Search results for "Statistical finance"

showing 10 items of 52 documents

Networks of equities in financial markets

2004

We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the method can be used to falsify widespread market models by directly comparing the topological properties of networks of real and artificial markets.

Statistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Financial marketINDEXESFOS: Physical sciencesQuantitative Finance - Statistical FinanceCondensed Matter PhysicsElectronic Optical and Magnetic MaterialsSettore FIS/02 - Fisica Teorica Modelli e Metodi MatematiciFOS: Economics and businessEconomic informationDYNAMIC ASSET TREESEconometricsEconomicsPortfolioVolatility (finance)INTERNETVOLATILITYCondensed Matter - Statistical Mechanics
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Hierarchical Structure in Financial Markets

1998

I find a topological arrangement of stocks traded in a financial market which has associated a meaningful economic taxonomy. The topological space is a graph connecting the stocks of the portfolio analyzed. The graph is obtained starting from the matrix of correlation coefficient computed between all pairs of stocks of the portfolio by considering the synchronous time evolution of the difference of the logarithm of daily stock price. The hierarchical tree of the subdominant ultrametric space associated with the graph provides information useful to investigate the number and nature of the common economic factors affecting the time evolution of logarithm of price of well defined groups of sto…

Statistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)LogarithmFinancial marketStructure (category theory)Quantitative Finance - Statistical FinanceFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksTopological spaceCondensed Matter PhysicsTree (graph theory)Electronic Optical and Magnetic MaterialsFOS: Economics and businessComputer Science::Computational Engineering Finance and ScienceEconometricsGraph (abstract data type)PortfolioUltrametric spaceCondensed Matter - Statistical MechanicsMathematics
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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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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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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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