nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒01‒18
five papers chosen by
Thanos Verousis

  1. A Semi-Markovian Modeling of Limit Order Markets By Anatoliy Swishchuk; Nelson Vadori
  2. Dynamic Multi-Factor Bid-Offer Adjustment Model: A Feedback Mechanism for Dealers (Market Makers) to Deal (Grapple) with the Uncertainty Principle of the Social Sciences By Ravi Kashyap
  3. Extended Abstract: Neural Networks for Limit Order Books By Justin Sirignano
  4. A detailed heterogeneous agent model for a single asset financial market with trading via an order book By Roberto Mota Navarro; Hern\'an Larralde Ridaura
  5. Price Discovery and Local Information in Cattle on Feed Reports By Matthew Diersen

  1. By: Anatoliy Swishchuk; Nelson Vadori
    Abstract: R. Cont and A. de Larrard (SIAM J. Finan. Math, 2013) introduced a tractable stochastic model for the dynamics of a limit order book, computing various quantities of interest such as the probability of a price increase or the diffusion limit of the price process. As suggested by empirical observations, we extend their framework to 1) arbitrary distributions for book events inter-arrival times (possibly non-exponential) and 2) both the nature of a new book event and its corresponding inter-arrival time depend on the nature of the previous book event. We do so by resorting to Markov renewal processes to model the dynamics of the bid and ask queues. We keep analytical tractability via explicit expressions for the Laplace transforms of various quantities of interest. We justify and illustrate our approach by calibrating our model to the five stocks Amazon, Apple, Google, Intel and Microsoft on June 21^{st} 2012. As in R. Cont and A. de Larrard, the bid-ask spread remains constant equal to one tick, only the bid and ask queues are modeled (they are independent from each other and get reinitialized after a price change), and all orders have the same size.
    Date: 2016–01
  2. By: Ravi Kashyap
    Abstract: The author seeks to develop a model to alter the bid-offer spread, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as with the rest of social sciences, where changes can be observed and decisions can be made by participants to influence the system, means that this model has to be adaptive and include a feedback loop that alters the bid-offer adjustment based on the modifications observed in the market and trading conditions, without a significant time delay. The factors used to adjust the spread are price volatility, which is publicly observable, and trade count and volume, which are generally known only to the market maker, in various instruments over different historical durations in time. The contributions of each factor to the bid-offer adjustment are computed separately and then consolidated to produce a very adaptive bid-offer quotation. The author uses the currency markets to build the sample model because they are extremely liquid and trading in them is not as transparent as other financial instruments, such as equities. Simulating the number of trades and the average size of trades from a lognormal distribution, the parameters of the lognormal distributions are chosen such that the total volume in a certain interval matches the volume publicly mentioned by currency trading firms. This methodology can easily be extended to other financial instruments and possibly to any product with the ability to make electronic price quotations, or can even be used to periodically perform manual price updates on products that are traded non-electronically.
    Date: 2016–01
  3. By: Justin Sirignano
    Abstract: We design and test neural networks for modeling the dynamics of the limit order book. In addition to testing traditional neural networks originally designed for classification, we develop a new neural network architecture for modeling spatial distributions (i.e., distributions on $\mathbb{R}^d$) which takes advantage of local spatial structure. Model performance is tested on 140 S\&P 500 and NASDAQ-100 stocks. The neural networks are trained using information from deep into the limit order book (i.e., many levels beyond the best bid and best ask). Techniques from deep learning such as dropout are employed to improve performance. Due to the computational challenges associated with the large amount of data, the neural networks are trained using GPU clusters. The neural networks are shown to outperform simpler models such as the naive empirical model and logistic regression, and the new neural network for spatial distributions outperforms the standard neural network.
    Date: 2016–01
  4. By: Roberto Mota Navarro; Hern\'an Larralde Ridaura
    Abstract: We present an agent based model of a single asset financial market that is capable of replicating several non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. While previous models reported in the literature are also capable of replicating some of these statistical properties, in general, they tend to oversimplify either the trading mechanisms or the behavior of the agents. In our model, we strived to capture the most important characteristics of both aspects to create agents that employ strategies inspired on those used in real markets, and, at the same time, a more realistic trade mechanism based on a double auction order book. We study the role of the distinct types of trader on the return statistics: specifically, correlation properties (or lack thereof), volatilty clustering, heavy tails, and the degree to which the distribution can be described by a log-normal. Further, by introducing the practice of profit taking, our model is also capable of replicating the stylized fact related to an asymmetry in the distribution of losses and gains.
    Date: 2016–01
  5. By: Matthew Diersen (Deparment of Economics South Dakota State University)
    Date: 2015–09–25

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