Local Whittle Analysis of Stationary Fractional Cointegration and the Implied–Realized Volatility Relation
提出局部Whittle拟极大似然估计量，联合估计分数协整模型的整合阶数和协整向量，在弱正则条件下证明一致性，并在局部正交条件下证明渐近正态性，蒙特卡洛模拟验证有限样本性能，应用于金融波动率序列检验隐含-已实现波动率的无偏性假设。
I consider local Whittle analysis of a stationary fractionally cointegrated model. The local Whittle quasi maximum likelihood estimator is proposed to jointly estimate the integration orders of the regressors, the integration order of the errors, and the cointegration vector. The proposed estimator is semiparametric in the sense that it employs local assumptions on the joint spectral density matrix of the regressors and the errors near the zero frequency. I show that the estimator is consistent under weak regularity conditions, and, under an additional local orthogonality condition between the regressors and the cointegration errors, I show asymptotic normality. Indeed, the estimator is asymptotically normal for the entire stationary region of the integration orders, and, thus, for a wider range of integration orders than the narrow-band frequency domain least squares estimator of the cointegration vector, and it is superior to the latter estimator with respect to asymptotic variance. Monte Carlo evidence documenting the finite-sample feasibility of the new methodology is presented. In an application to financial volatility series, I examine the unbiasedness hypothesis in the implied–realized volatility relation.