Search results for "Econometric"

showing 10 items of 3780 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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Properties of the elasticity of a continuous random variable. A special look at its behavior and speed of change

2016

ABSTRACTBelzunce et al. (1995) define the elasticity for non negative random variables as the reversed proportional failure rate (RPFR). Veres-Ferrer and Pavia (2012, 2014b) interpret it in economic terms, extending its definition to variables that can also take negative values, and briefly present the role of elasticity in characterizing probability distributions. This paper highlights a set of properties demonstrated by elasticity, which shows many similar properties to the reverse hazard function. This paper pays particular attention to studying the increase/decrease and the speed of change of the elasticity function. These are important properties because of the characterizing role of e…

Statistics and Probability021103 operations researchStochastic process0211 other engineering and technologiesFailure rate02 engineering and technology01 natural sciencesElasticity of a function010104 statistics & probabilitysymbols.namesakeEconometricssymbolsProbability distribution0101 mathematicsElasticity (economics)Fisher informationRandom variableMathematicsCommunications in Statistics - Theory and Methods
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An association model for bivariate data with application to the anlysis of university students' success.

2015

The academic success of students is a priority for all universities. We analyze the students' success at university by considering their performance in terms of both ‘qualitative performance’, measured by their mean grade, and ‘quantitative performance’, measured by university credits accumulated. These data come from an Italian University and concern a cohort of students enrolled at the Faculty of Economics. To jointly model both the marginal relationships and the association structure with covariates, we fit a bivariate ordered logistic model by penalized maximum likelihood estimation. The penalty term we use allows us to smooth the association structure and enlarge the range of possible …

Statistics and Probability05 social sciencesBivariate analysisLogistic regression01 natural sciencesTerm (time)010104 statistics & probabilityGoodness of fitBivariate data0502 economics and businessStatisticsCovariateEconometricsRange (statistics)Settore SECS-S/05 - Statistica Sociale050207 economics0101 mathematicsStatistics Probability and UncertaintyAssociation (psychology)Mathematicsmodels for association students' performance bivariate ordinal response Dale's model maximum penalized likelihood estimation
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Second‐order analysis of marked inhomogeneous spatiotemporal point processes: Applications to earthquake data

2018

To analyse interactions in marked spatio-temporal point processes (MSTPPs), we introduce marked second-order reduced moment measures and K-functions for inhomogeneous second-order intensity reweigh ...

Statistics and Probability05 social sciencesMathematical statistics01 natural sciencesPoint processMoment (mathematics)010104 statistics & probabilitySecond order analysis0502 economics and businessStatistical physics0101 mathematicsStatistics Probability and UncertaintyIntensity (heat transfer)050205 econometrics MathematicsScandinavian Journal of Statistics
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What Does Objective Mean in a Dirichlet-multinomial Process?

2017

Summary The Dirichlet-multinomial process can be seen as the generalisation of the binomial model with beta prior distribution when the number of categories is larger than two. In such a scenario, setting informative prior distributions when the number of categories is great becomes difficult, so the need for an objective approach arises. However, what does objective mean in the Dirichlet-multinomial process? To deal with this question, we study the sensitivity of the posterior distribution to the choice of an objective Dirichlet prior from those presented in the available literature. We illustrate the impact of the selection of the prior distribution in several scenarios and discuss the mo…

Statistics and Probability05 social sciencesPosterior probabilityBayesian inference01 natural sciencesDirichlet distributionBinomial distribution010104 statistics & probabilitysymbols.namesake0502 economics and businessStatisticsObjective approachPrior probabilitysymbolsEconometricsMultinomial distribution0101 mathematicsStatistics Probability and UncertaintyBeta distribution050205 econometrics MathematicsInternational Statistical Review
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