Subordinated Exchange Rate Models : Evidence for Heavy Tailed Distributions and Long-Range Dependence

We investigate the main properties of high-frequency exchange rate data in the setting of stochastic subordination and stable modeling, focusing on heavy-tailedness and long memory, together with their dependence on the sampling period. We show that the the instrinsic time process exhibits strong long-range dependence and has increments well described by a Weibull law, while the return series in intrinsic time has weak long memory and is well approximated by a stable Levy motion. We also show that the stable domain of attraction offers a good fit to the returns in physical time, which leads us to consider as a realistic model for exchange rate data a process subordinated to an alpha-stable Levy motion (possibly fractional stable) by a long-memory intrinsic time process with Weibull distributed increments