Drafts by Luca CATTIVELLI
In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), an... more In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), and we show that they provide asymptotic consistency in variable selection and the same efficiency as if the set of true predictors were known in advance (oracle property). A Monte Carlo exercise demonstrates the good performance of this approach and an empirical application shows its effectiveness in studying the network of volatility spillovers among European financial indices, during and after the sovereign debt crisis. We conclude demonstrating the superior volatility forecast ability of Adaptive Lasso techniques also when a common trend is removed prior to multivariate volatility spillover analysis.
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This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-... more This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-valued time series with long memory and seasonality. The modelling is achieved through an autoregressive Poisson process with a predictable stochastic intensity that is determined by two factors: a seasonal intraday pattern and a heterogeneous autoregressive component. We call the model SHARP, which is an acronym for seasonal heterogeneous autoregressive Poisson. We also present a mixed-data sampling extension of the model, which adopts the historical information flow more efficiently and provides the best (among all the models considered) forecasting performances, empirically, for the bid-ask spreads of NYSE equity stocks. We conclude by showing how bid-ask spread forecasts based on the SHARP model can be exploited in order to reduce the total cost incurred by a trader who is willing to buy or sell a given amount of an equity stock.
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Papers by Luca CATTIVELLI
This thesis is a collection of three essays on \ufb01nancial econometrics with a common backgroun... more This thesis is a collection of three essays on \ufb01nancial econometrics with a common background in ultra-high frequency modeling of market activity. In the \ufb01rst essay, we propose an accurate and fast-to-estimate forecasting model for discrete valued time series with long memory and seasonality.1 The modelling is achieved with an autoregressive conditional Poisson process that features seasonality and heterogeneous autoregressive components (whence the acronym SHARP: Seasonal Heterogeneous AutoRegressive Poisson). Motivated by the prominent role of the bid-ask spread as a transaction cost for trading, we apply the SHARP model to forecast the bid-ask spreads of a large sample of NYSE equity stocks. [...
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We consider a particle performing a stochastic motion on a one-dimensional lattice with jump widt... more We consider a particle performing a stochastic motion on a one-dimensional lattice with jump widths distributed according to a power-law with exponent μ + 1. Assuming that the walker moves in the presence of a distribution a(x) of targets (traps) depending on the spatial coordinate x, we study the probability that the walker will eventually find any target (will eventually be trapped). We focus on the case of power-law distributions a(x) ∼ x^-α and we find that as long as μ < α there is a finite probability that the walker will never be trapped, no matter how long the process is. This analytical result, valid on infinite chains, is corroborated by numerical simulations which also evidence the emergence of slow searching (trapping) times in finite-size system. The extension of this finding to higher-dimensional structures is also discussed.
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In this work we consider a simple random walk embedded in a generic branched structure and we fin... more In this work we consider a simple random walk embedded in a generic branched structure and we find a close-form formula to calculate the hitting time H(i,f) between two arbitrary nodes i and j. We then use this formula to obtain the set of hitting times { H(i,f)} for combs and their expectation values, namely the mean-first passage time ( MFPT_f), where the average is performed over the initial node while the final node f is given, and the global mean-first passage time ( GMFPT), where the average is performed over both the initial and the final node. Finally, we discuss applications in the context of reaction-diffusion problems.
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SSRN Electronic Journal, 2018
In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), an... more In this paper we adopt Adaptive Lasso techniques in vector Multiplicative Error Models (vMEM), and we show that they provide asymptotic consistency in variable selection and the same efficiency as if the set of true predictors were known in advance (oracle property). A Monte Carlo exercise demonstrates the good performance of this approach and an empirical application shows its effectiveness in studying the network of volatility spillovers among European financial indices, during and after the sovereign debt crisis. We conclude demonstrating the superior volatility forecast ability of Adaptive Lasso techniques also when a common trend is removed prior to multivariate volatility spillover analysis.
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Encounters between walkers performing a random motion on an appropriate structure can describe a ... more Encounters between walkers performing a random motion on an appropriate structure can describe a wide variety of natural phenomena ranging from pharmacokinetics to foraging. On homogeneous structures the asymptotic encounter probability between two walkers is (qualitatively) independent of whether both walkers are moving or one is kept fixed. On infinite comb-like structures this is no longer the case and here we deepen the mechanisms underlying the emergence of a finite probability that two random walkers will never meet, while one single random walker is certain to visit any site. In particular, we introduce an analytical approach to address this problem and even more general problems such as the case of two walkers with different diffusivity, particles walking on a finite comb and on arbitrary bundled structures, possibly in the presence of loops. Our investigations are both analytical and numerical and highlight that, in general, the outcome of a reaction involving two reactants...
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SSRN Electronic Journal, 2000
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Physical Review E, 2016
Encounters between walkers performing a random motion on an appropriate structure can describe a ... more Encounters between walkers performing a random motion on an appropriate structure can describe a wide variety of natural phenomena ranging from pharmacokinetics to foraging. On homogeneous structures the asymptotic encounter probability between two walkers is (qualitatively) independent of whether both walkers are moving or one is kept fixed. On infinite comblike structures this is no longer the case and here we deepen the mechanisms underlying the emergence of a finite probability that two random walkers will never meet, while one single random walker is certain to visit any site. In particular, we introduce an analytical approach to address this problem and even more general problems such as the case of two walkers with different diffusivity, particles walking on a finite comb and on arbitrary bundled structures, possibly in the presence of loops. Our investigations are both analytical and numerical and highlight that, in general, the outcome of a reaction involving two reactants on a comblike architecture can strongly differ according to whether both reactants are moving (no matter their relative diffusivities) or only one is moving and according to the density of shortcuts among the branches.
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Physical Review E, 2015
We consider a particle performing a stochastic motion on a one-dimensional lattice with jump leng... more We consider a particle performing a stochastic motion on a one-dimensional lattice with jump lengths distributed according to a power law with exponent μ+1. Assuming that the walker moves in the presence of a distribution a(x) of targets (traps) depending on the spatial coordinate x, we study the probability that the walker will eventually find any target (will eventually be trapped). We focus on the case of power-law distributions a(x)∼x^{-α} and we find that, as long as μ&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;α, there is a finite probability that the walker will never be trapped, no matter how long the process is. This result is shown via analytical arguments and numerical simulations which also evidence the emergence of slow searching (trapping) times in finite-size system. The extension of this finding to higher-dimensional structures is also discussed.
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Physical Review E, 2015
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Conference Presentations by Luca CATTIVELLI
In this paper we propose the adoption of Adaptive Lasso techniques for variable selection in vect... more In this paper we propose the adoption of Adaptive Lasso techniques for variable selection in vector Multiplicative Error Models (vMEM), proving that they provide the oracle property, that is, asymptotic consistency in variable selection and the same efficiency as if the set of true predictors were known in advance. A Monte Carlo exercise demonstrates the good performances of this estimator and an empirical application shows the effectiveness of this approach for the study of the network of volatility spillovers among European financial indices, during and after the sovereign debt crisis. We conclude demonstrating the superior volatility forecast ability of Adaptive Lasso techniques in a context where a common trend is removed prior to multivariate volatility spillover analysis.
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Drafts by Luca CATTIVELLI
Papers by Luca CATTIVELLI
Conference Presentations by Luca CATTIVELLI