基于独立成分分析的多维高频数据波动分析

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1、阵的研究中，构建了具有长记忆性的ICA—ARFIMA模型对多维资产高频收益率序列进行分析。通过ICA处理将多维资产收益率序列转换为几个相互独立的成分，然后分别计算各独：立成分的“己实现”波动率并进行相关模型估计，从而达到简化模型参数估计的目的。通过实证发现独立“已实现”波动率和对数独立“已实现”波动率都具有显著的长记忆性，ICA．ARFIMA模型能够很好地对多维资产收益率的“已实现”协方差矩阵进行估计。此外，我们将ICA—ARFIMA模型估计得到的“已实现”波动率引入到了风险价值VaR的计算中。实证结果表明ICA—ARFIMA模型得到的波动率能够很好地对资产收益率的风险进行刻画，比直

2、接利用原收益率序列估计得到的VaR值有更高的估计精度。关键词：独立成分分析；长记忆性；DCC—GARCH；ICA—GARCH；ICA—ARFIMA；VaRVOLATILITYMODELINGOFHIGH—FREQUENCYINTRADAYDATABASED0NINDEPENDENTCOM

3、PONENTSANALYSISABSTRACTWiththeintegrationoftheworldeconomy，theconnectionbetweenfinancialmarketsbothathomeandabroadisbecomingcloser．Asanimportantcompon

4、entinChina'sfinancialmarket，moreandmoreattentionsarepaidtothestockmarket．Inaddition，thestockmarketisinthedevelopingandimproving，allparticipantsshouldbefullyawareoftheriSl(sofitandhaveastrongawarenessofriskprevention．Volatilitymodelingisveryimportantinthemanagementofrisk．TheGeneralizedAutoregres

5、siveConditionalHeteroskedasticicy(GARCH)modelandStochasticVolatility(sv)modelarethemostimportantvolatilitymodelsoffinancialtimeseriesmodeling．Inthecaseofmultivariate，GARCHmodelisgeneralizedtothemukivariatecase．TheMultivariateGARCHmodelplaysasignificantroleintheassetallocationandportfolioselecti

6、on．Inthispaper，wesuccinctlysummarythedevelopmentoftheMultivariateGARCHmodelthenapplythemtoanalyzethevolatilityofthereturns．WesuggestusingDCC—GARCHmodelwithdynamiccorrelationICA—GARCHmodelbasedontheIndependentComponentsAnalysis(ICA)toanalyzethevolatilitylinksbetweeneightsliver—relatedstockslikeY

7、uguangGold&lead，TonglingNonferrousMetals，Jiangxicopper，YunnanChihongZinc&Germanium，WesternMining，ShengdaMining，ZijinMiningShenzhenZhongjinLingnanbasedonhigh一仔equencyintradaydataoffive-minutereturns．Theexperimentalresultsshowthatthereexistscorrelatioasbetweenthesliver-relatedstocksthecorrelation

8、sarevaryingovertime．WealsofindthatICA—GARCHmodelismoreeffectivetomodelmultivariatetimeseriesthanDCC—GARCHmodelbycomparingtheautocorrelationofthetwomodels’residualsICA—GARCHmodelcostsleSStimethantheDCC．GARCHmodel．Meanwhile，weintrod