High Frequency Finance: Using Scaling Laws to Build Trading Models

Dupuis, Alexandre; Olsen, Richard B.

doi:10.1002/9781118445785.ch20

Cited by 13 publications

(11 citation statements)

References 26 publications

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“…The aim of these scaling laws is to establish mathematical relationships among price moves, duration and trend frequency. Reference [13] [14]. Furthermore, a study that deciphers FX market activities based on the DC concept is reported in [15].…”

Section: Directional Changes: An Overviewmentioning

confidence: 99%

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Forecasting directional changes in the FX markets

Bakhach

Tsang

Jalalian

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Most of existing studies sample markets' prices as time series when developing models to predict market's trend. Directional Changes (DC) is an approach to summarize market prices other than time series. DC marks the market as downtrend or uptrend based on the magnitude of prices changes. In this paper we address the problem of forecasting trend's direction in the foreign exchange (FX) market under the DC framework. In particularly we aim to answer the question of whether the current trend will continue for a specific percentage before the trend ends. We propose one single independent variable to make the forecast. We assess the accuracy of our approach using three currency pairs in the FX market; namely EUR/CHF, GBP/CHF, and USD/JPY. The experimental results show that the accuracy of the proposed forecasting model is very good; in some cases, forecasting accuracy was over 80%. However, under particular settings the accuracy may not outperform dummy prediction. The results confirm that directional changes are predictable, and the identified independent variable is useful for forecasting under the DC framework.

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Section: Directional Changes: An Overviewmentioning

confidence: 99%

“…Similarly a market uptrend ends when we observe a price drop equal to the same threshold. Recently, many studies have shown that the DC framework is helpful in studying the FX markets [11][12][13][14][15][16][17]. However, the problem of forecasting trend's direction, or turning point, has not been considered from the DC perspective yet.…”

Section: Introductionmentioning

confidence: 99%

Forecasting directional changes in the FX markets

Bakhach

Tsang

Jalalian

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Different approaches have been developed to deal with time inhomogeneity of tick-by-tick data (see, for example, Refs. [2,63,64]); however, we decided to avoid to introduce such techniques both because we performed a univariate analysis and because we preferred to avoid to introduce a source of arbitrariness coming from the choice of a specific procedure. Finally we report that as for the overall shape of the function ζ (q), we found that in our data set there is no clear preference between the second or the fourth degree considered polynomials, despite in four cases out of six, where the fourth degree polynomial fit was found more suitable, the coefficient of the third degree term can be assumed to be zero within the error bounds.…”

Section: Discussionmentioning

confidence: 99%

Asymptotic scaling properties and estimation of the generalized Hurst exponents in financial data

2017

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We propose a method to measure the Hurst exponents of financial time series. The scaling of the absolute moments against the aggregation horizon of real financial processes and of both uniscaling and multiscaling synthetic processes converges asymptotically towards linearity in log-log scale. In light of this we found appropriate a modification of the usual scaling equation via the introduction of a filter function. We devised a measurement procedure which takes into account the presence of the filter function without the need of directly estimating it. We verified that the method is unbiased within the errors by applying it to synthetic time series with known scaling properties. Finally we show an application to empirical financial time series where we fit the measured scaling exponents via a second or a fourth degree polynomial, which, because of theoretical constraints, have respectively only one and two degrees of freedom. We found that on our data set there is not clear preference between the second or fourth degree polynomial. Moreover the study of the filter functions of each time series shows common patterns of convergence depending on the momentum degree.

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“…The directional‐change scaling law that is the focus of this paper has been found to be particularly useful. This scaling law has recently been utilized to develop FX trading algorithms (Dupuis and Olsen, ) and in risk management. Its use in risk management, via the construction of the ‘scale of market quakes’ (Bisig et al ., ), is interesting in that the approach is analogous to the Richter scale for earthquakes, which is a logarithmic scale based on the empirical observation that the probability of an earthquake of a given energy follows a scaling law.…”

Section: Introductionmentioning

confidence: 99%

Periodicities of Foreign Exchange Markets and the Directional Change Power Law

Giampaoli

Constantinou

2013

Intell. Sys. Acc. Fin. Mgmt.

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This paper utilizes advanced methods from Fourier analysis in order to describe periodicities in financial ultrahigh frequency foreign exchange data. The Lomb-Scargle Fourier transform is used to take into account the irregularity in spacing in the time domain. It provides a natural framework for the power spectra of different inhomogeneous time-series processes to be easily and quickly estimated. Furthermore, an event-based approach in intrinsic time based on a power-law relationship is employed using different event thresholds to filter the foreign exchange tick-data. The calculated spectral density demonstrates that the price process in intrinsic time contains different periodic components for directional changes, especially in the medium-long term, implying the existence of stylized facts of ultrahigh frequency data in the frequency domain.In fact, Aldridge (2010) calculates the maximum gain as the sum of price ranges at each frequency, whereas the maximum potential gain (loss) at every frequency is determined by the (negative) sum of all per-period ranges at that frequency. 190 I. GIAMPAOLI ET AL.

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High Frequency Finance: Using Scaling Laws to Build Trading Models

Cited by 13 publications

References 26 publications

Forecasting directional changes in the FX markets

Forecasting directional changes in the FX markets

Asymptotic scaling properties and estimation of the generalized Hurst exponents in financial data

Periodicities of Foreign Exchange Markets and the Directional Change Power Law

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