本文選題：獨立成分分析 + 長記憶性��； 參考：《浙江工商大學》2013年碩士論文

【摘要】：在世界經(jīng)濟一體化的背景下,我國金融市場與國際市場之間的聯(lián)系也越來越緊密,股票市場作為我國金融市場的重要組成部分越來越受到各方的關注。我國股票市場正處于不斷發(fā)展與完善的階段,各方參與者必須充分認識到股票市場的風險,同時需具有較強的風險防控意識。 資產(chǎn)的風險主要表現(xiàn)在它收益率的波動上,在對資產(chǎn)進行風險管理時往往需要借助對它們收益率波動的相關分析來進行。用于金融資產(chǎn)收益率波動分析的模型主要有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)收益率波動的研究提供了一個很好的工具。本論文在對多元GARCH模型進行簡單綜述的基礎上將其引入到高頻金融資產(chǎn)收益率的實證研究中,運用DCC-GARCH模型和基于獨立成分分析(ICA)的ICA-GARCH模型對白銀概念股中的豫光金鉛和銅陵有色等8個股票的五分鐘的收益率序列進行了估計。實證結(jié)果表明這8個概念股之間存在波動相關性,且這種相關性是隨著時間改變而改變的,DCC-GARCH模型和ICA-GARCH模型都能很好地對這種相關性進行刻畫,而通過比較這兩個模型的殘差自相關性發(fā)現(xiàn)ICA-GARCH模型具有更好的擬合優(yōu)勢,且運行速度更快。 同時,我們還將ICA引入到了多維資產(chǎn)的“已實現(xiàn)”協(xié)方差矩陣的研究中,構(gòu)建了具有長記憶性的ICA-ARFIMA模型對多維資產(chǎn)高頻收益率序列進行分析。通過ICA處理將多維資產(chǎn)收益率序列轉(zhuǎn)換為幾個相互獨立的成分,然后分別計算各獨立成分的“已實現(xiàn)”波動率并進行相關模型估計,從而達到簡化模型參數(shù)估計的目的。通過實證發(fā)現(xiàn)獨立“己實現(xiàn)”波動率和對數(shù)獨立“已實現(xiàn)”波動率都具有顯著的長記憶性,ICA-ARFIMA模型能夠很好地對多維資產(chǎn)收益率的“已實現(xiàn)”協(xié)方差矩陣進行估計。 此外,我們將ICA-ARFIMA模型估計得到的“已實現(xiàn)”波動率引入到了風險價值VaR的計算中。實證結(jié)果表明ICA-ARFIMA模型得到的波動率能夠很好地對資產(chǎn)收益率的風險進行刻畫,比直接利用原收益率序列估計得到的VaR值有更高的估計精度。
[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.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F224;F832.51

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