本文選題：獨(dú)立成分分析 + 長(zhǎng)記憶性��； 參考：《浙江工商大學(xué)》2013年碩士論文

【摘要】：在世界經(jīng)濟(jì)一體化的背景下,我國(guó)金融市場(chǎng)與國(guó)際市場(chǎng)之間的聯(lián)系也越來(lái)越緊密,股票市場(chǎng)作為我國(guó)金融市場(chǎng)的重要組成部分越來(lái)越受到各方的關(guān)注。我國(guó)股票市場(chǎng)正處于不斷發(fā)展與完善的階段,各方參與者必須充分認(rèn)識(shí)到股票市場(chǎng)的風(fēng)險(xiǎn),同時(shí)需具有較強(qiáng)的風(fēng)險(xiǎn)防控意識(shí)。 資產(chǎn)的風(fēng)險(xiǎn)主要表現(xiàn)在它收益率的波動(dòng)上,在對(duì)資產(chǎn)進(jìn)行風(fēng)險(xiǎn)管理時(shí)往往需要借助對(duì)它們收益率波動(dòng)的相關(guān)分析來(lái)進(jìn)行。用于金融資產(chǎn)收益率波動(dòng)分析的模型主要有GARCH模型和SV模型。多元GARCH模型的提出及發(fā)展為多維資產(chǎn)收益率波動(dòng)的研究提供了一個(gè)很好的工具。本論文在對(duì)多元GARCH模型進(jìn)行簡(jiǎn)單綜述的基礎(chǔ)上將其引入到高頻金融資產(chǎn)收益率的實(shí)證研究中,運(yùn)用DCC-GARCH模型和基于獨(dú)立成分分析(ICA)的ICA-GARCH模型對(duì)白銀概念股中的豫光金鉛和銅陵有色等8個(gè)股票的五分鐘的收益率序列進(jìn)行了估計(jì)。實(shí)證結(jié)果表明這8個(gè)概念股之間存在波動(dòng)相關(guān)性,且這種相關(guān)性是隨著時(shí)間改變而改變的,DCC-GARCH模型和ICA-GARCH模型都能很好地對(duì)這種相關(guān)性進(jìn)行刻畫,而通過(guò)比較這兩個(gè)模型的殘差自相關(guān)性發(fā)現(xiàn)ICA-GARCH模型具有更好的擬合優(yōu)勢(shì),且運(yùn)行速度更快。 同時(shí),我們還將ICA引入到了多維資產(chǎn)的“已實(shí)現(xiàn)”協(xié)方差矩陣的研究中,構(gòu)建了具有長(zhǎng)記憶性的ICA-ARFIMA模型對(duì)多維資產(chǎn)高頻收益率序列進(jìn)行分析。通過(guò)ICA處理將多維資產(chǎn)收益率序列轉(zhuǎn)換為幾個(gè)相互獨(dú)立的成分,然后分別計(jì)算各獨(dú)立成分的“已實(shí)現(xiàn)”波動(dòng)率并進(jìn)行相關(guān)模型估計(jì),從而達(dá)到簡(jiǎn)化模型參數(shù)估計(jì)的目的。通過(guò)實(shí)證發(fā)現(xiàn)獨(dú)立“己實(shí)現(xiàn)”波動(dòng)率和對(duì)數(shù)獨(dú)立“已實(shí)現(xiàn)”波動(dòng)率都具有顯著的長(zhǎng)記憶性,ICA-ARFIMA模型能夠很好地對(duì)多維資產(chǎn)收益率的“已實(shí)現(xiàn)”協(xié)方差矩陣進(jìn)行估計(jì)。 此外,我們將ICA-ARFIMA模型估計(jì)得到的“已實(shí)現(xiàn)”波動(dòng)率引入到了風(fēng)險(xiǎn)價(jià)值VaR的計(jì)算中。實(shí)證結(jié)果表明ICA-ARFIMA模型得到的波動(dòng)率能夠很好地對(duì)資產(chǎn)收益率的風(fēng)險(xiǎn)進(jìn)行刻畫,比直接利用原收益率序列估計(jì)得到的VaR值有更高的估計(jì)精度。
[Abstract]:Under the background of world economic integration, the relationship between Chinese financial market and international market is more and more close. As an important part of Chinese financial market, stock market is paid more and more attention by all sides.The stock market of our country is in the stage of continuous development and perfection. All participants must fully realize the risk of the stock market and have a strong awareness of risk prevention and control at the same time.The risk of assets is mainly reflected in the volatility of their return rate. In the risk management of assets, it is often necessary to use the relevant analysis of the volatility of their return rate to carry on the risk management.GARCH model and SV model are the main models used to analyze the volatility of financial asset return.The development of multivariate GARCH model provides a good tool for the study of multi-dimensional asset return volatility.On the basis of a brief review of the multivariate GARCH model, this paper introduces it into the empirical study of the return on high-frequency financial assets.The DCC-GARCH model and the ICA-GARCH model based on independent component analysis (ICA) are used to estimate the five minute yield series of eight stocks including Yuguang gold lead and Tongling nonferrous stock in silver concept stock.The empirical results show that there is a volatility correlation among the eight concept stocks, and this correlation can be described well by both the DCC-GARCH model and the ICA-GARCH model, which change with time.By comparing the residual autocorrelation of the two models, it is found that the ICA-GARCH model has better fitting advantages and faster running speed.At the same time, we introduce ICA into the study of "realized" covariance matrix of multidimensional assets, and construct a long-memory ICA-ARFIMA model to analyze the high-frequency return series of multidimensional assets.The multi-dimensional asset return series is transformed into several independent components by ICA processing, and then the realized volatility of each independent component is calculated and the model estimation is carried out respectively, so as to simplify the parameter estimation of the model.It is found that both independent "self-realized" volatility and logarithmic independent "realized" volatility have significant long-memory properties. ICA-ARFIMA model can estimate the "realized" covariance matrix of multi-dimensional asset return rate.In addition, we introduce the "realized" volatility estimated by ICA-ARFIMA model into the calculation of VaR.The empirical results show that the volatility obtained by the ICA-ARFIMA model can well describe the risk of the return on assets, which is more accurate than the VaR value estimated by using the original return series directly.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李紅權(quán),馬超群;股市收益率與波動(dòng)性長(zhǎng)期記憶效應(yīng)的實(shí)證研究[J];財(cái)經(jīng)研究;2005年08期

