A wavelet Whittle estimator of generalized long-memory stochastic volatility
A wavelet Whittle estimator of generalized long-memory stochastic volatility
复制标题
广义长记忆随机波动率的小波Whittle估计器
DOI：
10.1007/s10260-010-0153-9
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发表时间：
2011
影响因子：
1
通讯作者：
Michael Hauser
中科院分区：
文献类型：
--
作者：
A. Gonzaga;Michael Hauser
考虑了长记忆随机波动率模型的ak-GARMA推广，讨论了模型的性质，并提出了模型参数的小波Whittle估计。它的一致性得到了证明。Monte Carlo实验表明，小样本性质本质上是不可区分的Whittle估计，但相对于基于小波的近似最大似然估计是有利的。应用微软公司的股票，其实现波动率的日内季节性模式建模。
We consider ak-GARMA generalization of the long-memory stochastic volatility model, discuss the properties of the model and propose a wavelet-based Whittle estimator for its parameters. Its consistency is shown. Monte Carlo experiments show that the small sample properties are essentially indistinguishable from those of the Whittle estimator, but are favorable with respect to a wavelet-based approximate maximum likelihood estimator. An application is given for the Microsoft Corporation stock, modeling the intraday seasonal patterns of its realized volatility.