An empirical behavioral model of liquidity and volatility

By Szabolcs Mike, J. D. Farmer. 2007

247 citations recorded by the source corpus.

We develop a behavioral model for liquidity and volatility based on empirical regularities in trading order flow in the London Stock Exchange. This can be viewed as a very simple agent based model in which all components of the model are validated against real data. Our empirical studies of order flow uncover several interesting regularities in the way trading orders are placed and cancelled. The resulting simple model of order flow is used to simulate price formation under a continuous double auction, and the statistical properties of the resulting simulated sequence of prices are compared to those of real data. The model is constructed using one stock (AZN) and tested on 24 other stocks. For low volatility, small tick size stocks (called Group I) the predictions are very good, but for stocks outside Group I they are not good. For Group I, the model predicts the correct magnitude and functional form of the distribution of the volatility and the bid-ask spread, without adjusting any parameters based on prices. This suggests that at least for Group I stocks, the volatility and heavy tails of prices are related to market microstructure effects, and supports the hypothesis that, at least on short time scales, the large fluctuations of absolute returns are well described by a power law with an exponent that varies from stock to stock.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ea482f474af7ef223bf2e62835d807af1d45bcd6

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Citations in the corpus

The adaptive nature of liquidity taking in limit order books (2014, background): “(2014) P 06002 where LMF refers to the model developed by Lillo, Mike and Farmer in [18] and described above.”
Limit order books (2010, methodology): “The maximum arrival rate of incoming orders was reported to occur at a relative price of 0 on the LSE (Mike and Farmer, 2008), the Shenzhen Stock Exchange (Gu et al.”
The price impact of order book events: market orders, limit orders and cancellations (2009, background): “The relation between order flow and price changes has attracted considerable attention in recent years (Bouchaud et al. 2004, 2006, 2009, Lyons 2006, Hasbrouck 2007, Mike and Farmer 2008).”
Statistical inference for ergodic point processes and application to Limit Order Book (2015, background): “While the availability of high-frequency financial data has made in-depth analysis of LOB dynamics accessible, it is now possible to use statistical tools in order to highlight either empirical facts about their shapes and their evolution [26,9], or to design and select models that are able to repro”
Anomalous price impact and the critical nature of liquidity in financial markets (2011, background): “One knows, for example, that the deposition and cancellation rates of limit orders strongly depend on the distance from the current price [33], that the assumption of a Poisson process is an oversimplification [34] (these observations apply to the real order book, but probably will also hold for the”
Econophysics review: II. Agent-based models (2011, unknown): “This type of modeling is best represented by Mike and Farmer (2008).”
Econophysics: Empirical facts and agent-based models (2009, unknown): “Mike and Farmer (2008) find that in either case the exponential hypothesis (Poisson process) is not satisfied on the market.”