nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒02‒23
nine papers chosen by
Thanos Verousis

  1. Modeling and forecasting exchange rate volatility in time-frequency domain By Barunik, Jozef; Krehlik, Tomas; Vacha, Lukas
  2. Do co-jumps impact correlations in currency markets? By Jozef Barunik; Lukas Vacha
  3. Hawkes processes in finance By Emmanuel Bacry; Iacopo Mastromatteo; Jean-Fran\c{c}ois Muzy
  4. Market structure or traders’ behavior? An assessment of flash crash phenomena and their regulation based on a multi-agent simulation By Nathalie Oriol; Iryna Veryzhenko
  5. Modelling intensities of order flows in a limit order book By Ioane Muni Toke; Nakahiro Yoshida
  6. Unravelling the trading invariance hypothesis By Michael Benzaquen; Jonathan Donier; Jean-Philippe Bouchaud
  7. A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices By Abdi, Farshid; Ranaldo, Angelo
  8. A dynamic optimal execution strategy under stochastic price recovery By Masashi Ieda
  9. Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps By Michael Ho; Jack Xin

  1. By: Barunik, Jozef; Krehlik, Tomas; Vacha, Lukas
    Abstract: This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of volatility into several timescales approximates the behaviour of traders at corresponding investment horizons. The proposed methodology is moreover able to account for impact of jumps due to a recently proposed jump wavelet two scale realized volatility estimator. We propose a realized Jump-GARCH models estimated in two versions using maximum likelihood as well as observation-driven estimation framework of generalized autoregressive score. We compare forecasts using several popular realized volatility measures on foreign exchange rate futures data covering the recent financial crisis. Our results indicate that disentangling jump variation from the integrated variation is important for forecasting performance. An interesting insight into the volatility process is also provided by its multiscale decomposition. We find that most of the information for future volatility comes from high frequency part of the spectra representing very short investment horizons. Our newly proposed models outperform statistically the popular as well conventional models in both one-day and multi-period-ahead forecasting.
    Keywords: realized GARCH,wavelet decomposition,jumps,multi-period-ahead volatility forecasting
    Date: 2016
  2. By: Jozef Barunik; Lukas Vacha
    Abstract: We study how co-jumps influence covariance and correlation in currency markets. We propose a new wavelet-based estimator of quadratic covariation that is able to disentangle the continuous part of quadratic covariation from co-jumps. The proposed estimator is able to identify the statistically significant co-jumps that impact covariance structures by using bootstrapped test statistics. Empirical findings reveal the behavior of co-jumps during Asian, European and U.S. trading sessions. Our results show that the impact of co-jumps on correlations increased during the years 2012 - 2015. Hence appropriately estimating co-jumps is becoming a crucial step in understanding dependence in currency markets.
    Date: 2016–02
  3. By: Emmanuel Bacry; Iacopo Mastromatteo; Jean-Fran\c{c}ois Muzy
    Abstract: In this paper we propose an overview of the recent academic literature devoted to the applications of Hawkes processes in finance. Hawkes processes constitute a particular class of multivariate point processes that has become very popular in empirical high frequency finance this last decade. After a reminder of the main definitions and properties that characterize Hawkes processes, we review their main empirical applications to address many different problems in high frequency finance. Because of their great flexibility and versatility, we show that they have been successfully involved in issues as diverse as estimating the volatility at the level of transaction data, estimating the market stability, accounting for systemic risk contagion, devising optimal execution strategies or capturing the dynamics of the full order book.
    Date: 2015–02
  4. By: Nathalie Oriol (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS - Centre National de la Recherche Scientifique - UNS - Université Nice Sophia Antipolis); Iryna Veryzhenko (LIRSA - Laboratoire Interdisciplinaire de Recherche en Sciences de l'Action - Conservatoire National des Arts et Métiers [CNAM])
    Abstract: This paper aims at studying the flash crash caused by an operational shock with different market participants. We reproduce this shock in artificial market framework to study market quality in different scenarios, with or without strategic traders. We show that traders’ srategies influence the magnitude of the collapse. But, with the help of zero-intelligence traders framework, we show that despite theabsence of market makers, the order-driven market is resilient and favors a price recovery. We find that a short-sales ban imposed by regulator reduces short-term volatility.
    Keywords: flash crash, limit order book, technical trading,Agent-based modeling, zero-intelligence trader
    Date: 2015–05–01
  5. By: Ioane Muni Toke; Nakahiro Yoshida
