26,195 research outputs found
There is a VaR beyond usual approximations
Basel II and Solvency 2 both use the Value-at-Risk (VaR) as the risk measure
to compute the Capital Requirements. In practice, to calibrate the VaR, a
normal approximation is often chosen for the unknown distribution of the yearly
log returns of financial assets. This is usually justified by the use of the
Central Limit Theorem (CLT), when assuming aggregation of independent and
identically distributed (iid) observations in the portfolio model. Such a
choice of modeling, in particular using light tail distributions, has proven
during the crisis of 2008/2009 to be an inadequate approximation when dealing
with the presence of extreme returns; as a consequence, it leads to a gross
underestimation of the risks. The main objective of our study is to obtain the
most accurate evaluations of the aggregated risks distribution and risk
measures when working on financial or insurance data under the presence of
heavy tail and to provide practical solutions for accurately estimating high
quantiles of aggregated risks. We explore a new method, called Normex, to
handle this problem numerically as well as theoretically, based on properties
of upper order statistics. Normex provides accurate results, only weakly
dependent upon the sample size and the tail index. We compare it with existing
methods.Comment: 33 pages, 5 figure
Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series
We provide some asymptotic theory for the largest eigenvalues of a sample
covariance matrix of a p-dimensional time series where the dimension p = p_n
converges to infinity when the sample size n increases. We give a short
overview of the literature on the topic both in the light- and heavy-tailed
cases when the data have finite (infinite) fourth moment, respectively. Our
main focus is on the heavytailed case. In this case, one has a theory for the
point process of the normalized eigenvalues of the sample covariance matrix in
the iid case but also when rows and columns of the data are linearly dependent.
We provide limit results for the weak convergence of these point processes to
Poisson or cluster Poisson processes. Based on this convergence we can also
derive the limit laws of various function als of the ordered eigenvalues such
as the joint convergence of a finite number of the largest order statistics,
the joint limit law of the largest eigenvalue and the trace, limit laws for
successive ratios of ordered eigenvalues, etc. We also develop some limit
theory for the singular values of the sample autocovariance matrices and their
sums of squares. The theory is illustrated for simulated data and for the
components of the S&P 500 stock index.Comment: in Extremes; Statistical Theory and Applications in Science,
Engineering and Economics; ISSN 1386-1999; (2016
Level crossings and other level functionals of stationary Gaussian processes
This paper presents a synthesis on the mathematical work done on level
crossings of stationary Gaussian processes, with some extensions. The main
results [(factorial) moments, representation into the Wiener Chaos, asymptotic
results, rate of convergence, local time and number of crossings] are
described, as well as the different approaches [normal comparison method, Rice
method, Stein-Chen method, a general -dependent method] used to obtain them;
these methods are also very useful in the general context of Gaussian fields.
Finally some extensions [time occupation functionals, number of maxima in an
interval, process indexed by a bidimensional set] are proposed, illustrating
the generality of the methods. A large inventory of papers and books on the
subject ends the survey.Comment: Published at http://dx.doi.org/10.1214/154957806000000087 in the
Probability Surveys (http://www.i-journals.org/ps/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Growth Estimators and Confidence Intervals for the Mean of Negative Binomial Random Variables with Unknown Dispersion
The Negative Binomial distribution becomes highly skewed under extreme
dispersion. Even at moderately large sample sizes, the sample mean exhibits a
heavy right tail. The standard Normal approximation often does not provide
adequate inferences about the data's mean in this setting. In previous work, we
have examined alternative methods of generating confidence intervals for the
expected value. These methods were based upon Gamma and Chi Square
approximations or tail probability bounds such as Bernstein's Inequality. We
now propose growth estimators of the Negative Binomial mean. Under high
dispersion, zero values are likely to be overrepresented in the data. A growth
estimator constructs a Normal-style confidence interval by effectively removing
a small, pre--determined number of zeros from the data. We propose growth
estimators based upon multiplicative adjustments of the sample mean and direct
removal of zeros from the sample. These methods do not require estimating the
nuisance dispersion parameter. We will demonstrate that the growth estimators'
confidence intervals provide improved coverage over a wide range of parameter
values and asymptotically converge to the sample mean. Interestingly, the
proposed methods succeed despite adding both bias and variance to the Normal
approximation
Exact convergence rate and leading term in central limit theorem for student's t statistic
The leading term in the normal approximation to the distribution of Student's
t statistic is derived in a general setting, with the sole assumption being
that the sampled distribution is in the domain of attraction of a normal law.
The form of the leading term is shown to have its origin in the way in which
extreme data influence properties of the Studentized sum. The leading-term
approximation is used to give the exact rate of convergence in the central
limit theorem up to order n⁻¹/², where n denotes sample size. It is proved
that the exact rate uniformly on the whole real line is identical to the exact
rate on sets of just three points. Moreover, the exact rate is identical to
that for the non-Studentized sum when the latter is normalized for scale using
a truncated form of variance, but when the corresponding truncated centering
constant is omitted. Examples of characterizations of convergence rates are
also given. It is shown that, in some instances, their validity uniformly on
the whole real line is equivalent to their validity on just two symmetric
points
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