FRACTIONAL COINTEGRATION IN STOCHASTIC VOLATILITY MODELS

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+