我正在尝试,类似于R的ugarch
# standard GARCH model with optional ARMA part
spec <- ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(r,s)),
mean.model = list(armaOrder = c(p,q)), distribution.model = dist[1])
ugarchfit(spec, data = x[,i], solver = "hybrid", fit.control = list(scale = 1),
numderiv.control = list(hess.eps = 1e-2))
使用 ARCH 库将联合 ARIMA(p,0,q)-GARCH(r,s) 拟合到多个时间序列。基于几种测试方法,我想找出 p,q,r,s 的最佳拟合参数
基于 ARCH 文档均值模型可以选择No Mean、Constant Mean、Autoregressions和Heterogeneous Autoregressions。
arch.arch_model(y, x=None, mean='Constant', lags=0, vol='Garch', p=1, o=0, q=1, power=2.0, dist='Normal', hold_back=None)[source]
如何在 Python 中指定除 AR 之外的可选 MA 组件,类似于 statsmodels.tsa.arima_model.ARMA?
非常感谢您提前。