288 research outputs found
Bias-corrected and robust estimation of the bivariate stable tail dependence function
We consider the estimation of the bivariate stable tail dependence function and propose a bias-corrected and robust estimator. We establish its asymptotic behavior under suitable assumptions. The finite sample performance of the proposed estimator is examined on a simulation study involving both uncontaminated and contaminated samples
Robust estimator of distortion risk premiums for heavy-tailed losses
We use the so-called t-Hill tail index estimator proposed by Fabi\'an(2001),
rather than Hill's one, to derive a robust estimator for the distortion risk
premium of loss. Under the second-order condition of regular variation, we
establish its asymptotic normality. By simulation study, we show that this new
estimator is more robust than of Necir and Meraghni 2009 both for small and
large samples.Comment: submitte
Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks
Quantile regression is an increasingly important empirical tool in economics
and other sciences for analyzing the impact of a set of regressors on the
conditional distribution of an outcome. Extremal quantile regression, or
quantile regression applied to the tails, is of interest in many economic and
financial applications, such as conditional value-at-risk, production
efficiency, and adjustment bands in (S,s) models. In this paper we provide
feasible inference tools for extremal conditional quantile models that rely
upon extreme value approximations to the distribution of self-normalized
quantile regression statistics. The methods are simple to implement and can be
of independent interest even in the non-regression case. We illustrate the
results with two empirical examples analyzing extreme fluctuations of a stock
return and extremely low percentiles of live infants' birthweights in the range
between 250 and 1500 grams.Comment: 41 pages, 9 figure
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.
Fitting and goodness-of-fit test of non-truncated and truncated power-law distributions
Power-law distributions contain precious information about a large variety of
processes in geoscience and elsewhere. Although there are sound theoretical
grounds for these distributions, the empirical evidence in favor of power laws
has been traditionally weak. Recently, Clauset et al. have proposed a
systematic method to find over which range (if any) a certain distribution
behaves as a power law. However, their method has been found to fail, in the
sense that true (simulated) power-law tails are not recognized as such in some
instances, and then the power-law hypothesis is rejected. Moreover, the method
does not work well when extended to power-law distributions with an upper
truncation. We explain in detail a similar but alternative procedure, valid for
truncated as well as for non-truncated power-law distributions, based in
maximum likelihood estimation, the Kolmogorov-Smirnov goodness-of-fit test, and
Monte Carlo simulations. An overview of the main concepts as well as a recipe
for their practical implementation is provided. The performance of our method
is put to test on several empirical data which were previously analyzed with
less systematic approaches. The databases presented here include the half-lives
of the radionuclides, the seismic moment of earthquakes in the whole world and
in Southern California, a proxy for the energy dissipated by tropical cyclones
elsewhere, the area burned by forest fires in Italy, and the waiting times
calculated over different spatial subdivisions of Southern California. We find
the functioning of the method very satisfactory.Comment: 26 pages, 9 figure
Nonlinear dynamics and intermittency in a long-term copepod time series
We consider the nonlinear dynamics of a long-term copepod (small crustaceans) time series sampled weekly in the Mediterranean sea from 1967 to 1992. Such population dynamics display a high variability that we consider here in an interdisciplinary study, using tools borrowed from the field of statistical physics. We analyse the extreme events of male and female abundances, and of the total population, and show that they both have heavy tailed probability density functions (pdf). We provide hyperbolic fits of the form p(x) ∼ 1/xμ+1, and estimate the value of μ using Hill’s estimator. We then study the ratio of male to female abundances, compared to the female abundances. Using conditional probability density functions and conditional averages, we show that this ratio is independent of the female density, when the latter is larger than a given threshold. This property is very useful for modelization. We also consider the product of male to female abundances, which can be ecologically related to the encounters. We show that this product is extremely intermittent, and link its pdf to the female pdf
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