本文選題：隱馬爾可夫模型 + VaR風(fēng)險(xiǎn)價(jià)值��； 參考：《合肥工業(yè)大學(xué)》2013年碩士論文

【摘要】：新世紀(jì)特別是2008年世界金融危機(jī)爆發(fā)以來，國際、國內(nèi)金融市場都發(fā)生了深刻的變革，金融市場風(fēng)險(xiǎn)明顯增大，分析金融波動(dòng)特征和度量金融市場風(fēng)險(xiǎn)對(duì)投資與監(jiān)管都具有重要的意義。VaR(Value-at-Risk)方法以其高度的綜合概括能力，為投資者提供了一個(gè)直觀、全面的風(fēng)險(xiǎn)量化指標(biāo)，，目前已成為世界主流的風(fēng)險(xiǎn)測度方法。本文主要針對(duì)金融數(shù)據(jù)所呈現(xiàn)的尖峰厚尾、波動(dòng)持續(xù)、結(jié)構(gòu)轉(zhuǎn)變等特征，探討了金融市場風(fēng)險(xiǎn)度量的VaR模型的改進(jìn)方法與應(yīng)用。 首先對(duì)本文的研究背景及意義、VaR模型在國內(nèi)外的研究概況進(jìn)行概述，并提出了本文的主要內(nèi)容。然后介紹了隱馬爾可夫模型的基本概念與算法，指出其在異常狀態(tài)識(shí)別上的應(yīng)用。緊接著闡述了VaR的基本原理與計(jì)算方法，指出常用的ARCH模型族估算波動(dòng)率方法的不足，在此基礎(chǔ)上提出了本文模型：HMM-ARMA-GARCH模型，用隱馬爾可夫模型的狀態(tài)變量來描述金融市場的正常波動(dòng)狀態(tài)與異常波動(dòng)狀態(tài)，同時(shí)不可觀測的狀態(tài)變量能夠?qū)Σ▌?dòng)的集聚現(xiàn)象給出很好的解釋。對(duì)不同的狀態(tài)數(shù)據(jù)分別建立ARMA-GARCH模型來估算波動(dòng)率，同時(shí)給出VaR的具體計(jì)算方法。最后對(duì)上證企債指數(shù)進(jìn)行實(shí)證分析，采用Kupiec失敗頻率檢驗(yàn)法對(duì)VaR的準(zhǔn)確性進(jìn)行檢驗(yàn)，并與傳統(tǒng)的ARMA-GARCH模型的估算效果進(jìn)行比較。實(shí)證結(jié)果表明基于本文模型的VaR計(jì)算方法具有較好的估計(jì)效果，且能夠有效的降低GARCH模型高估波動(dòng)持續(xù)性的現(xiàn)象。
[Abstract]:In the new century, especially since the outbreak of the world financial crisis in 2008, profound changes have taken place in the international and domestic financial markets, and the risks in the financial markets have obviously increased. It is important to analyze the characteristics of financial volatility and to measure financial market risk for investment and supervision. The VaR (Value-at-Risk) method, with its high comprehensive generalization ability, provides an intuitive and comprehensive risk quantification index for investors. At present, it has become the mainstream risk measurement method in the world. Aiming at the characteristics of financial data, such as peak and thick tail, persistent fluctuation and structural transformation, this paper discusses the improved method and application of VaR model for financial market risk measurement. Firstly, the research background and significance of this paper are summarized, and the main contents of this paper are put forward. Then the basic concept and algorithm of hidden Markov model are introduced, and its application in abnormal state recognition is pointed out. Then, the basic principle and calculation method of VaR are expounded, and the shortcomings of the common arch model family are pointed out. On this basis, the model of this paper is proposed, which is the: HMM-ARMA-GARCH model. The state variables of hidden Markov model are used to describe the normal volatility and abnormal volatility in the financial market. At the same time, the unobservable state variables can give a good explanation for the agglomeration of volatility. The ARMA-GARCH model is established for different state data to estimate volatility, and the specific calculation method of VaR is given. Finally, the empirical analysis on the debt index of Shanghai stock market is carried out, and the accuracy of VaR is tested by using the Kupiec failure frequency test method, and compared with the estimation effect of the traditional ARMA-GARCH model. The empirical results show that the VaR calculation method based on this model has better estimation effect and can effectively reduce the phenomenon of overestimating volatility persistence in GARCH model.
【學(xué)位授予單位】：合肥工業(yè)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;O211.62

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