Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Galeano San Miguel, Pedro"'
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
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Factor copula models have been recently proposed for describing the joint distribution of a large number of variables in terms of a few common latent factors. In this paper, we employ a Bayesian procedure to make fast inferences for multi-factor and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::19c55de2eeba77ba0208f56fe054526e
http://hdl.handle.net/10016/27652
http://hdl.handle.net/10016/27652
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
Copula densities are widely used to model the dependence structure of financial time series. However, the number of parameters involved becomes explosive in high dimensions which results in most of the models in the literature being static. Factor co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::fc0cb7ba868e80bbb9fae23474cedfc9
http://hdl.handle.net/10016/24552
http://hdl.handle.net/10016/24552
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
The comparison of the means of two independent samples is one of the most popular problems in real-world data analysis. In the multivariate context, two-sample Hotelling's T² frequently used to test the equality of means of two independent Gaussian
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b61f2bf67c279cd9c69ce16d87d527c5
https://hdl.handle.net/10016/20253
https://hdl.handle.net/10016/20253
Autor:
Virbickaite, Audrone, Lopes, Hedibert F., Ausín Olivera, María Concepción, Galeano San Miguel, Pedro
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
This paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility (SV) models for financial data. The performance of this particle method is then compared with the standard Markov Chain Monte Carlo (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ef13282dc7b85aae7189f89c5ce1d950
https://hdl.handle.net/10016/19576
https://hdl.handle.net/10016/19576
Autor:
Galeano San Miguel, Pedro, Ausín Olivera, María Concepción, Virbickaite, Audrone, Lopes, Hedibert F.
This paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility (SV) models for financial data. The performance of this particle method is then compared with the standard Markov Chain Monte Carlo (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______645::1a7e0dfd27351e3fdb18792fdd4572da
https://e-archivo.uc3m.es/bitstream/handle/10016/19576/ws142819.pdf?sequence=1
https://e-archivo.uc3m.es/bitstream/handle/10016/19576/ws142819.pdf?sequence=1
Financial returns often present a complex relation with previous observations, along with a slight skewness and high kurtosis. As a consequence, we must pursue the use of flexible models that are able to seize these special features: a financial proc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______645::175078c4ae61816cd9e9acb6b2b6ffb1
https://e-archivo.uc3m.es/bitstream/handle/10016/19028/ws141711.pdf?sequence=1
https://e-archivo.uc3m.es/bitstream/handle/10016/19028/ws141711.pdf?sequence=1
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
Financial returns often present a complex relation with previous observations, along with a slight skewness and high kurtosis. As a consequence, we must pursue the use of flexible models that are able to seize these special features: a financial proc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::10ff8958c44f83ed4110de7f1aa0c952
http://hdl.handle.net/10016/19028
http://hdl.handle.net/10016/19028
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
This paper proposes methods to detect outliers in functional datasets. We are interested in challenging scenarios where functional samples are contaminated by outliers that may be difficult to recognize. The task of identifying a typical curves is ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::42bf243ee688fc671bd5e2d3b2eacf4c
http://hdl.handle.net/10016/19029
http://hdl.handle.net/10016/19029
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
We use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model to estimate the time-varying volatilities of financial returns. The ADCC-GJR-GARCH model takes into consideration the asymmetries in individual assets volatilities, as well a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ea995274d23583c221634a0d9c40e802
https://hdl.handle.net/10016/16967
https://hdl.handle.net/10016/16967
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
instname
This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a044c8bd9ccf26461294e6d67ca35b9c
https://hdl.handle.net/10016/16960
https://hdl.handle.net/10016/16960