本文選題：股指波動(dòng)性 + GARCH模型��； 參考：《江西財(cái)經(jīng)大學(xué)》2012年碩士論文

【摘要】：目前,我國(guó)股票市場(chǎng)起步晚,尚屬于新興股票市場(chǎng),與發(fā)達(dá)國(guó)家成熟的股票市場(chǎng)相比,在影響股市波動(dòng)性方面存在著許多不同的地方,因而其所表現(xiàn)出來的特有性質(zhì)對(duì)研究國(guó)內(nèi)股票市場(chǎng)的波動(dòng)性具有重要的意義。傳統(tǒng)GARCH模型對(duì)參數(shù)估計(jì)的方法主要是基于極大似然理論的基礎(chǔ)上,對(duì)模型進(jìn)行最優(yōu)化,但是由于GARCH模型對(duì)參數(shù)有一定的約束條件,故難以對(duì)模型的相關(guān)參數(shù)進(jìn)行進(jìn)一步的統(tǒng)計(jì)分析,同時(shí)在估計(jì)參數(shù)時(shí)往往存在偏離,難以達(dá)到實(shí)現(xiàn)最優(yōu)化的目的。 為了能更好地解決以上問題,本文嘗試應(yīng)用基于貝葉斯理論的馬爾科夫蒙特卡羅(MCMC)方法來構(gòu)建GARCH模型,以更好地描述上證股指收益率時(shí)間序列的波動(dòng)性特征。本文從不同的角度系統(tǒng)地研究GARCH類模型的估計(jì)方法,以探索和研究貝葉斯統(tǒng)計(jì)的新領(lǐng)域,并針對(duì)性地對(duì)模型進(jìn)行擴(kuò)展。具體來說,本文運(yùn)用MCMC方法對(duì)基于正態(tài)分布的GARCH(1,1)模型的參數(shù)進(jìn)行估計(jì),通過構(gòu)造一個(gè)收斂于待估計(jì)參數(shù)的后驗(yàn)分布,從而繞開了GARCH模型中對(duì)參數(shù)的約束,解決了一般優(yōu)化方法難以運(yùn)用的困難。 綜合對(duì)比分析基于BHHH方法的GARCH模型和基于MCMC方法的GARCH模型在上證股指波動(dòng)性的實(shí)證研究,試圖更好地利用新模型來描述和解釋股市的波動(dòng)性特征,這對(duì)推動(dòng)基于貝葉斯理論的MCMC方法在金融實(shí)踐中的應(yīng)用提供一定的參考價(jià)值。論文理論與實(shí)際相結(jié)合,既探討了模型的理論研究,也進(jìn)行了實(shí)證分析。在最后本文依據(jù)研究結(jié)果進(jìn)行總結(jié),并對(duì)今后的研究工作進(jìn)行了展望。
[Abstract]:At present, the stock market of our country starts late and still belongs to the emerging stock market. Compared with the mature stock market in developed countries, there are many differences in influencing the volatility of stock market. Therefore, it is of great significance to study the volatility of domestic stock market. The traditional method of parameter estimation in GARCH model is based on the maximum likelihood theory to optimize the model, but the GARCH model has some constraints on the parameters. Therefore, it is difficult to make further statistical analysis on the related parameters of the model, and at the same time, there are often deviations in estimating the parameters, so it is difficult to achieve the goal of optimization. In order to solve the above problems better, this paper attempts to use the Markov Monte Carlo (MCMC) method based on Bayesian theory to construct the GARCH model to better describe the volatility characteristics of the time series of Shanghai stock index yield. In this paper, we systematically study the estimation methods of GARCH model from different angles, to explore and study the new field of Bayesian statistics, and extend the model. Specifically, this paper uses MCMC method to estimate the parameters of GARCH (1K1) model based on normal distribution. By constructing a posterior distribution which converges to the parameters to be estimated, the constraints of parameters in GARCH model are circumvented. The difficulty of general optimization method is solved. By comparing and analyzing the GARCH model based on BHHH method and the GARCH model based on MCMC method in Shanghai stock index volatility, this paper tries to use the new model to describe and explain the volatility characteristics of the stock market. It provides some reference value for the application of MCMC method based on Bayesian theory in financial practice. Combining theory with practice, this paper not only discusses the theoretical research of the model, but also makes an empirical analysis. Finally, according to the results of the research, the future research work is prospected.
【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51

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本文編號(hào)：2088623