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Nonparametric tail risk, stock returns and the macroeconomy
Almeida, Caio; Ardison, Kym; Garcia, René; Vicente, … - 2016
Persistent link: https://www.econbiz.de/10011458735
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Comments on: Nonparametric tail risk, stock returns and the macroeconomy
Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2016
Persistent link: https://www.econbiz.de/10011518800
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Identification of ex ante returns using elicited choice probabilities
Méango, Romuald - 2023
Persistent link: https://www.econbiz.de/10014250393
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Adaptive Bayesian estimation of mixed discrete-continuous distributions under smoothness and sparsity
Norets, Andriy; Pelenis, Justinas - 2018
We consider nonparametric estimation of a mixed discrete-continuous distribution under anisotropic smoothness … derive lower bounds on the estimation rates in the total variation distance. Next, we consider a nonparametric mixture of … normals models can be used for optimal adaptive estimation of mixed discretecontinuous distributions. …
Persistent link: https://www.econbiz.de/10011895828
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Empirical likelihood
Owen, Art B. - 2001
Persistent link: https://www.econbiz.de/10001574339
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Vectors of two-parameter Poisson-Dirichlet processes
Leisen, Fabrizio; Lijoi, Antonio - 2010
The definition of vectors of dependent random probability measures is a topic of interest in applications to Bayesian statistics. They, indeed, represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. In this...
Persistent link: https://www.econbiz.de/10010343891
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Nonparametric Forecasting of Multivariate Probability Density Functions
Guegan, Dominique - 2018
The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able...
Persistent link: https://www.econbiz.de/10012917229
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Spatial dependence and space-time trends in extreme event
Einmahl, John H. J.; Ferreira, Ana; Haan, Laurens de; … - 2020
Persistent link: https://www.econbiz.de/10012182625
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On extreme value approximation to tails of distribution functions
Li, Deyuan - 2004
Persistent link: https://www.econbiz.de/10002210996
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Modeling extreme events : time-varying extreme tail shape
Schwaab, Bernd; Zhang, Xin; Lucas, André - 2021
Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a …
Persistent link: https://www.econbiz.de/10012429187