Time Variation in the Standard Forward Premium Regression: Some new Models and Tests15-09-2014
This paper makes two contributions to trying to understand the forward premium anomaly and the apparent breakdowns of Uncovered Interest Rate Parity (UIP).
First, investigation of the time series properties of the forward premium reveals either four or ﬁve breaks in the last twenty three years and evidence of long memory within each sub period. In fact the forward premium is highly nonlinear and appears to defy classiﬁcation as a process with a constant order of integration.
The second aspect of the paper is concerned with the time varying nature of the estimate of the slope parameter when spot returns are regressed on the lagged forward premium. We compare rolling type regression estimates, with Bayesian estimation and also a new Time Varying Parameter (TVP) method that is motivated by the TVP autoregression of Giraitis et al. (2014). The procedure is a form of kernel weighted regression and delivers relatively tight standard errors on the parameter estimates.
We ﬁnd the existence of the forward premium anomaly with large negative beta coefﬁcients in the 1980s and 1990s. For some currencies there is also evidence of large positive coefﬁcients and a reversal of the forward premium anomaly after the ﬁnancial crisis of 2008.