Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/18435
Title: Modeling Multivariate volatilities via most predictable factor
Authors: FU JINGYU
Keywords: multivariate volatilities; most predictable factor; linear combination;conditional variation; regression;time series
Issue Date: 26-Jul-2010
Source: FU JINGYU (2010-07-26). Modeling Multivariate volatilities via most predictable factor. ScholarBank@NUS Repository.
Abstract: We propose to model the multivariate volatilities via the most predictable factor (MPF) which is a linear combination of original multivariate data. We develop an optimization method to find the factor such that it has the largest conditional variation, and build up the regression relationship between the variance of MPF and original data. As a consequence, in the prediction of variance we only need to predict variance for MPF, and use the regression relationship to calculate the variance of each component. The proposed method is illustrated with simulations as well as real data examples.
URI: http://scholarbank.nus.edu.sg/handle/10635/18435
Appears in Collections:Master's Theses (Open)

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