2 王春峰;張蕊;房振明;李曄;;高頻數(shù)據(jù)下投資組合風(fēng)險(xiǎn)預(yù)測(cè)模型比較[J];系統(tǒng)工程;2007年03期

3 樊蓉;周宏;;基于中國(guó)股市波動(dòng)率的多元模型比較與分析[J];貴州大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年06期

4 劉曉彬;李瑜;羅洎;;股指期貨與現(xiàn)貨市場(chǎng)間波動(dòng)溢出效應(yīng)探究[J];宏觀經(jīng)濟(jì)研究;2012年07期

5 魯旭;趙迎迎;;滬深港股市動(dòng)態(tài)聯(lián)動(dòng)性研究——基于三元VAR-GJR-GARCH-DCC的新證據(jù)[J];經(jīng)濟(jì)評(píng)論;2012年01期

6 黃后川,陳浪南;中國(guó)股票市場(chǎng)波動(dòng)率的高頻估計(jì)與特性分析[J];經(jīng)濟(jì)研究;2003年02期

7 李文君;尹康;;多元GARCH模型研究述評(píng)[J];數(shù)量經(jīng)濟(jì)技術(shù)經(jīng)濟(jì)研究;2009年10期

8 王明進(jìn);;多元波動(dòng)率模型的一些新進(jìn)展[J];數(shù)理統(tǒng)計(jì)與管理;2010年02期

9 王雪標(biāo);周維利;范慶珍;;我國(guó)原油價(jià)格與外國(guó)原油價(jià)格的波動(dòng)溢出效應(yīng)——基于DCC-MGARCH模型分析[J];數(shù)理統(tǒng)計(jì)與管理;2012年04期

10 徐正國(guó);張世英;;多維高頻數(shù)據(jù)的“已實(shí)現(xiàn)”波動(dòng)建模研究[J];系統(tǒng)工程學(xué)報(bào);2006年01期

，

本文編號(hào)：1759472