nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒07‒16
five papers chosen by
Thanos Verousis
University of Newcastle

  1. The contribution of jumps to forecasting the density of returns By Christophe Chorro; Florian Ielpo; Benoît Sévi
  2. Formation of Market Beliefs in the Oil Market By Stanislav Anatolyev; Sergei Seleznev; Veronika Selezneva
  3. Demand for Information, Macroeconomic Uncertainty, and the Response of U.S. Treasury Securities to News By Foucault, Thierry; Benamar, Hedi; Vega, Clara
  4. Trading algorithms with learning in latent alpha models By Philippe Casgrain; Sebastian Jaimungal
  5. Positive Stock Information In Out-Of-The-Money Option Prices By Konstantinos Gkionis; Alexandros Kostakis

  1. By: Christophe Chorro (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Florian Ielpo (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Unigestion SA - UNIGESTION , IPAG Business School); Benoît Sévi (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes)
    Abstract: The extraction of the jump component in dynamics of asset prices haw witnessed a considerably growing body of literature. Of particular interest is the decomposition of returns' quadratic variation between their continuous and jump components. Recent contributions highlight the importance of this component in forecasting volatility at different horizons. In this article, we extend a methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns at horizons up to sixty days. We follow Boudt et al. (2011) to detect intraday returns that should be considered as jumps. The methodology is robust to intra-week periodicity and further delivers estimates of signed jumps in contrast to the rest of the literature where only the squared jump component can be estimated. Then, we estimate a bivariate model of returns and volatilities where the jump component is independently modeled using a jump distribution that fits the stylized facts of the estimated jumps. Our empirical results for S&P 500 futures, U.S. 10-year Treasury futures, USD/CAD exchange rate and WTI crude oil futures highlight the importance of considering the continuous/jump decomposition for density forecasting while this is not the case for volatility point forecast. In particular, we show that the model considering jumps apart from the continuous component consistenly deliver better density forecasts for forecasting horizons ranging from 1 to 30 days.
    Keywords: leverage effect,density forecasting,jumps,realized volatility,bipower variation,median realized volatility
    Date: 2017–01
  2. By: Stanislav Anatolyev; Sergei Seleznev; Veronika Selezneva
    Abstract: We characterize formation of market beliefs in the oil market by providing a complete characterization of the market reaction to oil inventory surprises. We utilize the unique sequential nature of inventory announcements to identify inventory shocks. We estimate an AR-ARCH-MEM model of the joint dynamics of returns, return volatilities and trading volumes around the announcements using high frequency data on oil futures contracts. Our model (i) handles illiquidity of long maturity contracts by accounting for trading inactivity, (ii) captures time varying trading intensity, and (iii) allows for structural changes in the dynamics and responses to news over time. We show (i) uniform formation of expectations across oil futures contracts with different maturities, (ii) a strong negative relation between inventories surprises and returns, (iii) no effect on the term premium, which suggests that inventory shocks are always considered to be permanent, and (iv) differentiation in the reaction of volume by maturity. We demonstrate how our results can be used to test theories of oil price determination and contribute to the debate on the recent oil glut.
    Keywords: oil market; ultra high frequency data; trading intensity; futures returns; return volatility; inventory surprises; expectation formation;
    JEL: C22 C32 C58 G12 G13
    Date: 2018–06
  3. By: Foucault, Thierry; Benamar, Hedi; Vega, Clara
    Abstract: We measure demand for information prior to nonfarm payroll announcements using a novel dataset consisting of clicks on news articles. We find that when information demand is high shortly before the release of the nonfarm payroll announcement, the price response of U.S. Treasury note futures to nonfarm payroll news surprises doubles. We argue that this relationship stems from the fact that market participants have more incentive to collect information when uncertainty about asset payoffs is higher, as implied by Bayesian learning models. Thus, high information demand about macroeconomic news is a proxy for high macroeconomic uncertainty.
    Keywords: Public information; Macroeconomic News; Uncertainty; U.S. Treasury futures; Investors Attention; Information Demand; Bitly; Media Coverage
    JEL: D83 G12 G14
    Date: 2018–04–13
  4. By: Philippe Casgrain; Sebastian Jaimungal
    Abstract: Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyses how to optimally trade with latent factors that cause prices to jump and diffuse. Moreover, we account for the effect of the trader's actions on quoted prices and the prices they receive from trading. Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem. We provide a verification theorem, and a methodology for calibrating the model by deriving a variation of the expectation-maximization algorithm. To illustrate the efficacy of the optimal strategy, we demonstrate its performance through simulations and compare it to strategies which ignore learning in the latent factors. We also provide calibration results for a particular model using Intel Corporation stock as an example.
    Date: 2018–06
  5. By: Konstantinos Gkionis (Queen Mary University of London); Alexandros Kostakis (Alliance Manchester Business School, University of Manchester)
    Abstract: We examine whether the option market leads the stock market with respect to positive in addition to negative price discovery. We document that out-of-themoney (OTM) option prices, which determine the Risk-Neutral Skewness (RNS) of the underlying stock return’s distribution, can embed positive information regarding the underlying stock. A long-only portfolio of stocks with the highest RNS values yields significant positive alpha in the post-ranking week during the period 1996-2014. This outperformance is mainly driven by stocks that are relatively underpriced but are also exposed to greater downside risk. These findings are consistent with a trading mechanism where investors choose to exploit perceived stock underpricing via OTM options due to their embedded leverage, rather than directly buying the underlying stock to avoid exposure to its potential downside. Due to the absence of severe limits-to-arbitrage for the long-side, the price correction signalled by RNS is very quick, typically overnight.
    Keywords: Option-Implied Information, Price Discovery, Risk-Neutral Skewness, Stock Underpricing, Downside Risk
    JEL: G12 G13 G14
    Date: 2018–05–30

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