Stochastic Modelling of Big Data in Finance

Anatoliy Swishchuk ()
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Anatoliy Swishchuk: University of Calgary

Methodology and Computing in Applied Probability, 2020, vol. 22, issue 4, 1613-1630

Abstract: Abstract We present a new approach to study big data in finance (specifically, in limit order books), based on stochastic modelling of price changes associated with high-frequency and algorithmic trading. We introduce a big data in finance, namely, limit order books (LOB), and describes them by Lobster data-academic data for studying LOB. Numerical results, associated with Lobster and other data, are presented, and explanation and justification of our method of studying of big data in finance are considered. We also describe various stochastic models for mid-price changes in the market, and explain how to use these models in practice, highlighting the methodological aspects of using the models.

Keywords: Big data in finance; Stochastic modelling; Limit order books; Semi-Markov modelling; Compound Hawkes pocess; General compound Hawkes process; Non-linear general compound Hawkes process; Multivariate general compound Hawkes process; LOBster data; CISCO data; Deutsche Boerse Group data; Methodological aspects of using the models; 60G55; 60K15; 91B50; 91B70; 60F05 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11009-020-09826-6

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