Search results for " Market microstructure"

showing 10 items of 29 documents

El componente de selección adversa de la horquilla de precios cotizada: una revisión de los modelos de estimación

2005

-Jose.E.Farinos@uv.es -Ana.M.Ibanez@uv.es Una de las principales preocupaciones en el área de la microestructura del mercado ha sido la estimación de los componentes no observables de la horquilla de precios a partir de las series de datos que proporcionan los mercados financieros, despertando quizá un mayor interés el de selección adversa por la implicaciones que supone la existencia del mismo. Esto ha provocado el desarrollo de numerosos modelos empíricos que, basándose en las propiedades estadísticas de las series de precios, proporcionan dichas estimaciones. La mayor disponibilidad de datos existentes en los mercados ha permitido el desarrollo en los últimos años de modelos basados en t…

Microestructura de los mercados financieros; Negociación informada; Horquilla de precios; Selección adversa; Costes de transacciónmarket microstructureselección adversajel:D82spreadinsider tradingFINANCIAL ECONOMICSadverse selection componentG12G34microestructura de los mercados financieros:CIENCIAS ECONÓMICAS::Economía sectorial::Finanzas y seguros [UNESCO]ECONOMICSORGANIZATIONAL BEHAVIOR AND HUMAN RESOURCE MANAGEMENTBUSINESS AND INTERNATIONAL MANAGEMENTUNESCO::CIENCIAS ECONÓMICAS::Economía sectorial::Finanzas y segurosjel:G12jel:G34Costes de transacciónD82MICROECONOMICSmicroestructura de los mercados financieros negociación informada horquilla de precios selección adversa market microstructure insider trading spread adverse selection component transaction costtransaction costhorquilla de preciosINDUSTRIAL RELATIONS AND LABORSTRATEGY AND MANAGEMENTnegociación informada
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Diffusive behavior and the modeling of characteristic times in limit order executions

2007

We present an empirical study of the first passage time (FPT) of order book prices needed to observe a prescribed price change Delta, the time to fill (TTF) for executed limit orders and the time to cancel (TTC) for canceled ones in a double auction market. We find that the distribution of all three quantities decays asymptotically as a power law, but that of FPT has significantly fatter tails than that of TTF. Thus a simple first passage time model cannot account for the observed TTF of limit orders. We propose that the origin of this difference is the presence of cancellations. We outline a simple model, which assumes that prices are characterized by the empirically observed distribution …

Physics - Physics and SocietyFOS: Physical sciencesPhysics and Society (physics.soc-ph)Power lawFOS: Economics and businessOrder bookTime to fillLimit (mathematics)Statistical physicsMicrostructureMathematicsQuantitative Finance - Trading and Market MicrostructureEconophysicsLimit order marketEconophysicProbability and statisticsFirst passage timeTrading and Market Microstructure (q-fin.TR)Distribution (mathematics)Physics - Data Analysis Statistics and ProbabilityExponentCensored dataFirst-hitting-time modelGeneral Economics Econometrics and FinanceFinanceData Analysis Statistics and Probability (physics.data-an)
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Limit order placement as an utility maximization problem and the origin of power law distribution of limit order prices

2006

I consider the problem of the optimal limit order price of a financial asset in the framework of the maximization of the utility function of the investor. The analytical solution of the problem gives insight on the origin of the recently empirically observed power law distribution of limit order prices. In the framework of the model, the most likely proximate cause of this power law is a power law heterogeneity of traders' investment time horizons .

Physics - Physics and SocietyQuantitative Finance - Trading and Market MicrostructureFinancial assetFOS: Physical sciencesFunction (mathematics)MaximizationPhysics and Society (physics.soc-ph)Condensed Matter PhysicsInvestment (macroeconomics)Power lawElectronic Optical and Magnetic MaterialsTrading and Market Microstructure (q-fin.TR)FOS: Economics and businesssymbols.namesakeProximate and ultimate causationUtility maximization problemsymbolsEconometricsEconomicsPareto distributioneconophysics financial markets business and management
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How does the market react to your order flow?

2012

We present an empirical study of the intertwined behaviour of members in a financial market. Exploiting a database where the broker that initiates an order book event can be identified, we decompose the correlation and response functions into contributions coming from different market participants and study how their behaviour is interconnected. We find evidence that (1) brokers are very heterogeneous in liquidity provision -- some are consistently liquidity providers while others are consistently liquidity takers. (2) The behaviour of brokers is strongly conditioned on the actions of {\it other} brokers. In contrast brokers are only weakly influenced by the impact of their own previous ord…

Physics - Physics and SocietyQuantitative Finance - Trading and Market MicrostructureMarket microstructureLimit order marketFinancial marketFOS: Physical sciencesBehavioural financePhysics and Society (physics.soc-ph)Market microstructureMonetary economicsMarket dynamicsFinancial marketFinancial markets microstructure Econophysics stochasti processesTrading and Market Microstructure (q-fin.TR)Market liquidityFOS: Economics and businessCompetition (economics)Empirical researchOrder (exchange)Physics - Data Analysis Statistics and ProbabilityOrder bookBusinessGeneral Economics Econometrics and FinanceData Analysis Statistics and Probability (physics.data-an)FinanceQuantitative Finance
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A theory for long-memory in supply and demand

