Temporal Aggregation of Garch Processes
推导了高频ARMA-GARCH模型在低频下的隐含模型，发现低频模型仍具有GARCH条件异方差性，且其参数依赖于高频模型的均值、方差和峰度参数，同时可从低频数据一致估计高频模型参数。
The authors derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. They show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well. The parameters in the conditional variance equation of the low frequency model depend upon mean, variance, and kurtosis parameters of the corresponding high frequency model. Moreover, strongly consistent estimators of the parameters in the high frequency model can be derived from low frequency data. The common assumption in applications that rescaled innovations are independent is disputable, since it depends upon the available data frequency. Copyright 1993 by The Econometric Society.