Bayesian parameter estimation of Jump-Langevin systems for trend following in finance

In this paper we present a Bayesian method for parameter estimation in linear Jump-Langevin systems, i.e. systems driven by a linear, mean-reverting jump-diffusion trend process. Such models have been applied successfully to trend following in finance, in order to develop momentum-based trading strategies. Parameter estimation is based around a reversible-jump MCMC method for jump-time inference. Parameter estimation is demonstrated on both synthetic and financial time series, and estimated parameters are compared with ad hoc parameter estimates used in earlier work.