【摘要】：從現(xiàn)代金融的數(shù)量化研究進(jìn)程中可以發(fā)現(xiàn)，波動(dòng)性始終是金融理論的核心問題，因此，如何對(duì)市場的波動(dòng)性進(jìn)行準(zhǔn)確的度量和預(yù)測，，成為理論界和實(shí)務(wù)界所關(guān)注的焦點(diǎn)�，F(xiàn)代經(jīng)濟(jì)計(jì)量學(xué)方法論的發(fā)展，為波動(dòng)性的建模分析提供了堅(jiān)實(shí)的方法論基礎(chǔ)，隨著研究的深入，在眾多專家和學(xué)者的努力下，波動(dòng)率模型的研究取得了顯著進(jìn)展。近幾年發(fā)展起來的基于隱馬爾科夫模型(HMM)預(yù)測波動(dòng)率的方法尤其有效。 本文采用的隱馬爾科夫模型由兩部分組成，馬爾科夫鏈和一般隨機(jī)過程，并且可以用狀態(tài)空間模型的形式來表示。其中，馬爾科夫鏈用來描述不可觀測的狀態(tài)，在狀態(tài)空間模型中用狀態(tài)方程表示；一般隨機(jī)過程用來描述觀察值與不可觀測的狀態(tài)之間的關(guān)系，本文中將收益率作為觀察值，用狀態(tài)空間模型中的量測方程來刻畫。一般來講，隱馬爾科夫模型的參數(shù)會(huì)隨著馬爾科夫鏈狀態(tài)的增加而迅速增加[1]，這樣當(dāng)狀態(tài)數(shù)較多時(shí)，參數(shù)估計(jì)就成為非常復(fù)雜的問題，為此，本文使用一種特殊的參數(shù)化過程，即能很好的反應(yīng)波動(dòng)率的市場特征，又使參數(shù)個(gè)數(shù)與馬爾科夫鏈狀態(tài)數(shù)無關(guān)。 本文應(yīng)用隱馬爾科夫模型預(yù)測波動(dòng)率時(shí)，波動(dòng)率由不可觀測的馬爾科夫過程驅(qū)動(dòng)，觀察值為收益率。通過歷史行情數(shù)據(jù)得到參數(shù)估計(jì)，進(jìn)而利用參數(shù)和當(dāng)前的觀察值，預(yù)測未來的波動(dòng)率。為了驗(yàn)證模型的有效性，我們將預(yù)測結(jié)果與實(shí)際情況作比較，并引入GARCH模型和T-GARCH模型作為比較模型。實(shí)證結(jié)果表明了隱馬爾科夫模型預(yù)測的有效性。
[Abstract]:From the quantitative research process of modern finance, it can be found that volatility is always the core problem of financial theory. Therefore, how to accurately measure and predict the volatility of the market has become the focus of attention of the theoretical and practical circles. The development of modern econometric methodology provides a solid methodological basis for the modeling and analysis of volatility. With the development of research, with the efforts of many experts and scholars, the research of volatility model has made remarkable progress. The method based on Hidden Markov Model (HMM) developed in recent years to predict volatility is particularly effective. The hidden Markov model in this paper is composed of two parts, Markov chain and general stochastic process, and can be expressed in the form of state space model. The Markov chain is used to describe the unobservable state, and the state equation is used in the state space model, and the general stochastic process is used to describe the relationship between the observed value and the unobservable state. In this paper, the rate of return is regarded as the observed value. The measurement equation in the state space model is used to describe it. In general, the parameters of Hidden Markov Model will increase rapidly with the increase of Markov chain state, so when the number of states is more, parameter estimation becomes a very complex problem. Therefore, a special parameterization process is used in this paper. Not only the market characteristics of volatility can be well reflected, but also the number of parameters is independent of the number of Markov chain states. In this paper, when using hidden Markov model to predict volatility, volatility is driven by an unobservable Markov process, and the observed value is a return rate. The parameters are estimated by historical market data, and then the future volatility is predicted by using the parameters and current observation values. In order to verify the validity of the model, we compare the prediction results with the actual situation, and introduce GARCH model and T-GARCH model as comparison models. The empirical results show that the hidden Markov model is effective.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F830

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 孫璐;郁燁;顧文鈞;;基于PCA和HMM的汽車保有量預(yù)測方法[J];交通運(yùn)輸工程學(xué)報(bào);2013年02期

相關(guān)碩士學(xué)位論文 前2條

1 戴曉婧;光纖通信接入技術(shù)的研究與應(yīng)用[D];北京化工大學(xué);2013年

2 許輝;維吾爾語聯(lián)機(jī)手寫體技術(shù)及其實(shí)現(xiàn)研究[D];新疆大學(xué);2013年

本文編號(hào)：2159846