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High frequency time series analysis and prediction using Markov models
Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)
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There has been a surge in interest in the analysis and prediction of high frequency time series in recent years. We consider the problem of predicting the direction of change in tick data of the Swiss franc/U.S. dollar exchange rate. To accomplish this, we show that a Markov model can nd regularities in certain local regions of the data and can be used to predict the direction of the next tick. Predictability seems to decrease in more recent years. With transaction costs, the model is unlikely to be pro table.
doi:10.1109/cifer.1997.618933
dblp:conf/cifer/Papageorgiou97
fatcat:ke27arciojfutfkamq7o242fzi