%0 Journal Article %1 christensen2012forecasting %A Christensen, H.L. %A Murphy, J. %A Godsill, S.J. %D 2012 %J Selected Topics in Signal Processing, IEEE Journal of %K filtering forecasting particle returns %N 4 %P 366-380 %R 10.1109/JSTSP.2012.2191532 %T Forecasting High-Frequency Futures Returns Using Online Langevin Dynamics %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6172210 %V 6 %X 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.

@article{christensen2012forecasting, abstract = {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.}, added-at = {2015-09-21T14:32:47.000+0200}, author = {Christensen, H.L. and Murphy, J. and Godsill, S.J.}, biburl = {https://www.bibsonomy.org/bibtex/27671fb69f60dc3d083c6c7b2b221f74c/krassi}, doi = {10.1109/JSTSP.2012.2191532}, interhash = {fae68cc3df4afc0448bf6d5052966196}, intrahash = {7671fb69f60dc3d083c6c7b2b221f74c}, issn = {1932-4553}, journal = {Selected Topics in Signal Processing, IEEE Journal of}, keywords = {filtering forecasting particle returns}, month = aug, number = 4, pages = {366-380}, timestamp = {2015-09-21T14:33:11.000+0200}, title = {Forecasting High-Frequency Futures Returns Using Online Langevin Dynamics}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6172210}, volume = 6, year = 2012 }