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

In: New Trends in Finance and Accounting

Author

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  • Milan Fičura

    (University of Economics, Prague)

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.

Suggested Citation

  • Milan Fičura, 2017. "Forecasting Jumps in the Intraday Foreign Exchange Rate Time Series with Hawkes Processes and Logistic Regression," Springer Proceedings in Business and Economics, in: David Procházka (ed.), New Trends in Finance and Accounting, chapter 0, pages 125-137, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-49559-0_12
    DOI: 10.1007/978-3-319-49559-0_12
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