A hidden Markov approach to the forward premium puzzle

Abstract

A Hidden Markov Chain (HMC) is applied to study the forward premium puzzle. The weekly quotient of the interest rate differential divided by the log exchange rate change is modeled as a Hidden Markov process. Compared with existing standard approaches, the Hidden Markov approach allows a detailed analysis of the puzzle on a day-to-day basis while taking into full account the presence of noise in the observations. Two and three state models are investigated. A three-state HMC model performs better than two-state models. Application of the three-state model reveals that the above quotient is mostly zero, and hence leads to the rejection of the uncovered interest rate parity hypothesis.