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on Market Microstructure |
| By: | Nathalie Oriol (University of Nice Sophia Antipolis, France; GREDEG CNRS); Alexandra Rufini (University of Nice Sophia Antipolis, France; GREDEG CNRS); Dominique Torre (University of Nice Sophia Antipolis, France; GREDEG CNRS) |
| Abstract: | European financial markets experiment a strong competition between historical players and new trading platforms, including the controversial dark pools. Our theoretical setting analyzes the interaction between heterogeneous investors and trading services providers in presence of market externalities. We compare different forms of organization of the market, each in presence of an off-exchange and an incumbent facing a two-sided activity (issuers and investors): a consolidated exchange with the incumbent only, and fragmented exchanges with several platforms, including lit and dark pools, in competition for order ows. By capturing investors from off-exchange, dark trading may enhance market externalities and market stakeholders' welfare. |
| Keywords: | microstructure, dark pools, Over-The-Counter market, liquidity, market externalities, two-sided markets |
| JEL: | G10 D62 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2015-21&r=mst |
| By: | Mark Paddrik (Office of Financial Research); Roy Hayes (University of Virginia); William Scherer (University of Virginia); Peter Beling (University of Virginia) |
| Abstract: | Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model's limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange (NYMEX). Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs. |
| Keywords: | Limit Order Book, Market Stability |
| Date: | 2014–11–25 |
| URL: | http://d.repec.org/n?u=RePEc:ofr:wpaper:14-09&r=mst |
| By: | Lorenzo Camponovo; Yukitoshi Matsushita; Taisuke Otsu |
| Abstract: | We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empirical likelihood and Pearson's chi-square as special cases, is not asymptotically pivotal under the so-called infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. We show that multiplying a correction term recovers the chi-square limiting distribution. Furthermore, we establish Bartlett correction for our modified nonparametric likelihood statistic under the constant and general non-constant volatility cases. In contrast to the existing literature, the empirical likelihood statistic is not Bartlett correctable under the infill asymptotics. However, by choosing adequate tuning constants for the power divergence family, we show that the second order refinement to the order n^2 can be achieved. |
| Keywords: | Nonparametric likelihood, Volatility, High frequency data |
| JEL: | C14 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:581&r=mst |
| By: | Martin Evans (Department of Economics, Georgetown University); Dagfinn Rime (BI Norwegian Business School and Norges Bank, Oslo Norway) |
| Abstract: | This paper examines why order flows are empirically important drivers of spot exchange rate dynamics. We consider a decomposition for the depreciation rate that must hold in any model and show that order flows will appear as important proximate drivers when they convey significant incremental information about future interest rate differentials, risk premiums and/or long-run exchange rate levels (i.e., information that cannot be inferred from publicly observed variables). We estimate the importance of these incremental information flows for the EURNOK spot exchange rate using eight years of high- quality, disaggregated, end-user order flow data collected by the Norges Bank. |
| Keywords: | exchange rate dynamics, microstructure, order flow. |
| JEL: | F3 F4 G1 |
| Date: | 2015–04–21 |
| URL: | http://d.repec.org/n?u=RePEc:geo:guwopa:gueconwpa~15-15-02&r=mst |
| By: | Mark E. Paddrik (Office of Financial Research); Richard Haynes (U.S. Department of the Treasury); Andrew E. Todd (University of Virginia); Peter A. Beling (University of Virginia); William T. Scherer (University of Virginia) |
| Abstract: | Electronic markets and automated trading have resulted in a drastic increase in the quantity and complexity of regulatory data. Reconstructing the limit order book and analyzing order flow is an emerging challenge for financial regulators. New order types, intra-market behavior and other exchange functionality further complicate the task of understanding market behavior at multiple levels. Data visualizations have proven to be a fundamental tool for building intuition and enabling exploratory data analysis in many fields. In this paper, we propose the incorporation of visualizations in the workflow of multiple financial regulatory roles, including market surveillance, enforcement, and supporting academic research. |
| Keywords: | Visualization, Visual Databases, Financial Markets, Law Enforcement |
| Date: | 2014–08–27 |
| URL: | http://d.repec.org/n?u=RePEc:ofr:discus:14-02&r=mst |
| By: | Vladimir Asriyan; William Fuchs; Brett Green |
| Abstract: | We study the effect of information spillovers and transparency in a dynamic setting with adverse selection and correlated asset values. A trade (or lack thereof) by one seller can provide information about the quality of other assets in the market. In equilibrium, the information content of this trading behavior is endogenously determined. We show that this endogeneity of information leads to multiple equilibria when the correlation between asset values is sufficiently high. That is, if buyers expect "bad" assets to trade quickly, then a seller with a bad asset has reason to be concerned about negative information being revealed, which induces her to trade quickly. Conversely, if buyers do not expect bad assets to trade quickly, then the seller has less to be concerned about and is more willing to wait. We study the implications for policies that target market transparency. We show that total welfare is higher when markets are fully transparent than when the market is fully opaque. However, both welfare and trading activity can decrease in the degree of market transparency. |
| Keywords: | asymmetric information, information spillovers, market transparency, liquidity. |
| JEL: | G12 G14 |
| Date: | 2015–04 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1482&r=mst |