2004

Recent empirical studies have demonstrated long-memory in the signs of orders to buy or sell in financial markets [2, 19]. We show how this can be caused by delays in market clearing. Under the common practice of order splitting, large orders are broken up into pieces and executed incrementally. If the size of such large orders is power law distributed, this gives rise to power law decaying autocorrelations in the signs of executed orders. More specifically, we show that if the cumulative distribution of large orders of volume v is proportional to v to the power -alpha and the size of executed orders is constant, the autocorrelation of order signs as a function of the lag tau is asymptotica…

PhysicsPhysics - Physics and SocietyActuarial scienceQuantitative Finance - Trading and Market MicrostructureCumulative distribution functionAutocorrelationFOS: Physical sciencesOrder (ring theory)Physics and Society (physics.soc-ph)Function (mathematics)Trading and Market Microstructure (q-fin.TR)FOS: Economics and businessCombinatoricsCondensed Matter - Other Condensed MatterExecution Commerce optimal liquidationLong memoryDiffusion (business)Constant (mathematics)Other Condensed Matter (cond-mat.other)
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Stock markets and quantum dynamics: A second quantized description

2009

In this paper we continue our description of stock markets in terms of some non-abelian operators which are used to describe the portfolio of the various traders and other observable quantities. After a first prototype model with only two traders, we discuss a more realistic model of market involving an arbitrary number of traders. For both models we find approximated solutions for the time evolution of the portfolio of each trader. In particular, for the more realistic model, we use the stochastic limit approach and a fixed point like approximation. © 2007 Elsevier B.V. All rights reserved

Physics::Physics and SocietyStatistics and ProbabilitySecond quantizationComputer Science::Computer Science and Game TheoryQuantitative Finance - Trading and Market MicrostructureQuantum dynamicQuantum dynamicsTime evolutionObservableStock marketsFixed pointCondensed Matter PhysicsSecond quantizationTrading and Market Microstructure (q-fin.TR)FOS: Economics and businessComputer Science::Multiagent SystemsComputer Science::Computational Engineering Finance and SciencePortfolioStatistical physicsSettore MAT/07 - Fisica MatematicaMathematical economicsStock (geology)MathematicsPhysica A: Statistical Mechanics and its Applications
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Statistical identification with hidden Markov models of large order splitting strategies in an equity market

2010

Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a net majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transactions size distributions of …

Quantitative Finance - Trading and Market Microstructuremedia_common.quotation_subjectFinancial marketEquity (finance)General Physics and AstronomyMarket trendAsymmetryTrading and Market Microstructure (q-fin.TR)FOS: Economics and businessStock exchangeEconometricsEconophysics Financial markets Hidden Markov ModelsSegmentationHidden Markov modelmedia_commonMathematics
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Multiscale Model Selection for High-Frequency Financial Data of a Large Tick Stock by Means of the Jensen–Shannon Metric

2014

Modeling financial time series at different time scales is still an open challenge. The choice of a suitable indicator quantifying the distance between the model and the data is therefore of fundamental importance for selecting models. In this paper, we propose a multiscale model selection method based on the Jensen–Shannon distance in order to select the model that is able to better reproduce the distribution of price changes at different time scales. Specifically, we consider the problem of modeling the ultra high frequency dynamics of an asset with a large tick-to-price ratio. We study the price process at different time scales and compute the Jensen–Shannon distance between the original…

Return distributionFinancemodel selectionComputer sciencebusiness.industryEntropy High frequency data Financial markets Market microstructureModel selectionGeneral Physics and AstronomyRanginglcsh:Astrophysicsmultiscale analysimultiscale analysisJensen–Shannon divergencelcsh:QC1-999Markov-switching modelinglcsh:QB460-466EconometricsJensen–Shannon divergencelcsh:Qbusinesslcsh:ScienceStock (geology)high frequency financial datalcsh:PhysicsEntropy
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Identification of clusters of investors from their real trading activity in a financial market

2012

We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that many statistically detected clusters of investors show a very high degree of synchronization in the time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and we find that several of them show an over-expression of specific categories of investors.

Social and Information Networks (cs.SI)FOS: Computer and information sciencesPhysicsPhysics - Physics and SocietyQuantitative Finance - Trading and Market MicrostructureBipartite systemFinancial marketFOS: Physical sciencesGeneral Physics and AstronomyNetworkComputer Science - Social and Information NetworksPhysics and Society (physics.soc-ph)tradingComplex networkBipartite systemTrading and Market Microstructure (q-fin.TR)FOS: Economics and businessIdentification (information)big dataSynchronization (computer science)EconometricsNetworks Bipartite systems Financial MarketsFinancial MarketsStock (geology)clustering
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Modeling the coupled return-spread high frequency dynamics of large tick assets

2015

Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We introduce a Markov-switching modeling approach for price change, where the latent Markov process is the transition between spreads. We then use a finite Markov mixture of logit regressions on past squared returns to describe the dependence of the probability of price changes. The model can thus be seen as a Double Chain Markov Model. We show that the model describes the shape of return distribution at different time aggregations, volatility clustering, and the anomalo…

Statistics and ProbabilityComputer Science::Computer Science and Game TheoryVolatility clusteringQuantitative Finance - Trading and Market MicrostructureMarkov chainLogitMarkov processStatistical and Nonlinear PhysicsMarkov modelmodels of financial markets nonlinear dynamics stochastic processesTrading and Market Microstructure (q-fin.TR)FOS: Economics and businesssymbols.namesakesymbolsEconometricsKurtosisFraction (mathematics)Almost surelyStatistics Probability and Uncertainty60J20Mathematics
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