Search results for "Statistical finance"

showing 10 items of 52 documents

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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Degree stability of a minimum spanning tree of price return and volatility

2002

We investigate the time series of the degree of minimum spanning trees obtained by using a correlation based clustering procedure which is starting from (i) asset return and (ii) volatility time series. The minimum spanning tree is obtained at different times by computing correlation among time series over a time window of fixed length $T$. We find that the minimum spanning tree of asset return is characterized by stock degree values, which are more stable in time than the ones obtained by analyzing a minimum spanning tree computed starting from volatility time series. Our analysis also shows that the degree of stocks has a very slow dynamics with a time-scale of several years in both cases.

Statistics and ProbabilityPhysics - Physics and SocietyFOS: Physical sciencesPhysics and Society (physics.soc-ph)Minimum spanning treeFOS: Economics and businessTime windowsStatisticsMathematical PhysicCluster analysisStock (geology)Condensed Matter - Statistical MechanicsMathematicsSpanning treeStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)EconophysicQuantitative Finance - Statistical FinanceStatistical and Nonlinear PhysicsAsset returnCondensed Matter PhysicsSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)VolatilityCorrelation-based clusteringPrice returnVolatility (finance)
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Dynamics of the Number of Trades of Financial Securities

1999

We perform a parallel analysis of the spectral density of (i) the logarithm of price and (ii) the daily number of trades of a set of stocks traded in the New York Stock Exchange. The stocks are selected to be representative of a wide range of stock capitalization. The observed spectral densities show a different power-law behavior. We confirm the $1/f^2$ behavior for the spectral density of the logarithm of stock price whereas we detect a $1/f$-like behavior for the spectral density of the daily number of trades.

Statistics and ProbabilityPhysics::Physics and SocietyStatistical Finance (q-fin.ST)LogarithmStatistical Mechanics (cond-mat.stat-mech)Spectral densityFOS: Physical sciencesQuantitative Finance - Statistical FinanceCondensed Matter PhysicsStock priceFOS: Economics and businessStock exchangeComputer Science::Computational Engineering Finance and ScienceEconometricsStock (geology)Condensed Matter - Statistical MechanicsMathematics
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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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Value-at-Risk and Tsallis statistics: risk analysis of the aerospace sector

2004

In this study, we analyze the aerospace stocks prices in order to characterize the sector behavior. The data analyzed cover the period from January 1987 to April 1999. We present a new index for the aerospace sector and we investigate the statistical characteristics of this index. Our results show that this index is well described by Tsallis distribution. We explore this result and modify the standard Value-at-Risk (VaR), financial risk assessment methodology in order to reflect an asset which obeys Tsallis non-extensive statistics.

Statistics and ProbabilityRisk analysisIndex (economics)Actuarial scienceStatistical Finance (q-fin.ST)EconophysicsStatistical Mechanics (cond-mat.stat-mech)Financial riskTsallis statisticsFOS: Physical sciencesQuantitative Finance - Statistical FinanceDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksCondensed Matter PhysicsFOS: Economics and businessEconomicsEconometricsTsallis distributionAsset (economics)Value at riskCondensed Matter - Statistical Mechanics
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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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The stabilizing effect of volatility in financial markets

2017

In financial markets, greater volatility is usually considered synonym of greater risk and instability. However, large market downturns and upturns are often preceded by long periods where price returns exhibit only small fluctuations. To investigate this surprising feature, here we propose using the mean first hitting time, i.e. the average time a stock return takes to undergo for the first time a large negative or positive variation, as an indicator of price stability, and relate this to a standard measure of volatility. In an empirical analysis of daily returns for $1071$ stocks traded in the New York Stock Exchange, we find that this measure of stability displays nonmonotonic behavior, …

Statistics and ProbabilityStatistical Finance (q-fin.ST)Stochastic volatilityFinancial economicsQuantitative Finance - Statistical FinanceImplied volatilityCondensed Matter Physics01 natural sciencesVolatility risk premiumSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)010305 fluids & plasmasHeston modelFOS: Economics and businessVolatility swap0103 physical sciencesEconometricsForward volatilityEconomicsVolatility smileVolatility (finance)010306 general physicsStatistical and Nonlinear Physic
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Some past and present challenges of econophysics

2016

We discuss the cultural background that was shared by some of the first econophysicists when they started to work on economic and financial problems with methods and tools of statistical physics. In particular we discuss about the role of stylized facts and statistical physical laws in economics and statistical physics respectively. As an example of the problems and potentials associated with the interaction of different communities of scholars dealing with problems observed in economic and financial systems we briefly discuss the development and the perspectives of the use of tools and concepts of networks in econophysics, economics and finance.

Stylized factEconophysicsGeneral Physics and AstronomyStatistical finance01 natural sciences010305 fluids & plasmasCultural backgroundPhysics and Astronomy (all)Work (electrical)0103 physical sciencesEconomicsGeneral Materials ScienceMaterials Science (all)Positive economicsPhysical and Theoretical Chemistry010306 general physicsPhysical law
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