Abstract
This article explores the realized volatility matrix and its model in the classic asset allocation problems. Update time method using UHF data synchronization China's stock market, using three different correction and noise reduction technology get three volatility matrix sequence; sequence through the matrix modeling portray get yields and volatility matrix vector prediction value and apply to asset allocation problem.
Highlights
Asset Allocation is the core of modern finance
This article is based on realized volatility ideological construct covariance matrix have been achieved as a risk measure introduced asset allocation issues - Risk analysis framework in the past, with the overall volatility as an unbiased estimate of the amount of assets at a time risk measurement within the segment
We use a section of three common correction and noise reduction technology availability of diverse realized volatility matrix sequence, need to use the sequence of multi-dimensional modeling of realized volatility matrix characterization
Summary
Asset Allocation is the core of modern finance. This article is based on realized volatility ideological construct covariance matrix have been achieved as a risk measure introduced asset allocation issues - Risk analysis framework in the past, with the overall volatility (volatility points) as an unbiased estimate of the amount of assets at a time risk measurement within the segment. Establish income-based asset allocation of high frequency data Financial - realized covariance analysis framework. Through the introduction of the common types of correction and noise reduction technology, to obtain the volatility matrix sequence use conditions autoregression Wishart model of realized covariance RCOV (Realized Covariance) modeling portrayed. Return volatility matrix model and combined model, building model and Mean -CAW -CAW-HAR models. The above model to the asset allocation problem in the past
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