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Bayesian Analysis of Hazard Regression Models under Order Restrictions on Covariate Effects and Ageing

Bhattacharjee, Arnab; Bhattacharjee, Madhuchhanda - Volkswirtschaftliche Fakultät, … - 2007

possibly age-varying (non-proportional), and there is multiplicative frailty with arbitrary distribution. Our framework … incorporates a wide variety of order restrictions on covariate dependence and duration dependence (ageing). We propose estimation … on the nature of ageing. For example, relevant theory may suggest that the baseline hazard function decreases with age …

Persistent link: https://www.econbiz.de/10005787182

Frailty Correlated Default

DUFFIE, Darrell; ECKNER, Andreas; HOREL, Guillaume; …

We analyze portfolio credit risk in light of dynamic “frailty,” by which the credit qualities of different firms depend … on common unobservable time-varying default covariates. Frailty is estimated to have a large impact on estimated …

Persistent link: https://www.econbiz.de/10005534189

A Simple Test for the Absence of Covariate Dependence in Hazard Regression Models

Bhattacharjee, Arnab - School of Economics and Finance, University of St. Andrews - 2007

Persistent link: https://www.econbiz.de/10005673169

Partial Orders with Respect to Continuous Covariates and Tests for the Proportional Hazards Model

Bhattacharjee, Arnab - School of Economics and Finance, University of St. Andrews - 2008

are applicable in the presence of multiple covariates and frailty. Small sample performance and applications to real data …

Persistent link: https://www.econbiz.de/10005673172

Bayesian semiparametric stochastic volatility modeling

Jensen, Mark J.; Maheu, John M. - 2008

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and...

Persistent link: https://www.econbiz.de/10010292240

Bayesian semiparametric multivariate GARCH modeling

Jensen, Mark J.; Maheu, John M. - 2012

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given...

Persistent link: https://www.econbiz.de/10010292242

Estimating a semiparametric asymmetric stochastic volatility model with a dirichlet process mixture

Jensen, Mark J.; Maheu, John M. - 2012

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

Persistent link: https://www.econbiz.de/10010292350

Bayesian nonparametric learning of how skill is distributed across the mutual fund industry

Fisher, Mark; Jensen, Mark J.; Tkac, Paula A. - 2019

In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet process...

Persistent link: https://www.econbiz.de/10012030285

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 conditions and possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates in the total...

Persistent link: https://www.econbiz.de/10011917384

Posterior consistency in conditional density estimation by covariate dependent mixtures

Norets, Andriy; Pelenis, Justinas - 2011

This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in...

Persistent link: https://www.econbiz.de/10010290994

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