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LICENSE.md
NewtonStepAugmented.m
PCtoSignal.m
PnDmatrix.m
Projection_S.m
Projection_nS.m
Psd.m
README.md
StructPlusStruct.m
TangentAugmented.m
almonFunction.m
dccLikelihood.m
dccLikelihoodAndScore.m
duplication.m
elimination.m
estimateDCC.m
exampleData.mat
garch11.m
getConstraintVars.m
getConstraintsCheckVals.m
getGradientLocalModel.m
getHcomponents.m
getHcomponentsVec.m
getHinv.m
getModelNumParameters.m
mat.m
math.m
mathstar.m
matr.m
plotLoglikelihoodFunctions.fig
reorderToPC.m
runDCCestimation.m
sig.m
signalToPC.m
vec.m
vech.m
vechstar.m
vecr.m

README.md

DCC-nonScalar-estimation

This repository contains functions that carry out the maximum likelihood estimation of scalar and non-scalar DCC models. The nonlinear positive definiteness constraints are treated via with the Bregman divergences. All the computations are provided in the paper:

Bauwens, L., Grigoryeva, L. and Ortega, J.-P. [2015] Estimation and empirical performance of non-scalar dynamic conditional correlation models. To appear in Computational Statistics and Data Analysis. doi:10.1016/j.csda.2015.02.013. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2407652

  • runDCCestimation.m allows to estimate scalar DCC, rank one deficient DCC, rank two deficient DCC, Hadamard DCC, Almon DCC, and Almon shuffle DCC models for the sample data file: exampleData.mat

REMARK: Notice that the parameters for the estimateDCC function have to be tuned.