【摘要】：金融市場上，，風(fēng)險管理是久談不衰的話題，風(fēng)險管理是保障金融機(jī)構(gòu)良性運(yùn)行的重要內(nèi)容，金融風(fēng)險是金融產(chǎn)品市場價格波動而導(dǎo)致未來可能發(fā)生的損失，在經(jīng)濟(jì)全球化和金融自由化不斷深化的背景下，金融風(fēng)險的表現(xiàn)形式也層出不窮，表現(xiàn)出多樣化、復(fù)雜化等特點(diǎn)。伴隨著2007年美國“次貸危機(jī)”的爆發(fā)，這次危機(jī)造成極大的危害性，更加引起人們對金融風(fēng)險的關(guān)注。在面對紛繁的金融產(chǎn)品及金融衍生品的風(fēng)險管理時，很多學(xué)者逐步將金融工程等一系列專業(yè)技術(shù)引入到風(fēng)險管理活動中，從更深層次探討風(fēng)險識別和控制。 面對金融市場的不穩(wěn)定性，如何有效的規(guī)避風(fēng)險，影響風(fēng)險的因素有哪些是投資者需要關(guān)注的。在眾多投資模型中，投資組合能有效的降低風(fēng)險，還需要研究組合中各資產(chǎn)的收益與風(fēng)險，以及組合資產(chǎn)之間的相關(guān)性等因素。所以要想有效的控制風(fēng)險，必須全面了解影響風(fēng)險的各種因素，分析哪些指標(biāo)能顯著地影響風(fēng)險的大小以及影響方向。 在前人研究的基礎(chǔ)上，以上市公司財務(wù)指標(biāo)與風(fēng)險的關(guān)聯(lián)性為依據(jù)，進(jìn)行研究。在進(jìn)行投資風(fēng)險的計量時，采用VaR方法。對股票收益率的分布進(jìn)行研究時，發(fā)現(xiàn)股票收益率并不嚴(yán)格服從正態(tài)分布，所以我們對股票收益率分布采用了更為合理的分布，在進(jìn)行資產(chǎn)組合風(fēng)險計量時，采用Copula模型，并利用Monte Carlo方法進(jìn)行多次模擬，其結(jié)果更加科學(xué)合理。 為了能夠較為全面的反映公司的經(jīng)營和財務(wù)狀況，我們分別從償債及資本結(jié)構(gòu)、盈利能力和營運(yùn)及成長能力三個方面選取了多個財務(wù)指標(biāo)，但是為了消除各指標(biāo)之間的相關(guān)性和多重共線性，利用主成分分析方法對指標(biāo)進(jìn)行降維，最后得到11個主要的，能夠全面反映公司狀況的財務(wù)指標(biāo)。 在進(jìn)行回歸分析時，由于我們選取樣本為50個樣本公司11個財務(wù)指標(biāo)的16年數(shù)據(jù)，表現(xiàn)為三維的截面數(shù)據(jù)，所以需要利用面板模型進(jìn)行分析，最后從統(tǒng)計結(jié)果得出，選取的11個財務(wù)指標(biāo)中，有5個能顯著的影響風(fēng)險。然后，我們給投資者、上市公司和政府監(jiān)管部門關(guān)于規(guī)避風(fēng)險的建議。
[Abstract]:In the financial market, risk management is a topic that has long been discussed. Risk management is an important part of ensuring the benign operation of financial institutions. Financial risk is a loss that may occur in the future as a result of market price fluctuations of financial products. Under the background of economic globalization and financial liberalization, the forms of financial risk emerge in endlessly, showing the characteristics of diversification and complexity. With the outbreak of the subprime mortgage crisis in 2007, the crisis caused great harm and caused more attention to financial risks. In the face of the numerous financial products and financial derivatives risk management, many scholars gradually introduce a series of professional technology such as financial engineering into risk management activities, from a deeper level to explore risk identification and control. Faced with the instability of financial markets, how to effectively avoid risk, which factors affect risk is what investors need to pay attention to. In many investment models, portfolio can effectively reduce the risk, but also need to study the income and risk of each asset in the portfolio, as well as the correlation between portfolio assets and other factors. Therefore, in order to effectively control risk, we must understand all kinds of factors that affect risk, and analyze which indicators can significantly affect the size and direction of risk. On the basis of previous studies, the relationship between financial indicators and risks of listed companies is studied. In the measurement of investment risk, the VaR method is adopted. When we study the distribution of stock return, we find that the stock return is not strictly obeyed to normal distribution, so we adopt a more reasonable distribution for the distribution of stock return. When we measure the risk of portfolio, we adopt Copula model. The Monte Carlo method is used to simulate many times, and the results are more scientific and reasonable. In order to fully reflect the company's operating and financial situation, we have selected a number of financial indicators from three aspects: debt service and capital structure, profitability, operation and growth ability. However, in order to eliminate the correlation and multiple collinearity among the indicators, the principal component analysis (PCA) method is used to reduce the dimension of the indicators. Finally, 11 main financial indicators are obtained, which can reflect the overall situation of the company. In regression analysis, because we select 16 years' data of 11 financial indicators of 50 sample companies, so we need to make use of panel model to analyze, finally, from the statistical results, Of the 11 selected financial indicators, 5 can significantly affect the risk. Then we give investors, listed companies and government regulators advice on risk aversion.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F275;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 華仁海,丁秀玲;我國股票市場收益、交易量、波動性動態(tài)關(guān)系的實證分析[J];財貿(mào)經(jīng)濟(jì);2003年12期

2 林美艷,薛宏剛,趙鳳群;上海證券市場收益率的正態(tài)性檢驗[J];紡織高�；A(chǔ)科學(xué)學(xué)報;2003年03期

3 張波;陳睿君;路璐;;粒子群算法在投資組合中的應(yīng)用[J];系統(tǒng)工程;2007年08期

4 楊湘豫;夏宇;;基于Copula方法的開放式基金投資組合的VaR研究[J];系統(tǒng)工程;2008年12期

5 李學(xué)軍;;基于主成分分析的上市公司財務(wù)危機(jī)預(yù)警模型研究[J];工業(yè)技術(shù)經(jīng)濟(jì);2011年07期

6 王培;王焱鑫;;面板數(shù)據(jù)的主成分分析及其應(yīng)用[J];貴州大學(xué)學(xué)報(自然科學(xué)版);2009年01期

7 陳收,楊寬,廖懿,雷輝;證券市場中股票成交量對投資組合優(yōu)化的影響[J];管理科學(xué)學(xué)報;2002年05期

8 李漢東,張世英;存在方差持續(xù)性的資本資產(chǎn)定價模型分析[J];管理科學(xué)學(xué)報;2003年01期

9 張宗新;朱偉驊;;中國證券市場系統(tǒng)性風(fēng)險結(jié)構(gòu)的實證分析[J];經(jīng)濟(jì)理論與經(jīng)濟(jì)管理;2005年12期

10 胡志娟;薛梅;;相關(guān)系數(shù)影響下投資組合的風(fēng)險研究[J];經(jīng)濟(jì)論壇;2010年10期

相關(guān)博士學(xué)位論文 前2條

1 謝磊;我國股票投資者投資風(fēng)險管理研究[D];中南大學(xué);2006年

2 戰(zhàn)雪麗;基于Copula理論的多金融資產(chǎn)定價與風(fēng)險測度[D];天津大學(xué);2007年

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