Bias-Reduced Estimation of Long Memory Stochastic Volatility
Bias-Reduced Estimation of Long Memory Stochastic Volatility
复制标题
长记忆随机波动率的减少偏差估计
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
M. Nielsen
中科院分区：
文献类型：
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作者：
Per Skaarup Frederiksen;M. Nielsen
我们建议使用局部多项式 Whittle 估计器的变体来估计波动过程中具有潜在非平稳性的长记忆随机波动模型的波动记忆参数。我们证明了估计量是渐近正态的，并且能够减少偏差以及任意接近参数速率（n1=2）的收敛速率。进行了蒙特卡罗研究来支持理论结果，并且对每日汇率的分析证明了估计量的经验有用性
We propose to use a variant of the local polynomial Whittle estimator to estimate the memory parameter in volatility for long memory stochastic volatility models with potential nonstation- arity in the volatility process. We show that the estimator is asymptotically normal and capable of obtaining bias reduction as well as a rate of convergence arbitrarily close to the parametric rate, n1=2. A Monte Carlo study is conducted to support the theoretical results, and an analysis of daily exchange rates demonstrates the empirical usefulness of the estimators