Search results for "J2"

showing 10 items of 137 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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Ergodicity for a stochastic Hodgkin–Huxley model driven by Ornstein–Uhlenbeck type input

2013

We consider a model describing a neuron and the input it receives from its dendritic tree when this input is a random perturbation of a periodic deterministic signal, driven by an Ornstein-Uhlenbeck process. The neuron itself is modeled by a variant of the classical Hodgkin-Huxley model. Using the existence of an accessible point where the weak Hoermander condition holds and the fact that the coefficients of the system are analytic, we show that the system is non-degenerate. The existence of a Lyapunov function allows to deduce the existence of (at most a finite number of) extremal invariant measures for the process. As a consequence, the complexity of the system is drastically reduced in c…

Statistics and ProbabilityDegenerate diffusion processesWeak Hörmander conditionType (model theory)01 natural sciencesPeriodic ergodicity010104 statistics & probability60H0760J25FOS: Mathematics0101 mathematicsComputingMilieux_MISCELLANEOUSMathematical physicsMathematics60J60Quantitative Biology::Neurons and CognitionProbability (math.PR)010102 general mathematicsErgodicityOrnstein–Uhlenbeck processHodgkin–Huxley model[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Hodgkin–Huxley model60J60 60J25 60H07Statistics Probability and UncertaintyTime inhomogeneous diffusion processesMathematics - Probability
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R&D, Competition and Growth with Human Capital Accumulation Revisited

2012

In this paper, we have presented a generalization of Bucci's (2003) model in which have disentangled the monopolistic mark-up in the intermediate goods sector, the intermediate goods share in the final output and the returns to specialization in order to have a better measurement of competition. Indeed, unlike Bucci (2003), in our model, the measure of competition is completely independent of the intermediate goods share in the final output and the returns to specialization. Our main finding is that, unlike Bucci (2003), we show that the competition does not play any role in growth. This result is explained by the complementarity of innovation and human capital assumed in the research produ…

Statistics and ProbabilityEconomics and EconometricsJ24O41technological changejel:D43Endogenous growth; horizontal differentiation; technological change; imperfect competition; human capitalHuman capitaljel:J24MicroeconomicsCompetition (economics)jel:O41Monopolistic competitionhorizontal differentiationSpecialization (functional)ddc:330Per capitaEconomicsProduction (economics)[ SHS.ECO ] Humanities and Social Sciences/Economies and financesimperfect competitionhuman capital[SHS.ECO] Humanities and Social Sciences/Economics and FinanceComputingMilieux_MISCELLANEOUSO31Endogenous growth theory[SHS.ECO]Humanities and Social Sciences/Economics and FinanceL16Endogenous growthjel:O31jel:L16HUMAN CAPITALImperfect competitionD43
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Lieu de résidence et discrimination salariale

2010

Ziel dieses Artikels ist es, die Lohnabweichungen zwischen Jugendlichen, die in sensiblen städtischen Zonen wohnen, am Ende ihrer Ausbildung und denjenigen, die zwar nicht in einer solchen Zone leben, die aber in städtischen Einheiten mit solchen Zonen wohnen, unter Berücksichtigung möglicher Barrieren beim Zugang zu bestimmten Beschäftigungen und insbesondere zu den Arbeitsplätzen von Führungskräften zu untersuchen. In Anknüpfung an Brown, Moon und Zoloth (1980) schlagen wir eine Zerlegung der Lohnabweichungen vor, bei der die Möglichkeit einer Differenzierung beim Zugang zu bestimmten Beschäftigungen entsprechend der Art des Stadtviertels, in dem die Jugendlichen wohnen, berücksichtigt wi…

