發(fā)布時間：2018-07-06 15:21

  本文選題：金融風(fēng)險 + GARCH模型　； 參考：《復(fù)旦大學(xué)》2014年碩士論文

【摘要】：風(fēng)險是指未來收益的不確定性,金融風(fēng)險是指金融變量的變動所引起的資產(chǎn)組合未來收益的不確定性。通常我們關(guān)注的是風(fēng)險可能帶來的損失,因此可以將風(fēng)險的概念表述為“由于結(jié)果的不確定性而帶來損失的可能性”。隨著經(jīng)濟全球化以及金融市場一體化,金融市場變得愈加復(fù)雜。如何有效地管理金融風(fēng)險是國內(nèi)外金融理論界和實物界關(guān)注的重中之重。金融市場風(fēng)險度量是度量由于市場因子的變化而致使金融資產(chǎn)產(chǎn)生的損失。金融風(fēng)險度量是風(fēng)險管理的核心部分。目前,金融市場風(fēng)險度量的主流方法主要有：均值-方差分析、靈敏度方法、波動性方法、VaR方法、壓力試驗以及極值理論。研究表明,金融資產(chǎn)收益率的時間序列具有尖峰后尾的特性,其并不服從正態(tài)分布。為了正確估計金融風(fēng)險,本文利用GARCH族模型,選取2004年1月2日至2013年12月31日的上證指數(shù)的日收盤價數(shù)據(jù),比較分析了假設(shè)收益率序列服從正態(tài)分布、t分布以及GED分布下的GARCH基于族模型的CVaR以及VAR的計算方法,并且和傳統(tǒng)的VaR方法進行比較,得到了以下結(jié)論：上海股票市場收益率具有尖峰后尾的特點,并且具有明顯的GARCH效應(yīng)。收益率序列基于GED分布下的VaR以及CVaR的計算結(jié)果要好于基于正態(tài)分布以及t分布的計算結(jié)果。這是由于正態(tài)分布的尾部較薄,隨著置信水平的增加,基于正態(tài)分布的風(fēng)險度量容易低估風(fēng)險。而t分布的尾部太厚,會造成高估風(fēng)險的后果。CVaR可以在VaR失效時比較準(zhǔn)確的度量出極端損失,相對于VaR而言,CVaR是一種能覆蓋更大范圍尾部風(fēng)險的風(fēng)險測度指標(biāo)。當(dāng)金融資產(chǎn)收益率相關(guān)性不顯著時,VaR及CVaR同時滿足次可加性,但是CVaR使得次可加性的效果更為明顯,可以更好的體現(xiàn)出風(fēng)險分散化效應(yīng)。本文的創(chuàng)新之處是將基于GARCH族模型下的CVaR以及VaR模型應(yīng)用到我國金融市場的風(fēng)險度量中(本文利用上證指數(shù)),輔以定性分析,對我國金融市場風(fēng)險進行度量和研究。
[Abstract]:Risk refers to the uncertainty of future income, and financial risk is the uncertainty of future income of portfolio caused by the change of financial variables. Usually we focus on the possible loss of risk, so we can express the concept of risk as "the possibility of loss due to uncertainty of the result". With the economic globalization and the integration of financial markets, financial markets have become more and more complex. How to manage financial risk effectively is the most important concern of domestic and foreign financial theorists and physical circles. Financial market risk measurement is to measure the loss of financial assets caused by the change of market factors. Financial risk measurement is the core part of risk management. At present, the main methods of financial market risk measurement are: mean-variance analysis, sensitivity method, volatility method and VaR method, pressure test and extreme value theory. The results show that the time series of financial asset return has the characteristics of peak and tail, and it is not obedient to normal distribution. In order to estimate financial risk correctly, this paper selects the daily closing price data of Shanghai Stock Exchange Index from January 2, 2004 to December 31, 2013 by using the GARCH family model. The calculation methods of CVaR and VAR based on family model for GARCH under normal distribution and GED distribution are compared and compared with the traditional VaR method. The following conclusions are obtained: the yield of Shanghai stock market has the characteristics of peak and tail and has obvious GARCH effect. The results of VaR and Cvar based on GED distribution are better than those based on normal distribution and t distribution. This is because the tail of the normal distribution is thin. With the increase of the confidence level, the risk measurement based on the normal distribution is easy to underestimate the risk. The tail of t distribution is too thick, which will result in overestimated risk. CVaR can accurately measure extreme loss when VaR fails. Compared with VaR, CVaR is a kind of risk measure index which can cover a larger range of tail risk. When the correlation of financial asset returns is not significant, VaR and Cvar satisfy the subadditivity simultaneously, but Cvar makes the effect of sub-additivity more obvious, which can better reflect the risk diversification effect. The innovation of this paper is to apply CVaR and VaR model based on GARCH family model to the risk measurement of financial market in our country (using Shanghai Stock Exchange Index) and to measure and study the risk of financial market in our country with qualitative analysis.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.5

【參考文獻】

相關(guān)碩士學(xué)位論文 前1條

1 吳劍;VaR計算方法的改進及其實證分析[D];上海交通大學(xué);2011年

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本文編號：2103223