nep-mst New Economics Papers
on Market Microstructure
Issue of 2014‒11‒07
five papers chosen by
Thanos Verousis

  1. False News, Informational Efficiency, and Price Reversals. By J. Dugast; T. Foucault
  2. Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading By Ulrich Hounyo
  3. Volatility is rough By Jim Gatheral; Thibault Jaisson; Mathieu Rosenbaum
  4. Spot Market Volatility and Futures Trading: The Pitfalls of Using a Dummy Variable Approach By Martin T. Bohl; Jeanne Diesteldorf; Christian A. Salm; Bernd Wilfling
  5. Forex Trading and the WMR Fix By Martin Evans

  1. By: J. Dugast; T. Foucault
    Abstract: Speculators can discover whether a signal is true or false by processing it but this takes time. Hence they face a trade-off between trading fast on a signal (i.e., before processing it), at the risk of trading on a false news, or trading after processing the signal, at the risk that prices already reflect their information. The number of speculators who choose to trade fast increases with news reliability and decreases with the cost of fast trading technologies. We derive testable implications for the effects of these variables on (i) the value of information, (ii) patterns in returns and trades, (iii) the frequency of price reversals in a stock, and (iv) informational efficiency. Cheaper fast trading technologies simultaneously raise informational efficiency and the frequency of ``mini-flash crashes": large price movements that revert quickly.
    Keywords: news, high-frequency trading, price reversals, informational efficiency, mini-flash crashes.
    JEL: G10 G12 G14
    Date: 2014
  2. By: Ulrich Hounyo (Oxford-Man Institute, University of Oxford, and Aarhus University and CREATES)
    Abstract: We propose a bootstrap method for estimating the distribution (and functionals of it such as the variance) of various integrated covariance matrix estimators. In particular, we first adapt the wild blocks of blocks bootstrap method suggested for the pre-averaged realized volatility estimator to a general class of estimators of integrated covolatility. We then show the first-order asymptotic validity of this method in the multivariate context with a potential presence of jumps, dependent microstructure noise, irregularly spaced and non-synchronous data. Due to our focus on nonstudentized statistics, our results justify using the bootstrap to estimate the covariance matrix of a broad class of covolatility estimators. The bootstrap variance estimator is positive semi-definite by construction, an appealing feature that is not always shared by existing variance estimators of the integrated covariance estimator. As an application of our results, we also consider the bootstrap for regression coefficients. We show that the wild blocks of blocks bootstrap, appropriately centered, is able to mimic both the dependence and heterogeneity of the scores, thus justifying the construction of bootstrap percentile intervals as well as variance estimates in this context. This contrasts with the traditional pairs bootstrap which is not able to mimic the score heterogeneity even in the simple case where no microstructure noise is present. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves the finite sample properties of the existing first-order asymptotic theory. We illustrate its practical use on high-frequency equity data.
    Keywords: High-frequency data, market microstructure noise, non-synchronous data, jumps, realized measures, integrated covariance, wild bootstrap, block bootstrap
    JEL: C15 C22 C58
    Date: 2014–10–07
  3. By: Jim Gatheral; Thibault Jaisson; Mathieu Rosenbaum
    Abstract: Estimating volatility from recent high frequency data, we revisit the question of the smoothness of the volatility process. Our main result is that log-volatility behaves essentially as a fractional Brownian motion with Hurst exponent H of order 0.1, at any reasonable time scale. This leads us to adopt the fractional stochastic volatility (FSV) model of Comte and Renault. We call our model Rough FSV (RFSV) to underline that, in contrast to FSV, H
    Date: 2014–10
  4. By: Martin T. Bohl; Jeanne Diesteldorf; Christian A. Salm; Bernd Wilfling
    Abstract: This paper challenges the existing literature examining the impact of the introduction of index futures trading on the volatility of its underlying. To overcome econometric shortcomings of previously published work using the dummy variable approach, we employ a Markov-switching-GARCH technique. This approach endogenously identifes distinct volatility regimes rather than modelling an exogenously defined one-step change in the volatility process. We investigate stock market volatility in France, Germany, Japan, the UK and the US. Our empirical results indicate that index futures trading does neither stabilize nor destabilize the underlying spot market.
    Keywords: Stock Index Futures, Stock Market Volatility, Markov-Switching-GARCH Model
    JEL: C32 G10 G14 G20
    Date: 2014–10
  5. By: Martin Evans (Department of Economics, Georgetown University)
    Abstract: Since 2013 regulators have been investigating the activities of some of the world's largest banks around the setting of daily benchmarks for forex prices. These benchmarks are a key linchpin of world financial markets, providing standardize prices used to value global equity and bond portfolios, to hedge currency exposure, and to write and execute derivatives' contracts. The most important of these benchmarks,called the "London 4pm Fix", "the WMR Fix" or just the "Fix", is published by the WM Company and Reuters based on forex trading around 4:00 pm GMT. This paper undertakes a detailed empirical analysis of the how forex rates behave around the Fix drawing on a decade of tick-by-tick data for 21 currency pairs. The analysis reveals that the behavior of spot rates in the minutes immediately before and after 4:00 pm are quite unlike that observed at other times. Pre- and post-Fix changes in spot rates are extraordinarily volatile and exhibit strong negative serial correlation, particularly on the last trading day of each month. These statistical features appear pervasive, they are present across all 21 currency pairs throughout the decade. However, they are also inconsistent with the predictions of existing microstructure models of competitive forex trading.
    Keywords: Forex Trading, Order Flows, Forex Price Fixes, Microstructure Trading Models
    JEL: F3 F4 G1
    Date: 2014–08–01

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