statistical phenomenon arising from the persistent autocorrelation of forward premiums. Again, these attempts have achieved limited success. Other work has attempted to model the exchange rate as a random or walk. For example, Baillie and Bollerslev (2000) present a stylized model of the exchange rate as a semi-martingale that imposes UIP and allows the daily spot exchange rate to possess very persistent volatility. This model accurately reproduces some of the observed data on the forward premium anomaly and concludes that the spot market will converge to UIP with larger sample sizes and longer time horizons. These models make an important contribution by allowing for stochastic variation in the exchange rate, but remain silent on the driving force behind the variation. Namely, they do not directly model the impact of traders’ decisions on the spot rate. Recently, a new branch of simple stochastic models of interacting agents has been proposed, predominately in models of the market. The models draw from the broader behavioral finance movement, but the design and specification of the models more closely resemble models of particle interactions in physics. These models are derived from a basic insight in physics that many statistical regularities can often be explained as laws” “scaling emerging from the lower level interactions of the constituent parts of a complex system. Scaling laws are the signature of systems with a large number of interacting units that emerge from the interaction of many, complex subunits and are only dependent on a few basic principles of interaction. Economists later adapted these scaling laws models to complex financial systems to explain macro level statistical regularities as derivatives of complex, micro level interactions of agents. For example, Stanley et al. (1996) argued that
statistical phenomenon arising from the persistent autocorrelation of forward premiums. Again, these attempts have achieved limited success. Other work has attempted to model the exchange rate as a random or walk. For example, Baillie and Bollerslev (2000) present a stylized model of the exchange rate as a semi-martingale that imposes UIP and allows the daily spot exchange rate to possess very persistent volatility. This model accurately reproduces some of the observed data on the forward premium anomaly and concludes that the spot market will converge to UIP with larger sample sizes and longer time horizons. These models make an important contribution by allowing for stochastic variation in the exchange rate, but remain silent on the driving force behind the variation. Namely, they do not directly model the impact of traders’ decisions on the spot rate. Recently, a new branch of simple stochastic models of interacting agents has been proposed, predominately in models of the market. The models draw from the broader behavioral finance movement, but the design and specification of the models more closely resemble models of particle interactions in physics. These models are derived from a basic insight in physics that many statistical regularities can often be explained as “scaling laws” emerging from the lower level interactions of the constituent parts of a complex system. Scaling laws are the signature of systems with a large number of interacting units that emerge from the interaction of many, complex subunits and are only dependent on a few basic principles of interaction. Economists later adapted these scaling laws models to complex financial systems to explain macro level statistical regularities as derivatives of complex, micro level interactions of agents. For example, Stanley et al. (1996) argued that