Estimating persistence in the volatility of asset returns with signal plus noise models
本文用长记忆随机波动模型和半参数估计方法分析纳斯达克指数日数据，发现波动率存在长记忆性，整合阶数在0.3到0.5之间，序列平稳且均值回复。
ABSTRACT This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by Robinson ( Annals of Statistics 23 : 1630–1661), and shown by Arteche ( Journal of Econometrics 119 : 131–154) to be consistent and asymptotically normal in the context of signal plus noise models. Daily data on the NASDAQ index are analysed. The results suggest that volatility has a component of long‐memory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean‐reverting. Copyright © 2011 John Wiley & Sons, Ltd.