Search results for "FINANCIAL MARKET"

showing 10 items of 198 documents

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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Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR

2020

In this paper, we extend the monthly financial stress index for Lithuania, computed by the European Central Bank, to a daily frequency and we also include banking sector stress among its constituents, beyond bond, equity and foreign exchange markets. We investigate the causal relationship between the daily financial stress index and monthly industrial production growth, using a Granger causality test applied to a mixed-frequency VAR. Our results suggest evidence of Granger causality from financial stress to industrial production growth once the index is enriched by daily observations from the financial markets. Our findings, based on impulse response analysis, confirm the negative effect of…

Statistics and ProbabilityEconomics and EconometricsMixed frequencyIndustrial productionBond05 social sciencesFinancial marketEquity (finance)Mathematics (miscellaneous)Granger causalityFinancial stress index0502 economics and businessEconometricsEconomicsGranger causalityFinancial distress050207 economicsReal economyMixed frequency dataSocial Sciences (miscellaneous)050205 econometrics
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Trading leads to scale-free self-organization

2009

Financial markets display scale-free behavior in many different aspects. The power-law behavior of part of the distribution of individual wealth has been recognized by Pareto as early as the nineteenth century. Heavy-tailed and scale-free behavior of the distribution of returns of different financial assets have been confirmed in a series of works. The existence of a Pareto-like distribution of the wealth of market participants has been connected with the scale-free distribution of trading volumes and price-returns. The origin of the Pareto-like wealth distribution, however, remained obscure. Here we show that it is the process of trading itself that under two mild assumptions spontaneously…

Statistics and ProbabilityFactor marketPhysics - Physics and SocietyQuantitative Finance - Trading and Market MicrostructureStatistical Finance (q-fin.ST)Market rateFinancial economicsFinancial marketQuantitative Finance - Statistical FinanceFOS: Physical sciencesPhysics and Society (physics.soc-ph)Market microstructureCondensed Matter Physicscomputer.software_genreDomestic marketTrading and Market Microstructure (q-fin.TR)FOS: Economics and businessOrder (exchange)EconomicsNational wealthAlgorithmic tradingcomputer
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Applications of statistical mechanics to finance

1999

Abstract We discuss some apparently “universal” aspects observed in the empirical analysis of stock price dynamics in financial markets. Specifically we consider (i) the empirical behavior of the return probability density function and (ii) the content of economic information in financial time series.

Statistics and ProbabilityFinanceSeries (mathematics)business.industryFinancial marketProbability density functionStatistical mechanicsStatistical financeCondensed Matter PhysicsMarket depthEconomic informationEconomicsFinancial modelingbusinessPhysica A: Statistical Mechanics and its Applications
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Calibration of optimal execution of financial transactions in the presence of transient market impact

2012

Trading large volumes of a financial asset in order driven markets requires the use of algorithmic execution dividing the volume in many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the price reacts to trades. In this paper we consider a recently introduced market impact model (Bouchaud et al., 2004), which has the property of describing both the volume and the temporal dependence of price change due to trading. We show how this model can be used to describe price impact also in aggregated trade time or in real time. We then solve analytically and calibrate wit…

Statistics and ProbabilityMathematical optimizationQuantitative Finance - Trading and Market MicrostructureStatistical Finance (q-fin.ST)Financial market Econophysics stochastic processesFinancial assetComputer scienceVolume (computing)Efficient frontierQuantitative Finance - Statistical FinanceStatistical and Nonlinear PhysicsRisk neutralTrading and Market Microstructure (q-fin.TR)FOS: Economics and businessOrder (exchange)Financial transactionfinancial instruments and regulation models of financial markets risk measure and managementTransient (computer programming)Statistics Probability and UncertaintyMarket impact
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Hitting Time Distributions in Financial Markets

2006

We analyze the hitting time distributions of stock price returns in different time windows, characterized by different levels of noise present in the market. The study has been performed on two sets of data from US markets. The first one is composed by daily price of 1071 stocks trade for the 12-year period 1987-1998, the second one is composed by high frequency data for 100 stocks for the 4-year period 1995-1998. We compare the probability distribution obtained by our empirical analysis with those obtained from different models for stock market evolution. Specifically by focusing on the statistical properties of the hitting times to reach a barrier or a given threshold, we compare the prob…

