Abstract :
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+
