Fractional Cointegration In StochasticVolatility Models

Asset returns are frequently assumed to be determined by one or more commonfactors. We consider a bivariate factor model, where the unobservable commonfactor and idiosyncratic errors are stationary and serially uncorrelated, but havestrong dependence in higher moments. Stochastic volatility models for the latentvariables are employed, in view of their direct application to asset pricing models.Assuming the underlying persistence is higher in the factor than in the errors, afractional cointegrating relationship can be recovered by suitable transformation ofthe data. We propose a narrow band semiparametric estimate of the factorloadings, which is shown to be consistent with a rate of convergence, and its finitesample properties are investigated in a Monte Carlo experiment.