7,898 research outputs found
Bayesian inference for CoVaR
Recent financial disasters emphasised the need to investigate the consequence
associated with the tail co-movements among institutions; episodes of contagion
are frequently observed and increase the probability of large losses affecting
market participants' risk capital. Commonly used risk management tools fail to
account for potential spillover effects among institutions because they provide
individual risk assessment. We contribute to analyse the interdependence
effects of extreme events providing an estimation tool for evaluating the
Conditional Value-at-Risk (CoVaR) defined as the Value-at-Risk of an
institution conditioned on another institution being under distress. In
particular, our approach relies on Bayesian quantile regression framework. We
propose a Markov chain Monte Carlo algorithm exploiting the Asymmetric Laplace
distribution and its representation as a location-scale mixture of Normals.
Moreover, since risk measures are usually evaluated on time series data and
returns typically change over time, we extend the CoVaR model to account for
the dynamics of the tail behaviour. Application on U.S. companies belonging to
different sectors of the Standard and Poor's Composite Index (S&P500) is
considered to evaluate the marginal contribution to the overall systemic risk
of each individual institutio
Partially linear censored quantile regression
Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate. In many cases however, greater flexibility is desired to go beyond the usual multiple regression paradigm. One area of common interest is that of partially linear models: one (or more) of the explanatory covariates are assumed to act on the response through a non-linear function. Here the CRQ approach of Portnoy (J Am Stat Assoc 98:1001–1012, 2003) is extended to this partially linear setting. Basic consistency results are presented. A simulation experiment and unemployment example justify the value of the partially linear approach over methods based on the Cox proportional hazards model and on methods not permitting nonlinearity
Discussion paper. Conditional growth charts
Growth charts are often more informative when they are customized per
subject, taking into account prior measurements and possibly other covariates
of the subject. We study a global semiparametric quantile regression model that
has the ability to estimate conditional quantiles without the usual
distributional assumptions. The model can be estimated from longitudinal
reference data with irregular measurement times and with some level of
robustness against outliers, and it is also flexible for including covariate
information. We propose a rank score test for large sample inference on
covariates, and develop a new model assessment tool for longitudinal growth
data. Our research indicates that the global model has the potential to be a
very useful tool in conditional growth chart analysis.Comment: This paper discussed in: [math/0702636], [math/0702640],
[math/0702641], [math/0702642]. Rejoinder in [math.ST/0702643]. Published at
http://dx.doi.org/10.1214/009053606000000623 in the Annals of Statistics
(http://www.imstat.org/aos/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Nonparametric Econometrics: The np Package
We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
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