nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒03‒23
five papers chosen by
Thanos Verousis

  1. David vs Goliath (You against the Markets), A Dynamic Programming Approach to Separate the Impact and Timing of Trading Costs By Ravi Kashyap
  2. Liquidity costs: a new numerical methodology and an empirical study By Christophe Michel; Victor Reutenauer; Denis Talay; Etienne Tanré
  3. Extrapolation and Bubbles By Nicholas Barberis; Robin Greenwood; Lawrence Jin; Andrei Shleifer
  4. How Crashes Develop: Intradaily Volatility and Crash Evolution By David S. Bates
  5. The Social Value of Financial Expertise By Pablo Kurlat

  1. By: Ravi Kashyap
    Abstract: To trade, or not to trade, that is the question Whether an optimizer can yield the answer Against the spikes and crashes of markets gone wild. To quench one's thirst before liquidity runs dry Or wait till the tide of momentum turns mild. A trader's conundrum is whether (and how much) to trade during a given interval or wait for the next interval when the price momentum is more favorable to his direction of trading. We develop a fundamentally different stochastic dynamic programming model of trading costs based on the Bellman principle of optimality. We use this model to provide insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their actions) and Market Timing (price move caused by everyone else) components. Plugging different distributions of prices and volumes into this framework can help traders decide when to bear higher Market Impact by trading more in the hope of offsetting the cost of trading at a higher price later. We derive formulations of this model under different laws of motion of the security prices. We start with a benchmark scenario and extend this to include multiple sources of uncertainty, liquidity constraints due to volume curve shifts and relate trading costs to the spread.
    Date: 2016–03
  2. By: Christophe Michel (FIM - Service Interest Rates and Hybrid Quantitative Research - CALYON); Victor Reutenauer (Fotonower); Denis Talay (TOSCA - TO Simulate and CAlibrate stochastic models - CRISAM - Inria Sophia Antipolis - Méditerranée - INRIA - IECL - Institut Élie Cartan de Lorraine - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique); Etienne Tanré (TOSCA - TO Simulate and CAlibrate stochastic models - CRISAM - Inria Sophia Antipolis - Méditerranée - INRIA - IECL - Institut Élie Cartan de Lorraine - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We consider rate swaps which pay a fixed rate against a floating rate in presence of bid-ask spread costs. Even for simple models of bid-ask spread costs, there is no explicit optimal strategy minimizing a risk measure of the hedging error. We here propose an efficient algorithm, based on the stochas-tic gradient method, to obtain an approximate optimal strategy without solving a stochastic control problem. We validate our algorithm by numer-ical experiments. We also develop several variants of the algorithm and discuss their performances in terms of the numerical parameters and the liquidity cost.
    Keywords: Optimization optimisation,Stochastic Algorithms,Interest rates derivatives
    Date: 2016
  3. By: Nicholas Barberis; Robin Greenwood; Lawrence Jin; Andrei Shleifer
    Abstract: We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals—an average of the asset’s past price changes and the asset’s degree of overvaluation. The two signals are in conflict, and investors “waver” over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. We analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model’s distinctive predictions.
    JEL: G02 G12
    Date: 2016–01
  4. By: David S. Bates
    Abstract: This paper explores whether affine models with volatility jumps estimated on intradaily S&P 500 futures data over 1983-2008 can capture major daily outliers such as the 1987 stock market crash. I find that intradaily jumps in futures prices are typically small, and that self-exciting but short-lived volatility spikes capture intradaily and daily returns better. Multifactor models of the evolution of diffusive variance and jump intensities improve fits substantially, including out-of-sample over 2009-13. The models capture reasonably well the conditional distributions of daily returns and of realized variance outliers, but underpredict realized variance inliers.
    JEL: C22 G13
    Date: 2016–02
  5. By: Pablo Kurlat
    Abstract: I study expertise acquisition in a model of trading under asymmetric information. I propose and implement a method to estimate the ratio of social to private marginal value of expertise. This can be decomposed into three sufficient statistics: traders' average profits, the fraction of bad assets among traded assets and the elasticity of good assets traded with respect to capital inflows. For venture capital, the ratio is between 0.64 and 0.83 and for junk bond underwriting, it is between 0.09 and 0.26. In both cases this is less than one so at the margin financial expertise destroys surplus.
    JEL: D53 D82 G14
    Date: 2016–02

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