    Abstract: We propose a parametric model for the simulation of limit order books. We assume that limit orders, market orders and cancellations are submitted according to point processes with state-dependent intensities. We propose new functional forms for these intensities, as well as new models for the placement of limit orders and cancellations. For cancellations, we introduce the concept of "priority index" to describe the selection of orders to be cancelled in the order book. Parameters of the model are estimated using likelihood maximization. We illustrate the performance of the model by providing extensive simulation results, with a comparison to empirical data and a standard Poisson reference.
    Date: 2016–02
  6. By: Michael Benzaquen; Jonathan Donier; Jean-Philippe Bouchaud
    Abstract: We confirm and substantially extend the recent empirical result of Andersen et al. (2015), where it is shown that the amount of risk $W$ exchanged in the E-mini S&P futures market (i.e. price times volume times volatility) scales like the 3/2 power of the number of trades $N$. We show that this 3/2-law holds very precisely across 12 futures contracts and 300 single US stocks, and across a wide range of times scales. However, we find that the "trading invariant" $I=W/N^{3/2}$ proposed by Kyle and Obfizhaeva (2010) is in fact quite different for different contracts, in particular between futures and single stocks. Our analysis suggests $I/S$ as a more natural candidate, where $S$ is the bid-ask spread. We also establish two more complex scaling laws for the volatility $\sigma$ and the traded volume $V$ as a function of $N$, that reveal the existence of a characteristic number of trades $N_0$ above which the expected behaviour $\sigma \sim \sqrt{N}$ and $V \sim N$ hold, but below which strong deviations appear, induced by the size of the tick.
    Date: 2016–02
  7. By: Abdi, Farshid; Ranaldo, Angelo
    Abstract: Using readily available data on daily close, high, and low prices, we develop a straightforward method to estimate the bid-ask spread. Compared with other spread estimators, our method is simpler and has an intuitive closed-form solution without need of further approximations. We test our method numerically and empirically using the Trade and Quotes (TAQ) data. Assessed against other daily estimates, our estimator generally provides the highest cross-sectional and average time-series correlation with the TAQ effective spread benchmark, as well as smallest predictions errors. To illustrate some potential applications, we show that our estimator improves the measurement of systematic liquidity risk and commonality in liquidity.
    Date: 2016–01
  8. By: Masashi Ieda
    Abstract: In the present paper, we study the optimal execution problem under stochastic price recovery based on limit order book dynamics. We model price recovery after execution of a large order by accelerating the arrival of the refilling order, which is defined as a Cox process whose intensity increases by the degree of the market impact. We include not only the market order but also the limit order in our strategy in a restricted fashion. We formulate the problem as a combined stochastic control problem over a finite time horizon. The corresponding Hamilton-Jacobi-Bellman quasi-variational inequality is solved numerically. The optimal strategy obtained consists of three components: (i) the initial large trade; (ii) the unscheduled small trades during the period; (iii) the terminal large trade. The size and timing of the trade is governed by the tolerance for market impact depending on the state at each time step, and hence the strategy behaves dynamically. We also provide competitive results due to inclusion of the limit order, even though a limit order is allowed under conservative evaluation of the execution price.
    Date: 2015–02
  9. By: Michael Ho; Jack Xin
    Abstract: Estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns, market microstructure noise and jumps. One technique for addressing both asynchronous returns and market microstructure is the Kalman-EM (KEM) algorithm. However the KEM approach assumes log-normal prices and does not address jumps in the return process which can corrupt estimation of the covariance matrix. In this paper we extend the KEM algorithm to price models that include jumps. We propose two sparse Kalman filtering approaches to this problem. In the first approach we develop a Kalman Expectation Conditional Maximization (KECM) algorithm to determine the unknown covariance as well as detecting the jumps. For this algorithm we consider Laplace and the spike and slab jump models, both of which promote sparse estimates of the jumps. In the second method we take a Bayesian approach and use Gibbs sampling to sample from the posterior distribution of the covariance matrix under the spike and slab jump model. Numerical results using simulated data show that each of these approaches provide for improved covariance estimation relative to the KEM method in a variety of settings where jumps occur.
    Date: 2016–02

This nep-mst issue is ©2016 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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