Abstract:
Under rational expectations and risk neutrality the linear projection
of exchange rate change on the forward premium has a unit
coefficient. However, empirical estimates of this coefficient are significantly
less than one and often negative. We investigate whether
replacing rational expectations by discounted least squares (or “perpetual”)
learning can explain the result. We calculate the asymptotic
bias under perpetual learning and show that there is a negative bias
that becomes strongest when the fundamentals are strongly persistent,
i.e. close to a random walk. Simulations confirm that perpetual
learning is potentially able to explain the forward premium puzzle.