Statistics and ProbabilityPhysics - Physics and SocietyAutoregressive conditional heteroskedasticityStock market modelFOS: Physical sciencesPhysics and Society (physics.soc-ph)Langevin-type equationHeston modelEconophysics; Stock market model; Langevin-type equation; Heston model; Complex SystemsFOS: Economics and businessEconometricsMathematicsGeometric Brownian motionStatistical Finance (q-fin.ST)Actuarial scienceEconophysicFinancial marketHitting timeQuantitative Finance - Statistical FinanceComplex SystemsProbability and statisticsCondensed Matter PhysicsSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Heston modelPhysics - Data Analysis Statistics and ProbabilityProbability distributionStock marketData Analysis Statistics and Probability (physics.data-an)
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The adaptive nature of liquidity taking in limit order books

2014

In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A possible solution is provided by asymmetric liquidity, which states that the impact of a buy or sell order is inversely related to the probability of its occurrence. We empirically find that when the order flow predictability increases in one direction, the liquidity in the opposite side decreases, but the probability that a trade moves the price decreases significantly. While the…

Statistics and ProbabilityQuantitative Finance - Trading and Market MicrostructureStatistical Finance (q-fin.ST)Limit order book econophysics market efficiencyfinancial instruments and regulationAutocorrelationFinancial marketQuantitative Finance - Statistical FinanceStatistical and Nonlinear PhysicsProbability and statisticsTrading and Market Microstructure (q-fin.TR)Market liquidityFOS: Economics and businessFlow (mathematics)Order (exchange)risk measure and managementOrder bookEconomicsEconometricsmodels of financial marketStatistics Probability and UncertaintyPredictabilityStatistical and Nonlinear Physic
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Investigation of Simulated Trading — A multi agent based trading system for optimization purposes

2010

Abstract Some years ago, Bachem, Hochstattler, and Malich proposed a heuristic algorithm called Simulated Trading for the optimization of vehicle routing problems. Computational agents place buy-orders and sell-orders for customers to be handled at a virtual financial market, the prices of the orders depending on the costs of inserting the customer in the tour or for his removal. According to a proposed rule set, the financial market creates a buy-and-sell graph for the various orders in the order book, intending to optimize the overall system. Here I present a thorough investigation for the application of this algorithm to the traveling salesman problem.

Statistics and ProbabilitySet (abstract data type)Mathematical optimizationHeuristic (computer science)Computer scienceMulti-agent systemVehicle routing problemFinancial marketOrder bookGraph (abstract data type)2-optCondensed Matter PhysicsTravelling salesman problemPhysica A: Statistical Mechanics and its Applications
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Dynamics of a financial market index after a crash

2002

We discuss the statistical properties of index returns in a financial market just after a major market crash. The observed non-stationary behavior of index returns is characterized in terms of the exceedances over a given threshold. This characterization is analogous to the Omori law originally observed in geophysics. By performing numerical simulations and theoretical modelling, we show that the nonlinear behavior observed in real market crashes cannot be described by a GARCH(1,1) model. We also show that the time evolution of the Value at Risk observed just after a major crash is described by a power-law function lacking a typical scale.

Statistics and ProbabilityStatistical Finance (q-fin.ST)Index (economics)Actuarial scienceStatistical Mechanics (cond-mat.stat-mech)EconophysicsScale (ratio)Autoregressive conditional heteroskedasticityFinancial marketFOS: Physical sciencesQuantitative Finance - Statistical FinanceCrashFunction (mathematics)Condensed Matter PhysicsFOS: Economics and businessEconophysicsFinancial marketsCrashesValue at RiskEconometricsEconomicsCondensed Matter - Statistical MechanicsValue at riskPhysica A: Statistical Mechanics and its Applications
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Volatility in Financial Markets: Stochastic Models and Empirical Results

2002

We investigate the historical volatility of the 100 most capitalized stocks traded in US equity markets. An empirical probability density function (pdf) of volatility is obtained and compared with the theoretical predictions of a lognormal model and of the Hull and White model. The lognormal model well describes the pdf in the region of low values of volatility whereas the Hull and White model better approximates the empirical pdf for large values of volatility. Both models fails in describing the empirical pdf over a moderately large volatility range.

Statistics and ProbabilityStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Stochastic modellingEconophysicFinancial marketFOS: Physical sciencesQuantitative Finance - Statistical FinanceStatistical and Nonlinear PhysicsProbability density functionStochastic processeCondensed Matter PhysicsEmpirical probabilitySettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)FOS: Economics and businessVolatilityLognormal modelHullEconomicsEconometricsMathematical PhysicVolatility (finance)Condensed Matter - Statistical Mechanics
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