estimation of parameter

基本解釋

But the methods of estimation of parameter of the GARCH models, BHHH and GMM, may become invalid because they usually meet the situation that the middle data fluctuate greatly.
而GARCH模型參數(shù)估計(jì)的主要方法BHHH算法和廣義矩方法在實(shí)際運(yùn)算中常遇到中間數(shù)據(jù)震蕩從而導(dǎo)致算法整體失效等。
So firstly to get a better estimation of parameter using iterate inversion on wide scale , then using this estimation as initial value on mini scale till to get global optimum of original problem.
因此可先在粗尺度上迭代反演，得到一個(gè)較好的參數(shù)估計(jì)，再將這個(gè)估計(jì)作為較精細(xì)尺度的初值進(jìn)行反演，直至原問題的全局最優(yōu)解。
Finally, we present a demonstration of Chinese stock which shows obvious bivariate GARCH effect and apply MAA to the estimation, which solve the estimation of parameter of bivariate GARCH model well.
最后，我們對中國股市統(tǒng)計(jì)數(shù)據(jù)進(jìn)行了應(yīng)用研究，結(jié)果表明中國股市存在著顯著的二元GARCH效應(yīng)，并利用多智能體退火算法較好地解決了二元GARCH模型的參數(shù)估計(jì)應(yīng)用。