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Robust Value at Risk Prediction : Appendix

Mancini, Loriano - 2010

This appendix extends simulation and empirical results reported in Mancini and Trojani (2010). It discusses the choice of the robustness tuning constants; describes the unconditional, independence and conditional coverage tests for VaR forecast evaluation; provides additional Monte Carlo...

Persistent link: https://www.econbiz.de/10013138328

Robust block bootstrap panel predictability tests

Westerlund, Joakim; Smeekes, Stephan - 2013

Persistent link: https://www.econbiz.de/10010199463

Skewed logistic distribution for statistical temperature post-processing in mountainous areas

Gebetsberger, Manuel; Stauffer, Reto; Mayr, Georg. J.; … - 2018

to follow a Gaussian response distribution. However, Gaussian regression models with only few covariates are often not … if many covariates are incorporated. Therefore, a simple refinement of the classical non-homogeneous Gaussian regression …

Persistent link: https://www.econbiz.de/10011847486

Robust Model Selection in Dynamic Models with an Application to Comparing Predictive Accuracy

Kiefer, Nicholas M.; Choi, Hwan-sik - 2006

A model selection procedure based on a general criterion function, with an example of the Kullback-Leibler Information Criterion (KLIC) using quasi-likelihood functions, is considered for dynamic non-nested models. We propose a robust test which generalizes Lien and Vuong's (1987) test with a...

Persistent link: https://www.econbiz.de/10014054565

A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims

Hong, Liang - 2016

Accurate prediction of future claims is a fundamentally important problem in insurance. The Bayesian approach is natural in this context, as it provides a complete predictive distribution for future claims. The classical credibility theory provides a simple approximation to the mean of that...

Persistent link: https://www.econbiz.de/10013002976

Quantile Regression for Peak Demand Forecasting

Gibbons, Charles - 2014

quantile regression (OFQR), that uses quantile regression to estimate a model of daily peak demand, then uses a loss function …

Persistent link: https://www.econbiz.de/10013048663

A Tutorial on Opinion Polls for Elections and Marketing Research Using Five Approaches : Logistic Regression, Discriminant Analysis, Neural Networks with Factor Analysis, Wavelets...

Giovanis, Eleftherios - 2008

In this work we show a briefly presentation of four approaches to opinion polls. The example we present here, is referred on exit polls which have been realized for the elections of Serres Municipal in Greece on October 22nd of 2006. The methodology can be applied in any opinion poll, not only...

Persistent link: https://www.econbiz.de/10012723227

Package CovRegpy : Regularised Covariance Regression and Forecasting in Python

van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023

) framework, Regularised Covariance Regression (RCR) framework, Risk Premia Parity (RPP) weighting functions, Singular Spectrum … covariance regression framework, implicit factors can be used in the traditional explicit market factor setting and RPP … were developed out of seeking to develop Covariance Regression for financial applications …

Persistent link: https://www.econbiz.de/10014253907

The Bayesian approach to capital allocation at operational risk : a combination of statistical data and expert opinion

Habachi, Mohamed; Benbachir, Saâd - In: International Journal of Financial Studies : open … 8 (2020) 1/9, pp. 1-24

Operational risk management remains a major concern for financial institutions. Indeed, institutions are bound to manage their own funds to hedge this risk. In this paper, we propose an approach to allocate one's own funds based on a combination of historical data and expert opinion using the...

Persistent link: https://www.econbiz.de/10012168944

Automated and distributed statistical analysis of economic agent-based models

Vandin, Andrea; Giachini, Daniele; Lamperti, Francesco; … - 2020

We propose a novel approach to the statistical analysis of simulation models and, especially, agent-based models (ABMs). Our main goal is to provide a fully automated and model-independent tool-kit to inspect simulations and perform counter-factual analysis. Our approach: (i) is easy-to-use by...

Persistent link: https://www.econbiz.de/10012308914