發(fā)布時(shí)間：2018-04-16 07:46

  本文選題：VaR方法 + 正態(tài)分布　； 參考：《遼寧大學(xué)》2013年碩士論文

【摘要】：證券市場風(fēng)險(xiǎn)的度量，即對(duì)證券市場風(fēng)險(xiǎn)進(jìn)行測(cè)量，是證券市場風(fēng)險(xiǎn)管理的基礎(chǔ)。需要構(gòu)建合適的模型使用恰當(dāng)?shù)姆椒�，而這一方面的研究也是當(dāng)前金融研究領(lǐng)域的一個(gè)熱門話題。證券市場風(fēng)險(xiǎn)度量的模型和估計(jì)方法是多種多樣的，其中使用的最為廣泛的是VaR（Value-at-Risk）方法。VaR是在正常的市場環(huán)境下，在某一特定的時(shí)間區(qū)間和置信度水平，對(duì)預(yù)期的最大損失進(jìn)行測(cè)量的一種方法。VaR方法適用于復(fù)雜的投資組合，說明該投資組合的杠桿效應(yīng)和分散效果。首先它可以給證券持有者提供風(fēng)險(xiǎn)量化指標(biāo)，指導(dǎo)內(nèi)部決策的制定；其次在進(jìn)行投資決策時(shí)，VaR還可以對(duì)收益和預(yù)期風(fēng)險(xiǎn)進(jìn)行權(quán)衡。VaR方法提供了一種關(guān)于市場風(fēng)險(xiǎn)的綜合性度量，是建立在可靠的科學(xué)基礎(chǔ)之上的。 在理論方面，本文對(duì)VaR方法做了較詳盡的介紹。先對(duì)VaR的計(jì)算方法在不同層面作了詳細(xì)的介紹，其中包括在正態(tài)分布、t分布以及GED分布下的VaR計(jì)算；后又詳細(xì)介紹了各種方差預(yù)測(cè)模型，，本文在前人研究的基礎(chǔ)上，綜合了所有模型進(jìn)行介紹；之后的第三章，對(duì)各種計(jì)算方法和方差預(yù)測(cè)模型分別進(jìn)行了較全面的理論比較，其中還簡單介紹了另外兩種常用的風(fēng)險(xiǎn)度量方法，并與VaR方法作了簡單的比較。在實(shí)證檢驗(yàn)方面，我們以上證綜指為樣本數(shù)據(jù)，把不同分布下的其中八種最有效的模型應(yīng)用到實(shí)證中，通過數(shù)據(jù)輸出結(jié)果的比較，在24中模型中通過分析參數(shù)估計(jì)，進(jìn)行VaR計(jì)算結(jié)果檢驗(yàn)。實(shí)證的結(jié)果顯示：與正態(tài)分布與t分布相比，GED分布能夠更好的刻畫上證綜指的波動(dòng)性，且在GED分布下，各種方差預(yù)測(cè)模型中PARCH模型得出的檢驗(yàn)值結(jié)果最好。
[Abstract]:The measurement of securities market risk, that is, the measurement of securities market risk, is the basis of securities market risk management.It is necessary to construct the appropriate model and use the appropriate method, which is also a hot topic in the field of financial research.There are a variety of models and estimation methods for risk measurement in securities market. The most widely used method is VaRN Value-at-Risk.VaR is in a normal market environment, in a specific time interval and confidence level.The method of measuring the expected maximum loss. VaR method is suitable for complex portfolio, which shows the leverage effect and dispersion effect of the portfolio.First, it can provide securities holders with quantitative risk indicators to guide the making of internal decisions; secondly, VaR can also weigh the return and expected risks to provide a comprehensive measure of market risk.Is based on sound science.In theory, the VaR method is introduced in detail.Firstly, the calculation methods of VaR are introduced in detail at different levels, including the calculation of VaR under normal distribution t distribution and GED distribution, and then various variance prediction models are introduced in detail.After the introduction of all the models, in the third chapter, the author makes a comprehensive theoretical comparison of various calculation methods and variance prediction models, and briefly introduces the other two commonly used risk measurement methods.A simple comparison is made with the VaR method.In the empirical test, we take the Shanghai Composite Index as the sample data, and apply eight of the most effective models under different distribution to the empirical results. Through the comparison of the data output results, we analyze the parameter estimation in the 24 model.The results of VaR calculation were tested.The empirical results show that: compared with normal distribution and t-distribution, PARCH distribution can better describe the volatility of Shanghai Composite Index, and under the GED distribution, PARCH model has the best test results.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F830.91;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 朱小斌;時(shí)間序列計(jì)量經(jīng)濟(jì)學(xué)(下)——協(xié)整和自回歸條件異方差模型[J];外國經(jīng)濟(jì)與管理;2003年12期

2 張世英,柯珂;ARCH模型體系[J];系統(tǒng)工程學(xué)報(bào);2002年03期

3 詹原瑞;市場風(fēng)險(xiǎn)的量度:VaR的計(jì)算與應(yīng)用[J];系統(tǒng)工程理論與實(shí)踐;1999年12期

4 吳長鳳;利用回歸-GARCH模型對(duì)我國滬深股市的分析[J];預(yù)測(cè);1999年04期

本文編號(hào)：1757914