Christensen, HL and Murphy, J and Godsill, SJ (2012) Forecasting high-frequency futures returns using online langevin dynamics. IEEE Journal on Selected Topics in Signal Processing, 6. pp. 366-380. ISSN 1932-4553Full text not available from this repository.
Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency. © 2011 IEEE.
|Uncontrolled Keywords:||Futures trading online learning particle filter quantitative finance tracking|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:29|
|Last Modified:||22 Dec 2014 01:21|