈資產(chǎn)收益率波動(dòng)率中的不對(duì)稱性影響。 （5）凈資產(chǎn)收益率波動(dòng)率與現(xiàn)金流之間的動(dòng)態(tài)關(guān)系的實(shí)證研究。該部分先是以凈資產(chǎn)收益率波動(dòng)率和現(xiàn)金流為變量建立向量自回歸模型，并借助Granger因果關(guān)系分析法和脈沖響應(yīng)函數(shù)法進(jìn)行結(jié)構(gòu)分析；再以凈資產(chǎn)收益率和現(xiàn)金流為變量建立向量自回歸模型，并借助方差分解進(jìn)行結(jié)構(gòu)分析。 （6）結(jié)論。該部分對(duì)本文所做研究做出總結(jié)，并對(duì)存在的問題進(jìn)行說明，最后指出未來研究方向。 本文首先通過文獻(xiàn)比較以及理論演繹，得出結(jié)論：GARCH模型對(duì)金融數(shù)據(jù)時(shí)間序列的擬合效果和預(yù)測(cè)能力較好，且預(yù)測(cè)期限越短預(yù)測(cè)能力越好；EGARCH模型在捕捉波動(dòng)率中的非對(duì)稱性影響中表現(xiàn)較好；已實(shí)現(xiàn)波動(dòng)率對(duì)高頻交易數(shù)據(jù)的預(yù)測(cè)能力較好。本文借助GARCH模型和EGARCH（1，1）模型，以及另外幾種基本的GARCH擴(kuò)展模型--GARCH-M模型、TGARCH模型和PGARCH模型來實(shí)證分析凈資產(chǎn)收益率波動(dòng)情況，結(jié)果表明：GARCH（1，1）模型對(duì)中國(guó)上市公司凈資產(chǎn)收益率的擬合效果和預(yù)測(cè)能力相對(duì)較好，，EGARCH（1，1）模型證實(shí)了中國(guó)上市公司凈資產(chǎn)收益率波動(dòng)率中存在較為顯著的非對(duì)稱性影響。 關(guān)于凈資產(chǎn)收益率波動(dòng)率與現(xiàn)金流之間的相互關(guān)系的實(shí)證研究。本文先是以凈資產(chǎn)收益率波動(dòng)率和現(xiàn)金流為變量建立VAR模型，并借助Granger因果關(guān)系分析法和脈沖響應(yīng)函數(shù)分析法進(jìn)行結(jié)構(gòu)化分析，結(jié)論顯示：凈資產(chǎn)收益率波動(dòng)率與現(xiàn)金流之間存在雙向的Granger因果關(guān)系，即二者互為對(duì)方的Granger原因；凈資產(chǎn)收益率波動(dòng)率受自身殘差沖擊的影響大于受現(xiàn)金流殘差的影響，且受自身影響時(shí)響應(yīng)更為迅速，同時(shí)，現(xiàn)金流受自身殘差沖擊影響大于受凈資產(chǎn)收益率波動(dòng)率的殘差影響。其次以凈資產(chǎn)收益率和現(xiàn)金流為變量建立VAR模型，并利用方差分解進(jìn)行結(jié)構(gòu)化分析，結(jié)論顯示：凈資產(chǎn)收益率波動(dòng)率變動(dòng)由自身殘差所導(dǎo)致的比例遠(yuǎn)大于由現(xiàn)金流殘差所導(dǎo)致的比例，同樣現(xiàn)金流方差變動(dòng)由自身殘差貢獻(xiàn)的比例也更大，這種貢獻(xiàn)比例在沖擊產(chǎn)生時(shí)會(huì)發(fā)生適當(dāng)?shù)淖兓�，并在未來某期保持穩(wěn)定。
[Abstract]:Net asset yield is a comprehensive financial index, which directly reflects the profitability of a company. It is the most important reference index for the majority of investors in the modern mature market. In the field of investment, all walks of life have been paid attention to. In this paper, the net assets income of 44 Chinese listed companies in 1994 is selected with the help of the existing research results. The rate data is the research object, the fluctuation of net asset returns is empirically studied, and the dynamic relationship between the volatility of net assets yield and the cash flow is analyzed. The empirical experience and suggestions are provided for the investors, managers and follow-up research.
The main contents of this paper are as follows:
(1) introduction. This part first expounds the background and significance of this paper, including the proposal of the problem, and clearly defines the relevant concepts, and then introduces the research ideas and methods of this article, and finally is the innovation and deficiency of this article.
(2) literature review. This part mainly studies the literature on net asset returns, volatility model and vector autoregressive model, and sums up its main viewpoints and research methods, which indicates the direction of the empirical study.
(3) an overview of the volatility model. This section introduces a variety of mainstream volatility models widely used in the financial field, and selects the most suitable models for this model through analysis and comparison.
(4) an empirical analysis of the volatility of net asset returns. This part sets up multiple volatility models and makes a comprehensive evaluation to find out a relatively superior model, which is used to predict the volatility of net asset returns and to test the asymmetry in the volatility of net asset returns.
(5) an empirical study of the dynamic relationship between the volatility of net asset returns and the cash flow. This part first establishes a vector autoregressive model with the volatility of net asset returns and cash flow as a variable, and uses the Granger causality analysis and impulse response function to analyze the structure, and then changes in the net assets yield and cash flow. Vector auto regression model is established and variance analysis is used for structural analysis.
(6) conclusion. This part summarizes the research done in this paper, explains the existing problems, and points out the future research direction.
Through literature comparison and theoretical deduction, this paper draws a conclusion that the GARCH model is better for the fitting effect and prediction ability of the time series of financial data, and the better the prediction time limit is, the better the EGARCH model is in the unsymmetry effect in the capture of the volatility; it has realized the preview of the volatility rate to the high frequency transaction data. With the help of GARCH model and EGARCH (1,1) model, and several other basic GARCH expansion model --GARCH-M models, TGARCH model and PGARCH model, this paper empirically analyses the fluctuation of net asset returns. The results show that the fitting effect and prediction ability of GARCH (1,1) model to the net asset returns of Chinese listed companies are relative. The EGARCH (1,1) model confirms that there is a significant asymmetric effect in the volatility of net asset yield in China's listed companies.
An empirical study on the relationship between the volatility of net assets yield and cash flow is studied in this paper. In this paper, the VAR model is established with the volatility of net asset returns and the cash flow as variables, and the structural analysis is carried out by means of Granger causality analysis and impulse response function analysis. There is a two-way Granger causality between the gold flow, that is, the two parties are each other's Granger reasons, and the volatility of the net asset returns is more affected by the impact of their own residual impact than the residual of the cash flow, and the response is more rapid when they are affected by themselves. At the same time, the impact of the cash flow on its own residual impact is greater than the volatility of the net assets. Secondly, the VAR model is set up with the net asset returns and cash flow as variables, and the structural analysis is carried out by variance decomposition. The conclusion shows that the ratio of volatility of net assets yield fluctuation is far greater than that caused by the residual of cash flow, and the change of the variance of cash flow is contributed by its own residuals. The ratio is also greater, and this contribution ratio will change appropriately when the impact arises, and will remain stable in the future.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F224;F832.51

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