nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒03‒12
five papers chosen by
Thanos Verousis

  1. Nonlinear Intermediary Pricing in the Oil Futures Market By Daniel Bierbaumer; Malte Rieth; Anton Velinov
  2. Risk Everywhere: Modeling and Managing Volatility By Bollerslev, Tim; Hood, Benjamin; Huss, John; Pedersen, Lasse Heje
  3. Extracting the multi-timescale activity patterns of online financial markets By Teruyoshi Kobayashi; Anna Sapienza; Emilio Ferrara
  4. Market Impact in a Latent Order Book By Pierre Laffitte; Ismael Lemhadri
  5. How much information is incorporated in financial asset prices? Experimental Evidence By Lionel Page; Christoph Siemroth

  1. By: Daniel Bierbaumer; Malte Rieth; Anton Velinov
    Abstract: We study the state-dependent trading behavior of financial intermediaries in the oil futures market, using structural vector autoregressions with Markov switching in heteroskedasticity. We decompose changes in futures price volatility into changes in the slopes of traders' demand curves and in the variability of their demand shocks. We find that the downward-sloping demand curve of intermediaries steepens significantly during turbulent times. Moreover, the variance of intermediaries' own demand shocks doubles during these episodes. These findings suggest that the futures pricing of intermediaries is nonlinear and increases the hedging costs of producers and processors of oil when volatility is high.
    Keywords: Commodities, Structural VAR, Financial Intermediaries, State-dependency, Asset Pricing, Markov Switching
    JEL: C32 G12 G21 Q02
    Date: 2018
  2. By: Bollerslev, Tim; Hood, Benjamin; Huss, John; Pedersen, Lasse Heje
    Abstract: Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities within and across asset classes in panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more conventional procedures that do not incorporate the information in the high-frequency intraday data and/or the similarities in the volatilities. A utility-based framework designed to evaluate the economic gains from risk modeling highlights the interplay between parsimony of model specification, transaction costs, and speed of trading in the practical implementation of the different risk models.
    Keywords: high-frequency data; Market and volatility risk; realized utility; realized volatility; risk modeling and forecasting; risk targeting; volatility trading
    JEL: C22 C51 C53 C58
    Date: 2018–02
  3. By: Teruyoshi Kobayashi (Graduate School of Economics, Kobe University); Anna Sapienza (University of Southern California, Information Sciences Institute, Los Angeles); Emilio Ferrara (University of Southern California, Information Sciences Institute, Los Angeles)
    Abstract: Online financial markets can be represented as complex systems where trading dynamics can be captured and characterized at different resolutions and time scales. In this work, we develop a methodology based on non-negative tensor factorization (NTF) aimed at extracting and revealing the multi-timescale trading dynamics governing online financial systems. We demonstrate the advantage of our strategy first using synthetic data, and then on real-world data capturing all interbank transactions (over a million) occurred in an Italian online financial market (e-MID) between 2001 and 2015. Our results demonstrate how NTF can uncover hidden activity patterns that characterize groups of banks exhibiting different trading strategies (normal vs. early vs. flash trading, etc.). We further illustrate how our methodology can reveal “crisis modalities†in trading triggered by endogenous and exogenous system shocks: as an example, we reveal and characterize trading anomalies in the midst of the 2008 financial crisis.
    Date: 2018–02
  4. By: Pierre Laffitte; Ismael Lemhadri
    Abstract: We revisit the classical problem of market impact through the lens of a new agent-based model. Drawing from the mean-field approach in Statistical Mechanics and Physics, we assume a large number of agents interacting in the order book. By taking the 'continuum' limit we obtain a set of nonlinear differential equations, the core of our dynamical theory of price formation. And we explicitly solve them using Fourier analysis. One could talk as well of a "micro-macro" approach of equilibrium, where the market price is the consequence of each ("microscopic") agent behaving with respect to his preferences and to global ("macroscopic") information. When a large market order (or metaorder) perturbs the market, our model recovers the square-root law of impact, providing new insights on the price formation process. In addition, we give various limiting cases, examples and possible extensions.
    Date: 2018–02
  5. By: Lionel Page; Christoph Siemroth
    Abstract: We propose a new estimation method and use experimental data from multiple double auction experiments in the literature to directly estimate how much information is incorporated in financial market prices. We find that public information is almost completely reflected in prices, but that surprisingly little private information—less than 50%—is incorporated in prices. Our estimates therefore suggest that while semi-strong informational efficiency is consistent with the data, financial market prices may be very far from strong-form efficiency. We compare our estimates with beliefs of economists surveyed at the Econometric Society Meetings, and find that economists and finance researchers alike expect market prices to reflect considerably more private information than what we estimated.
    JEL: C92 D82 D84 G14
    Date: 2018–02–27

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