cdcc_gradient: This functions calculates numerical gradient of...
In xdcclarge: Estimating a (c)DCC-GARCH Model in Large Dimensions
Description
Usage
Arguments
Value
Description
This functions calculates numerical gradient of log-likelihood of cDCC-GARCH model.
Usage
1
cdcc_gradient(param, ht, residuals, stdresids, uncR, d = 1e-05)
Arguments
param
cDCC-GARCH parameters(alpha,beta)
ht
matrix of conditional variance vectors (T by N)
residuals
matrix of residual(de-mean) returns (T by N)
stdresids
matrix of standrdized(De-GARCH) residual returns (T by N)
uncR
unconditional correlation matrix of stdresids (N by N)
d
(log-lik(x+d) - log-lik(x))/d
Value
numerical gradient of log-likelihood of cDCC-GARCH model(vector)
xdcclarge documentation built on May 2, 2019, 12:40 p.m.
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Description Usage Arguments Value
Description
This functions calculates numerical gradient of log-likelihood of cDCC-GARCH model.
Usage
1 | cdcc_gradient(param, ht, residuals, stdresids, uncR, d = 1e-05)
|
Arguments
param |
cDCC-GARCH parameters(alpha,beta) |
ht |
matrix of conditional variance vectors (T by N) |
residuals |
matrix of residual(de-mean) returns (T by N) |
stdresids |
matrix of standrdized(De-GARCH) residual returns (T by N) |
uncR |
unconditional correlation matrix of stdresids (N by N) |
d |
(log-lik(x+d) - log-lik(x))/d |
Value
numerical gradient of log-likelihood of cDCC-GARCH model(vector)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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