Statistical Modeling of High-Frequency Financial Data

Rama Cont
<span title="">2011</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="" style="color: black;">IEEE Signal Processing Magazine</a> </i> &nbsp;
T he availability of high-frequency data on transactions, quotes, and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and brought up new theoretical and computational challenges. Market dynamics at the transaction level cannot be characterized solely in terms the dynamics of a single price, and one must also take into account the interaction between buy and sell orders of different types by modeling the order flow
more &raquo; ... the bid price, ask price, and possibly other levels of the limit order book. We outline the empirical characteristics of high-frequency financial time series and provide an overview of stochastic models for the continuoustime dynamics of a limit order book, focusing in particular on models that describe the limit order book as a queuing system. We describe some applications of such models and point to some open problems. ELECTRONIC ORDER-DRIVEN MARKETS In recent years, automated trading and dealing have largely replaced floor-based trading in equity markets. Electronic Crossing Networks (ECNs) such as Archipelago, Instinet, Brut, and Tradebook providing order-driven trading systems have captured a large share of the market. In contrast to markets where a market maker or specialist centralizes buy and sell orders and provides liquidity by setting bid and ask quotes, these electronic platforms aggregate all outstanding limit orders in a limit order book that is available to market participants, and market orders are executed against the best available prices, in a mechanical manner. As a result of the ECNs' popularity, established exchanges such as the NYSE, Nasdaq, the Tokyo Stock Exchange, Toronto Stock Exchange, Vancouver Stock Exchange, Euronext (Paris, Amsterdam, Brussels), and the London Stock Exchange have fully or partially adopted electronic order-driven platforms.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/msp.2011.941548</a> <a target="_blank" rel="external noopener" href="">fatcat:3dc76ss2cng5nnmlpcbse4wdsm</a> </span>
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