Statistics and ProbabilityEconomics and EconometricsZone urbaineSociology and Political ScienceInégalité salariale[SHS.EDU]Humanities and Social Sciences/Educationdiscrimination territoriale0211 other engineering and technologies02 engineering and technologyJEL: J - Labor and Demographic Economics/J.J7 - Labor Discrimination/J.J7.J71 - DiscriminationZone sensibleJEL: J - Labor and Demographic Economics/J.J2 - Demand and Supply of Labor/J.J2.J24 - Human Capital • Skills • Occupational Choice • Labor Productivitydiscrimination salarialediscrimination territorialecapital humainZone Urbaine Sensible11. Sustainability0502 economics and business[ SHS.ECO ] Humanities and Social Sciences/Economies and finances050207 economics[SHS.ECO] Humanities and Social Sciences/Economics and Financediscrimination salarialeDiscrimination racialecapital humainEffet05 social sciencesAccès à l'emploi021107 urban & regional planning[SHS.ECO]Humanities and Social Sciences/Economics and FinanceZone Urbaine SensibleSecteur résidentiel8. Economic growthJEL : J - Labor and Demographic Economics/J.J2 - Demand and Supply of Labor/J.J2.J24 - Human Capital • Skills • Occupational Choice • Labor ProductivityJeuneJEL : J - Labor and Demographic Economics/J.J7 - Labor Discrimination/J.J7.J71 - Discrimination
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Establishing some order amongst exact approximations of MCMCs

2016

Exact approximations of Markov chain Monte Carlo (MCMC) algorithms are a general emerging class of sampling algorithms. One of the main ideas behind exact approximations consists of replacing intractable quantities required to run standard MCMC algorithms, such as the target probability density in a Metropolis-Hastings algorithm, with estimators. Perhaps surprisingly, such approximations lead to powerful algorithms which are exact in the sense that they are guaranteed to have correct limiting distributions. In this paper we discover a general framework which allows one to compare, or order, performance measures of two implementations of such algorithms. In particular, we establish an order …

Statistics and ProbabilityFOS: Computer and information sciences65C05Mathematical optimizationMonotonic function01 natural sciencesStatistics - ComputationPseudo-marginal algorithm010104 statistics & probabilitysymbols.namesake60J05martingale couplingalgoritmitFOS: MathematicsApplied mathematics60J220101 mathematicsComputation (stat.CO)Mathematics65C40 (Primary) 60J05 65C05 (Secondary)Martingale couplingMarkov chainmatematiikkapseudo-marginal algorithm010102 general mathematicsProbability (math.PR)EstimatorMarkov chain Monte Carloconvex orderDelta methodMarkov chain Monte CarloOrder conditionsymbolsStatistics Probability and UncertaintyAsymptotic variance60E15Martingale (probability theory)Convex orderMathematics - ProbabilityGibbs sampling
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Can the Adaptive Metropolis Algorithm Collapse Without the Covariance Lower Bound?

2011

The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix at step $n+1$ \[ S_n = Cov(X_1,...,X_n) + \epsilon I, \] that is, the sample covariance matrix of the history of the chain plus a (small) constant $\epsilon>0$ multiple of the identity matrix $I$. The lower bound on the eigenvalues of $S_n$ induced by the factor $\epsilon I$ is theoretically convenient, but practically cumbersome, as a good value for the parameter $\epsilon$ may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitly bound the eigenvalues of $S_n$ away …

Statistics and ProbabilityFOS: Computer and information sciencesIdentity matrixMathematics - Statistics TheoryStatistics Theory (math.ST)Upper and lower boundsStatistics - Computation93E3593E15Combinatorics60J27Mathematics::ProbabilityLaw of large numbers65C40 60J27 93E15 93E35stochastic approximationFOS: MathematicsEigenvalues and eigenvectorsComputation (stat.CO)Metropolis algorithmMathematicsProbability (math.PR)Zero (complex analysis)CovariancestabilityUniform continuityBounded function65C40Statistics Probability and Uncertaintyadaptive Markov chain Monte CarloMathematics - Probability
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Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance

2017

We establish an ordering criterion for the asymptotic variances of two consistent Markov chain Monte Carlo (MCMC) estimators: an importance sampling (IS) estimator, based on an approximate reversible chain and subsequent IS weighting, and a standard MCMC estimator, based on an exact reversible chain. Essentially, we relax the criterion of the Peskun type covariance ordering by considering two different invariant probabilities, and obtain, in place of a strict ordering of asymptotic variances, a bound of the asymptotic variance of IS by that of the direct MCMC. Simple examples show that IS can have arbitrarily better or worse asymptotic variance than Metropolis-Hastings and delayed-acceptanc…

