Search results for "FOS: Economics and business"

showing 10 items of 145 documents

Taxonomy of stock market indices

2000

We investigate sets of financial non-redundant and nonsynchronously recorded time series. The sets are composed by a number of stock market indices located all over the world in five continents. By properly selecting the time horizon of returns and by using a reference currency we find a meaningful taxonomy. The detection of such a taxonomy proves that interpretable information can be stored in a set of nonsynchronously recorded time series.

Statistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Series (mathematics)Computer scienceQuantitative Finance - Statistical FinanceFOS: Physical sciencesTime horizoncomputer.software_genreStock market indexFOS: Economics and businessSet (abstract data type)CurrencyTaxonomy (general)EconometricsData miningTime seriescomputerCondensed Matter - Statistical MechanicsPhysical Review E
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Power-law relaxation in a complex system: Omori law after a financial market crash

2003

We study the relaxation dynamics of a financial market just after the occurrence of a crash by investigating the number of times the absolute value of an index return is exceeding a given threshold value. We show that the empirical observation of a power law evolution of the number of events exceeding the selected threshold (a behavior known as the Omori law in geophysics) is consistent with the simultaneous occurrence of (i) a return probability density function characterized by a power law asymptotic behavior and (ii) a power law relaxation decay of its typical scale. Our empirical observation cannot be explained within the framework of simple and widespread stochastic volatility models.

Statistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Stochastic volatilityStochastic processFOS: Physical sciencesQuantitative Finance - Statistical FinanceAbsolute valueCrashProbability density functionPower lawFOS: Economics and businessLawEconometricsRelaxation (physics)Time seriesCondensed Matter - Statistical MechanicsMathematicsPhysical Review E
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Statistical Properties of Statistical Ensembles of Stock Returns

1999

We select n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fluctuating in time and are stochastic processes themselves. We characterize the statistical properties of central moments by investigating their probability density function and temporal correlation properties.

Statistical ensemblePhysics::Physics and SocietyStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Stochastic processFinancial economicsQuantitative Finance - Statistical FinanceFOS: Physical sciencesProbability density functionTemporal correlationStock priceFOS: Economics and businessStock exchangeComputer Science::Computational Engineering Finance and ScienceEconomicsEconometricsGeneral Economics Econometrics and FinanceFinanceStock (geology)Condensed Matter - Statistical Mechanics
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Variety and volatility in financial markets

2000

We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the $n$ stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each trading day of our database, we study the ensemble return distribution. We find that a typical ensemble return distribution exists in most of the trading days with the exception of crash and rally days and of the days subsequent to these extreme events. We analyze each ensemble return distribution by extracting its first two central moments. We observe that these moments are fluctua…

Statistical ensembleStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Stochastic processFinancial marketQuantitative Finance - Statistical FinanceFOS: Physical sciencesProbability density functionRelative strengthFOS: Economics and businessStock exchangeEconometricsVolatility (finance)Condensed Matter - Statistical MechanicsStock (geology)MathematicsPhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
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An interest rates cluster analysis

2004

An empirical analysis of interest rates in money and capital markets is performed. We investigate a set of 34 different weekly interest rate time series during a time period of 16 years between 1982 and 1997. Our study is focused on the collective behavior of the stochastic fluctuations of these time-series which is investigated by using a clustering linkage procedure. Without any a priori assumption, we individuate a meaningful separation in 6 main clusters organized in a hierarchical structure.

Statistics and ProbabilityCollective behaviormedia_common.quotation_subjectFOS: Physical sciencesLinkage (mechanical)computer.software_genrelaw.inventionFOS: Economics and businesslawEconometricsCluster (physics)Cluster analysisCondensed Matter - Statistical Mechanicsmedia_commonStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)EconophysicsSeries (mathematics)Quantitative Finance - Statistical FinanceCondensed Matter PhysicsInterest rateCondensed Matter - Other Condensed MatterData miningCapital marketcomputerOther Condensed Matter (cond-mat.other)
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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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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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Multivariate GARCH estimation via a Bregman-proximal trust-region method

2011

The estimation of multivariate GARCH time series models is a difficult task mainly due to the significant overparameterization exhibited by the problem and usually referred to as the "curse of dimensionality". For example, in the case of the VEC family, the number of parameters involved in the model grows as a polynomial of order four on the dimensionality of the problem. Moreover, these parameters are subjected to convoluted nonlinear constraints necessary to ensure, for instance, the existence of stationary solutions and the positive semidefinite character of the conditional covariance matrices used in the model design. So far, this problem has been addressed in the literature only in low…

Statistics and ProbabilityMathematical optimizationPolynomialComputer scienceDiagonalComputational Finance (q-fin.CP)[QFIN.CP]Quantitative Finance [q-fin]/Computational Finance [q-fin.CP]FOS: Economics and businessQuantitative Finance - Computational FinanceDimension (vector space)0502 economics and business91G70 65C60050207 economicsMathematics050205 econometrics Trust regionStatistical Finance (q-fin.ST)Series (mathematics)Applied Mathematics05 social sciencesConstrained optimizationQuantitative Finance - Statistical Finance[QFIN.ST]Quantitative Finance [q-fin]/Statistical Finance [q-fin.ST]Computational MathematicsNonlinear systemComputational Theory and MathematicsParametrizationCurse of dimensionality
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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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