Bias-Reduced Estimation of Long-Memory Stochastic Volatility
提出一种局部多项式Whittle估计量的变体来估计长记忆随机波动率模型中的记忆参数，该估计量渐近正态且能实现偏差缩减，收敛速度接近参数速率n^{1/2}，蒙特卡洛模拟和汇率数据分析验证了其有效性。
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 nonstationarity 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.