Statistical Modeling of High-Frequency Financial Data

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Universal features of price formation in financial markets: perspectives from deep learning (2018, background): “…dynamics in financial markets over the last decade has unleashed TeraBytes of high-frequency data on transactions, order flow and order book dynamics in listed markets, which provide us with a detailed view of the high-frequency dynamics of supply, demand and price in these markets (Cont 2011).”
Optimal order placement in limit order markets (2012, unknown): “Empirical studies show that both market and limit orders affect prices, and the average impact of small orders can be well approximated by a linear function (Cont et al. 2014), as in Kyle (1985). We assume that the impact cost is paid on all orders placed at times 0 and T , irrespective of whether t”
Price Dynamics in a Markovian Limit Order Market (2011, methodology): “The state of the order book is modified by the following order book events: limit orders (at the bid or ask), market orders, and cancellations (see Cont, Stoikov, and Talreja (2010b), Cont, Kukanov, and Stoikov (2010a), Smith et al.”
Modelling high-frequency limit order book dynamics with support vector machines (2015, methodology): “In the former approach, statistical properties of the limit order book for the target ﬁnancial asset are developed and conditional quantities are then derived and modelled (Rosu 2009, Cont et al. 2010, Shek 2011, Cont 2011, He and Kercheval 2012).”