Subsampling based inference forUstatistics under thick tails using self-normalization

2018 ◽  
Vol 138 ◽  
pp. 95-103
Author(s):  
Willa W. Chen ◽  
Rohit S. Deo
Keyword(s):  
Thick Tails

scholarly journals Bimodal t-ratios: the impact of thick tails on inference

2010 ◽  
Vol 13 (2) ◽  
pp. 271-289 ◽  
Author(s):  
Carlo V. Fiorio ◽  
Vassilis A. Hajivassiliou ◽  
Peter C. B. Phillips
Keyword(s):  
Thick Tails ◽  
The Impact

scholarly journals Volatility filtering in estimation of kurtosis (and variance)

2019 ◽  
Vol 7 (1) ◽  
pp. 1-23
Author(s):  
Stanislav Anatolyev
Keyword(s):  
Confidence Intervals ◽  
Method Of Moments ◽  
Usual Method ◽  
Coverage Probabilities ◽  
High Volatility ◽  
Filtering Technique ◽  
The One ◽  
Moments Estimates ◽  
Thick Tails ◽  
Kurtosis Coefficient

AbstractThe kurtosis of the distribution of financial returns characterized by high volatility persistence and thick tails is notoriously difficult to estimate precisely. We propose a simple but effective procedure of estimating the kurtosis coefficient (and variance) based on volatility filtering that uses a simple GARCH model. In addition to an estimate, the proposed algorithm issues a signal of whether the kurtosis (or variance) is finite or infinite. We also show how to construct confidence intervals around the proposed estimates. Simulations indicate that the proposed estimates are much less median biased than the usual method-of-moments estimates, their confidence intervals having much more precise coverage probabilities. The procedure alsoworks well when the underlying volatility process is not the one the filtering technique is based on. We illustrate how the algorithm works using several actual series of returns.

scholarly journals Functional inequalities, thick tails and asymptotics for the critical mass Patlak–Keller–Segel model

2012 ◽  
Vol 262 (5) ◽  
pp. 2142-2230 ◽  
Author(s):  
Adrien Blanchet ◽  
Eric A. Carlen ◽  
José A. Carrillo
Keyword(s):  
Critical Mass ◽  
Functional Inequalities ◽  
Thick Tails

A RISK-ANALYTIC APPROACH TO CONTROL OF LARGE-VOLUME OIL SPILLS

1975 ◽  
Vol 1975 (1) ◽  
pp. 301-306 ◽  
Author(s):  
A. S. Paulson ◽  
A. D. Schumaker ◽  
W. A. Wallace
Keyword(s):  
Large Volume ◽  
Oil Spills ◽  
Probability Distributions ◽  
Data File ◽  
Coast Guard ◽  
New Approach ◽  
Appropriate Treatment ◽  
Coefficients Of Variation ◽  
The Right ◽  
Thick Tails

ABSTRACT The frequency of large-volume oil spills is considerably greater than is consistent with prediction based upon traditional methods. The reason for this phenomenon is that standard probability distributions of magnitude of spills do not have the flexibility to admit of very large coefficients of variation, especially for distributions which are highly skewed to the right. Hence, distributions which have large means relative to the median and which have long thick tails are prerequisites for an appropriate treatment of the problem. The class of stable laws provides a convenient method for investigating the empirical oil spill experience: several large spills dominate the total volume of spillage in virtually all accounting periods; e.g., quarterly. Our methodology involves a statistical assessment of “accident-proneness component;” if one exists, the data is further examined to identify insofar as is possible the genesis of the component (s); if none exists, we assess the frequency and severity of discharge for various geographic areas. A new approach has been utilized to fit these long, thick-tailed probability distribution to a U.S. Coast Guard data file on oil spills, the pollution incident reporting system (PIRS), with considerable success. We pay particular attention to the fitted upper tail vis-a-vis the actual upper tail. The agreement, where our methodology is deemed applicable, is very good We also indicate improvements to methodology and applications.

scholarly journals An opportunity of application of excess factor in hydrology

2012 ◽  
Vol 9 (12) ◽  
pp. 13635-13649 ◽  
Author(s):  
V. Kovalenko ◽  
E. Gaidukova ◽  
A. Kachalova
Keyword(s):  
Stochastic Model ◽  
River Flow ◽  
Large Error ◽  
Hydrological Processes ◽  
Multiplicative Noises ◽  
Flow Formation ◽  
Thick Tails ◽  
Engineering Hydrology

Abstract. In last few years in hydrology an interest to excess factor has appeared as a reaction to unsuccessful attempts to simulate and predict evolving hydrological processes, which attributive property is statistical instability. The article shows, that the latter has a place at strong relative multiplicative noises of probabilistic stochastic model of a river flow formation, phenomenological display of which are "the thick tails" and polymodality, for which the excess factor "answers", by being ignored by a modern hydrology in connection to the large error of its calculation because of insufficient duration of lines of observation over a flow. However, it is found out, that the duration of observation of several decades practically stabilizes variability of the excess factor, the error of which definition appears commensurable with an error of other calculated characteristics used in engineering hydrology.

scholarly journals Mixed Compound Poisson Distributions

1986 ◽  
Vol 16 (S1) ◽  
pp. S59-S79 ◽  
Author(s):  
Gord Willmot
Keyword(s):  
Negative Binomial ◽  
Random Variable ◽  
Computational Formula ◽  
Inverse Gaussian ◽  
Poisson Distributions ◽  
Mixing Distributions ◽  
Chi Squared ◽  
General Distributions ◽  
Generalized Inverse Gaussian ◽  
Thick Tails

AbstractThe distribution of total claims payable by an insurer is considered when the frequency of claims is a mixed Poisson random variable. It is shown how in many cases the total claims density can be evaluated numerically using simple recursive formulae (discrete or continuous).Mixed Poisson distributions often have desirable properties for modelling claim frequencies. For example, they often have thick tails which make them useful for long-tailed data. Also, they may be interpreted as having arisen from a stochastic process. Mixing distributions considered include the inverse Gaussian, beta, uniform, non-central chi-squared, and the generalized inverse Gaussian as well as other more general distributions.It is also shown how these results may be used to derive computational formulae for the total claims density when the frequency distribution is either from the Neyman class of contagious distributions, or a class of negative binomial mixtures. Also, a computational formula is derived for the probability distribution of the number in the system for the M/G/1 queue with bulk arrivals.

Modeling exchange rate dynamics: Non-linear dependence and thick tails

1993 ◽  
Vol 12 (1) ◽  
pp. 33-63 ◽  
Author(s):  
Anya McGuirk ◽  
John Robertson ◽  
Aris Spanos
Keyword(s):  
Exchange Rate ◽  
Linear Dependence ◽  
Exchange Rate Dynamics ◽  
Non Linear ◽  
Thick Tails

Modelling the sterling-deutschmark exchange rate: Non-linear dependence and thick tails

1996 ◽  
Vol 13 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Nikitas Pittis
Keyword(s):  
Exchange Rate ◽  
Linear Dependence ◽  
Non Linear ◽  
Thick Tails
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