Feng, Yuanhua and Yu, Keming (2006): Nonparametric estimation of timevarying covariance matrix in a slowly changing vector random walk model.

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Abstract
A new multivariate random walk model with slowly changing parameters is introduced and investigated in detail. Nonparametric estimation of local covariance matrix is proposed. The asymptotic distributions, including asymptotic biases, variances and covariances of the proposed estimators are obtained. The properties of the estimated value of a weighted sum of individual nonparametric estimators are also studied in detail. The integrated effect of the estimation errors from the estimation for the difference series to the integrated processes is derived. Practical relevance of the model and estimation is illustrated by application to several foreign exchange rates.
Item Type:  MPRA Paper 

Institution:  HeriotWatt University and Brunel University 
Original Title:  Nonparametric estimation of timevarying covariance matrix in a slowly changing vector random walk model 
Language:  English 
Keywords:  Multivariate time series; slowly changing vector random walk; local covariance matrix; kernel estimation; asymptotic properties; forecasting 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models G  Financial Economics > G0  General > G00  General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General 
Item ID:  1597 
Depositing User:  Yuanhua Feng 
Date Deposited:  30. Jan 2007 
Last Modified:  17. May 2015 20:58 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/1597 