  We present a simple hybrid dynamical model as a tool to investigate
behavioral strategies based on trend following. The multiplicative symbolic
dynamics are generated using a lognormal diffusion model for the at-the-money
implied volatility term structure. Thus, are model exploits information from
derivative markets to obtain qualititative properties of the return
distribution for the underlier. We apply our model to the JPY-USD exchange rate
and the corresponding 1mo., 3mo., 6mo. and 1yr. implied volatilities. Our
results indicate that the modulation of autoregressive trend following using
derivative-based signals significantly improves the fit to the distribution of
times between successive sign flips in the underlier time series.
