Forecasting Jumps in the Intraday Foreign Exchange Rate Time Series with Hawkes Processes and Logistic Regression

Milan Fičura ()
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Milan Fičura: University of Economics, Prague

Chapter Chapter 12 in New Trends in Finance and Accounting, 2017, pp 125-137 from Springer

Abstract: Abstract Methodology for modelling and prediction of jumps in the high-frequency financial time series is presented. The intraday seasonality of jump intensity is modelled through a series of regime-specific dummy variables, while the self-exciting (clustering) behaviour of jumps is modelled with the Hakes process and alternatively with logistic regression. The models are tested on the 15-min-frequency EUR/USD time series with nonparametrically identified jumps via the L-estimator. The results indicate strong ability of the models to predict jump occurrences in the 15-min horizon. Most of the predictive accuracy does, however, stem from the intraday seasonality pattern of jump intensity, while the self-exciting component has only a minor effect on the overall performance. The identified self-exciting behaviour of jumps is very strong, but only short-term. Long-term clustering of jumps surprisingly was not identified by the models applied to the intraday frequency. There was also no significant difference in the predictive accuracy of the Hawkes process-based model and the logistic regression-based model.

Keywords: High-frequency data; Self-exciting jumps; Hawkes process; Logistic regression (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-49559-0_12

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DOI: 10.1007/978-3-319-49559-0_12

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