VaR方法在股票風(fēng)險管理中的實證研究
本文選題:VaR + 股票 ; 參考:《安徽農(nóng)業(yè)大學(xué)》2017年碩士論文
【摘要】:近年來,中國金融市場越來越開放,隨之而來的金融風(fēng)險問題也日漸突出。當(dāng)今各大金融機構(gòu)管理的基礎(chǔ)和核心就是如何處理好風(fēng)險問題。而VaR方法具備多種優(yōu)勢,是可以度量金融風(fēng)險的一種比較流行的方法。VaR方法出現(xiàn)和運用越來越頻繁,它逐漸變成了國際風(fēng)險管理的行業(yè)標(biāo)準。中國的股票市場僅有20多年的歷程。雖然我們收獲到許多有益經(jīng)驗,但是仍然有一些不規(guī)范之處。這也使得中國的股票市場的震動頻率比英法等國要高很多。鑒于我國股票市場的現(xiàn)狀,我們必須重視風(fēng)險管理工作,有必要將VaR方法結(jié)合我國國情,更好的應(yīng)用在股票風(fēng)險管理中。本文詳細地介紹了股票的概念、發(fā)展歷程和股票風(fēng)險管理等相關(guān)知識以及VaR的定義,模型系統(tǒng)和計算方法等等。本文搜集了2010—2016年最新的上證指數(shù)和深證成指收盤價數(shù)據(jù),將理論結(jié)合實證,應(yīng)用大量的金融學(xué)、計量經(jīng)濟學(xué)和統(tǒng)計學(xué)方面的知識進行實證研究。本文共分為五大部分。第一部分是有關(guān)股票和股票風(fēng)險等相關(guān)理論。股票是最具爭議的理財工具之一,具有風(fēng)險性特征。第二部分詳細介紹了VaR的定義、模型系統(tǒng)和三種計算方法。簡要介紹了方差—協(xié)方差法、歷史模擬法以及蒙特卡洛模擬法這三種計算方法[1]。第三部分是關(guān)于VaR方法在股票風(fēng)險管理中的實證研究,主要以2010年1月4日至2016年12月30日這1700個上證指數(shù)和深證成指的每日收盤價格為原始數(shù)據(jù)進行實證分析。比較了上證指數(shù)和深證成指分年度的對數(shù)收益率基本統(tǒng)計特征。運用eviews8.0軟件進行平穩(wěn)性檢驗、正態(tài)性檢驗、自相關(guān)性檢驗和異方差性檢驗等各項模型檢驗。第四部分是上證指數(shù)和深證成指的VaR計算結(jié)果及分析。分別建立了上證指數(shù)和深證成指對數(shù)收益率的GARCH(1,1)模型,求出兩者的VaR值。最后是關(guān)于VaR方法在股票風(fēng)險管理中的研究結(jié)論及啟示。這部分總結(jié)了本文的研究結(jié)論,得到自己的個人啟示。
[Abstract]:In recent years, China's financial market is becoming more and more open, followed by financial risks are also increasingly prominent. How to deal with the risk is the foundation and core of the management of the major financial institutions today. VaR method has many advantages, and it is a popular method to measure financial risk. VaR method appears and uses more and more frequently, and it has gradually become the industry standard of international risk management. China's stock market has only had a history of more than 20 years. Although we have gained many useful experiences, there are still some irregularities. This also makes China's stock market vibrate much more frequently than countries such as Britain and France. In view of the present situation of our country's stock market, we must pay attention to the risk management work, it is necessary to combine the VaR method with the national conditions of our country, and apply it to stock risk management better. This paper introduces the concept, development history and stock risk management of stock in detail, as well as the definition, model system and calculation method of VaR. This paper collects the latest closing price data of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index from 2010 to 2016, and makes empirical research by applying a large amount of knowledge in finance, econometrics and statistics. This paper is divided into five parts. The first part is about stock and stock risk and other related theories. Stock is one of the most controversial financial tools, with risk characteristics. In the second part, the definition, model system and three calculation methods of VaR are introduced in detail. This paper briefly introduces three calculation methods: variance-covariance method, historical simulation method and Monte Carlo simulation method [1]. The third part is an empirical study on the VaR method in stock risk management, mainly based on the 1700 Shanghai Stock Exchange Index and the daily closing price of Shenzhen Composite Index from January 4, 2010 to December 30, 2016. This paper compares the basic statistical characteristics of logarithmic rate of return between Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index. Eviews8.0 software is used to test the stability, normality, autocorrelation and heteroscedasticity. The fourth part is the VaR calculation results and analysis of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index. The model of logarithmic return rate of Shanghai stock index and Shenzhen stock index is established, and the VaR value of the two indexes is calculated. Finally, the conclusion and enlightenment of VaR method in stock risk management are discussed. This part summarizes the conclusions of this paper, and gets its own personal inspiration.
【學(xué)位授予單位】:安徽農(nóng)業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:F832.51;F224
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