FRACTIONAL COINTEGRATION IN STOCHASTIC VOLATILITY MODELS
研究了双变量因子模型中，当共同因子和异质误差在高阶矩上存在强依赖时，如何通过数据变换恢复分数协整关系，并提出了因子载荷的窄带半参数估计方法。
Asset returns are frequently assumed to be determined by one or more common factors. We consider a bivariate factor model where the unobservable common factor and idiosyncratic errors are stationary and serially uncorrelated but have strong dependence in higher moments. Stochastic volatility models for the latent variables are employed, in view of their direct application to asset pricing models. Assuming that the underlying persistence is higher in the factor than in the errors, a fractional cointegrating relationship can be recovered by suitable transformation of the data. We propose a narrow band semiparametric estimate of the factor loadings, which is shown to be consistent with a rate of convergence, and its finite-sample properties are investigated in a Monte Carlo experiment.