Statistics and ProbabilityFOS: Computer and information sciencesdelayed-acceptanceMarkovin ketjut01 natural sciencesStatistics - Computationasymptotic variance010104 statistics & probabilitysymbols.namesake60J22 65C05unbiased estimatorFOS: MathematicsApplied mathematics0101 mathematicsComputation (stat.CO)stokastiset prosessitestimointiMathematicsnumeeriset menetelmätpseudo-marginal algorithmApplied Mathematics010102 general mathematicsProbability (math.PR)EstimatorMarkov chain Monte CarloCovarianceInfimum and supremumWeightingMarkov chain Monte CarloMonte Carlo -menetelmätDelta methodimportance samplingModeling and SimulationBounded functionsymbolsImportance samplingMathematics - Probability
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Wardowski conditions to the coincidence problem

2015

In this article we first discuss the existence and uniqueness of a solution for the coincidence problem: Find p ∈ X such that Tp = Sp, where X is a nonempty set, Y is a complete metric space, and T, S:X → Y are two mappings satisfying a Wardowski type condition of contractivity. Later on, we will state the convergence of the Picard-Juncgk iteration process to the above coincidence problem as well as a rate of convergence for this iteration scheme. Finally, we shall apply our results to study the existence and uniqueness of a solution as well as the convergence of the Picard-Juncgk iteration process toward the solution of a second order differential equation. Ministerio de Economía y Competi…

Statistics and ProbabilityIterative methodsIterative methodCoincidence pointsComplete metric space54H25common fixed pointsConvergence (routing)Applied mathematicsUniquenessMathematicsApplied Mathematics and Statistics47J25lcsh:T57-57.97Applied MathematicsMathematical analysisOrder (ring theory)State (functional analysis)Rate of convergencecoincidence pointsRate of convergenceordinary differential equationsOrdinary differential equationlcsh:Applied mathematics. Quantitative methodsCommon fixed pointsiterative methodslcsh:Probabilities. Mathematical statisticslcsh:QA273-280rate of convergenceFrontiers in Applied Mathematics and Statistics
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Random time-changes and asymptotic results for a class of continuous-time Markov chains on integers with alternating rates

2021

We consider continuous-time Markov chains on integers which allow transitions to adjacent states only, with alternating rates. We give explicit formulas for probability generating functions, and also for means, variances and state probabilities of the random variables of the process. Moreover we study independent random time-changes with the inverse of the stable subordinator, the stable subordinator and the tempered stable subodinator. We also present some asymptotic results in the fashion of large deviations. These results give some generalizations of those presented in Di Crescenzo A., Macci C., Martinucci B. (2014).

Statistics and ProbabilityPure mathematicsSubordinatormoderate deviationsInversefractional processfractional process; large deviations; moderate deviations; tempered stable subordinatorlarge deviationsChain (algebraic topology)FOS: MathematicsProbability-generating function60F10 60J27 60G22 60G52MathematicsMarkov chainlcsh:T57-57.97lcsh:MathematicsProbability (math.PR)State (functional analysis)tempered stable subordinatorlcsh:QA1-939Modeling and SimulationSettore MAT/06lcsh:Applied mathematics. Quantitative methodsLarge deviations theoryStatistics Probability and UncertaintyRandom variableMathematics - Probability
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On the stability and ergodicity of adaptive scaling Metropolis algorithms

2011

The stability and ergodicity properties of two adaptive random walk Metropolis algorithms are considered. The both algorithms adjust the scaling of the proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of the algorithms, the adapted scaling parameter is not constrained within a predefined compact interval. The first algorithm is based on scale adaptation only, while the second one incorporates also covariance adaptation. A strong law of large numbers is shown to hold assuming that the target density is smooth enough and has either compact support or super-exponentially decaying tails.

Statistics and ProbabilityStochastic approximationMathematics - Statistics TheoryStatistics Theory (math.ST)Law of large numbersMultiple-try Metropolis01 natural sciencesStability (probability)010104 statistics & probabilityModelling and Simulation65C40 60J27 93E15 93E35Adaptive Markov chain Monte CarloFOS: Mathematics0101 mathematicsScalingMetropolis algorithmMathematicsta112Applied Mathematics010102 general mathematicsRejection samplingErgodicityProbability (math.PR)ta111CovarianceRandom walkMetropolis–Hastings algorithmModeling and SimulationAlgorithmStabilityMathematics - ProbabilityStochastic Processes and their Applications
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