Dual Sourcing Under Heavy-Tailed Demand: An Extreme-Value Theory Approach

2014 ◽  
Author(s):  
Isik Bicer
Keyword(s):  
Extreme Value Theory ◽  
Value Theory ◽  
Extreme Value ◽  
Theory Approach ◽  
Dual Sourcing ◽  
Heavy Tailed

scholarly journals Extreme Value Theory Applied to r Largest Order Statistics Under the Bayesian Approach

2019 ◽  
Vol 42 (2) ◽  
pp. 143-166 ◽  
Author(s):  
Renato Santos Silva ◽  
Fernando Ferraz Nascimento
Keyword(s):  
Monte Carlo ◽  
Order Statistics ◽  
Bayesian Approach ◽  
Extreme Value Theory ◽  
Likelihood Estimation ◽  
Value Theory ◽  
Extreme Value ◽  
Environmental Data ◽  
Heavy Tailed ◽  
The Bayesian Approach

Extreme Value Theory (EVT) is an important tool to predict efficient gains and losses. Its main areas of analyses are economic and environmental. Initially, for that form of event, it was developed the use of patterns of parametric distribution such as Normal and Gamma. However, economic and environmental data presents, in most cases, a heavy-tailed distribution, in contrast to those distributions. Thus, it was faced a great difficult to frame extreme events. Furthermore, it was almost impossible to use conventional models, making predictions about non-observed events, which exceed the maximum of observations. In some situations EVT is used to analyse only the maximum of some dataset, which provide few observations, and in those cases it is more effective to use the r largest-order statistics. This paper aims to propose Bayesian estimators' for parameters of the r largest-order statistics. During the research, it was used Monte Carlo simulation to analyze the data, and it was observed some properties of those estimators, such as mean, variance, bias and Root Mean Square Error (RMSE). The estimation of the parameters provided inference for its parameters and return levels. This paper also shows a procedure to the choice of the r-optimal to the r largest-order statistics, based on the Bayesian approach applying Markov chains Monte Carlo (MCMC). Simulation results reveal that the Bayesian approach has a similar performance to the Maximum Likelihood Estimation, and the applications were developed using the Bayesian approach and showed a gain in accurary compared with otherestimators.

scholarly journals Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1425
Author(s):  
Miloš Božović
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Value Theory ◽  
Extreme Value ◽  
Theory Approach ◽  
Risk Modeling ◽  
Tail Risk ◽  
Goodness Of Fit Tests ◽  
Level Of Significance

This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.

scholarly journals Statistics of the separation between sliding rigid rough surfaces: Simulations and extreme value theory approach

2019 ◽  
Vol 99 (2) ◽  
Author(s):  
Nicolas Ponthus ◽  
Julien Scheibert ◽  
Kjetil Thøgersen ◽  
Anders Malthe-Sørenssen ◽  
Joël Perret-Liaudet
Keyword(s):  
Extreme Value Theory ◽  
Rough Surfaces ◽  
Value Theory ◽  
Extreme Value ◽  
Theory Approach

Mortality and morbidity peaks modeling: An extreme value theory approach

2016 ◽  
Vol 27 (5) ◽  
pp. 1498-1512 ◽  
Author(s):  
Y Chiu ◽  
F Chebana ◽  
B Abdous ◽  
D Bélanger ◽  
P Gosselin
Keyword(s):  
Public Health ◽  
Health Care ◽  
Extreme Value Theory ◽  
Social Stress ◽  
Value Theory ◽  
Extreme Value ◽  
Health Care Facilities ◽  
Theory Approach ◽  
Emergency Rooms ◽  
Mortality And Morbidity

Hospitalizations and deaths belong to the most studied health variables in public health. Those variables are usually analyzed through mean events and trends, based on the whole dataset. However, this approach is not appropriate to comprehend health outcome peaks which are unusual events that strongly impact the health care network (e.g. overflow in hospital emergency rooms). Peaks can also be of interest in etiological research, for instance when analyzing relationships with extreme exposures (meteorological conditions, air pollution, social stress, etc.). Therefore, this paper aims at modeling health variables exclusively through the peaks, which is rarely done except over short periods. Establishing a rigorous and general methodology to identify peaks is another goal of this study. To this end, the extreme value theory appears adequate with statistical tools for selecting and modeling peaks. Selection and analysis for deaths and hospitalizations peaks using extreme value theory have not been applied in public health yet. Therefore, this study also has an exploratory goal. A declustering procedure is applied to the raw data in order to meet extreme value theory requirements. The application is done on hospitalization and death peaks for cardiovascular diseases, in the Montreal and Quebec metropolitan communities (Canada) for the period 1981–2011. The peak return levels are obtained from the modeling and can be useful in hospital management or planning future capacity needs for health care facilities, for example. This paper focuses on one class of diseases in two cities, but the methodology can be applied to any other health peaks series anywhere, as it is data driven.

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