Results 61 to 70 of about 19,957,777 (200)

Heavy-tailed distributions in VaR calculations [PDF]

The essence of the Value-at-Risk (VaR) and Expected Shortfall (ES) computations is estimation of low quantiles in the portfolio return distributions.
Rafal Weron, Adam Misiorek
core  

Learning with Spectral Kernels and Heavy-Tailed Data

CoRR, 2009
Two ubiquitous aspects of large-scale data analysis are that the data often have heavy-tailed properties and that diffusion-based or spectral-based methods are often used to identify and extract structure of interest. Perhaps surprisingly, popular distribution-independent methods such as those based on the VC dimension fail to provide nontrivial ...
Michael W. Mahoney, Hariharan Narayanan
openaire   +3 more sources

Phase-Type Variational Autoencoders for Heavy-Tailed Data

CoRR
Heavy-tailed distributions are ubiquitous in real-world data, where rare but extreme events dominate risk and variability. However, standard Variational Autoencoders (VAEs) employ simple decoder distributions, such as Gaussian distributions, that fail to capture heavy-tailed behavior, while existing heavy-tail-aware extensions remain restricted to ...
Abdelhakim Ziani, András Horváth, Paolo Ballarini   +2 more
openaire   +3 more sources

Modeling Heavy-Tailed Stock Index Returns Using the Generalized Hyperbolic Distribution [PDF]

In the present study, we estimate the parameters of the Generalized Hyperbolic Distribution for a series of stock index returns including the Romanian BETC and indexes from other two Eastern European countries, Hungary and the Czech Republic.
Necula, Ciprian
core  

Model Averaging and Grid Maps for Modeling Heavy-Tailed Insurance Data

Risks
This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single “best” model selection commonly used in actuarial ...
Lira B. Mothibe, Sandile C. Shongwe
doaj   +1 more source

Taylor's law of fluctuation scaling for semivariances and higher moments of heavy-tailed data. [PDF]

Proc Natl Acad Sci U S A, 2021
Brown M, Cohen JE, Tang CF, Yam SCP.
europepmc   +1 more source

Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student?s t-Distribution [PDF]

Bayesian analysis of a stochastic volatility model with a generalized hyperbolic (GH) skew Student?s t-error distribution is described where we first consider an asymmetric heavy-tailed error and leverage effects.
Jouchi Nakajima, Yasuhiro Omori
core  

Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact

, 2023
We present two easy-to-implement gradient-free/zeroth-order methods to optimize a stochastic non-smooth function accessible only via a black-box. The methods are built upon efficient first-order methods in the heavy-tailed case, i.e., when the gradient ...
Pavel Dvurechensky, Dvurechensky, Pavel, Dvinskikh, Darina, Alexander Gasnikov, Nikita Kornilov, Gasnikov, Alexander, Darina Dvinskikh, Kornilov, Nikita   +7 more
core   +1 more source

Improved Probability-Weighted Moments and Two-Stage Order Statistics Methods of Generalized Extreme Value Distribution

Mathematics
This study evaluates six parameter estimation methods for the generalized extreme value (GEV) distribution: maximum likelihood estimation (MLE), two probability-weighted moments (PWM-UE and PWM-PP), and three robust two-stage order statistics estimators (
Autcha Araveeporn
doaj   +1 more source

Inference for heavy-tailed data with Gaussian dependence

, 2023
26 pages, 7 figures, 1 table. Textual modifications primarily in the introduction and conclusion, some simulated plots were updated.
openaire   +2 more sources