scholarly journals Comparison of certain value-at-risk estimation methods for the two-parameter Weibull loss distribution

2011 ◽  
Vol 235 (11) ◽  
pp. 3304-3314 ◽  
Author(s):  
Omer L. Gebizlioglu ◽  
Birdal Şenoğlu ◽  
Yeliz Mert Kantar
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Estimation Methods ◽  
Loss Distribution ◽  
Two Parameter

Ranking the predictive performances of value-at-risk estimation methods

2012 ◽  
Vol 28 (4) ◽  
pp. 849-873 ◽  
Author(s):  
Emrah Şener ◽  
Sayad Baronyan ◽  
Levent Ali Mengütürk
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Estimation Methods

scholarly journals Improving Value-at-Risk Estimation from the Normal EGARCH Model

2017 ◽  
Vol 11 (1) ◽  
pp. 91-106 ◽  
Author(s):  
Mahsa Gorji ◽  
Rasoul Sajjad
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Egarch Model

scholarly journals Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

2017 ◽  
Vol 65 (5) ◽  
pp. 1687-1694
Author(s):  
Tomáš Konderla ◽  
Václav Klepáč
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Listed Company ◽  
Mixture Distribution ◽  
Normal Mixture ◽  
Development Characteristic ◽  
The Common

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA‑GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal‑mixture distribution against previously used GARCH with many types of non‑normal innovations.

Value-at-risk performance in emerging and developed countries

2018 ◽  
Vol 14 (5) ◽  
pp. 591-612
Author(s):  
Luiz Eduardo Gaio ◽  
Tabajara Pimenta Júnior ◽  
Fabiano Guasti Lima ◽  
Ivan Carlin Passos ◽  
Nelson Oliveira Stefanelli
Keyword(s):  
At Risk ◽  
Financial Crises ◽  
Value At Risk ◽  
Risk Estimation ◽  
Developed Countries ◽  
Emerging Countries ◽  
Copula Functions ◽  
Predictive Capacity ◽  
Arch Models ◽  
Content Type

Purpose The purpose of this paper is to evaluate the predictive capacity of market risk estimation models in times of financial crises. Design/methodology/approach For this, value-at-risk (VaR) valuation models applied to the daily returns of portfolios composed of stock indexes of developed and emerging countries were tested. The Historical Simulation VaR model, multivariate ARCH models (BEKK, VECH and constant conditional correlation), artificial neural networks and copula functions were tested. The data sample refers to the periods of two international financial crises, the Asian Crisis of 1997, and the US Sub Prime Crisis of 2008. Findings The results pointed out that the multivariate ARCH models (VECH and BEKK) and Copula-Clayton had similar performance, with good adjustments in 100 percent of the tests. It was not possible to perceive significant differences between the adjustments for developed and emerging countries and of the crisis and normal periods, which was different to what was expected. Originality/value Previous studies focus on the estimation of VaR by a group of models. One of the contributions of this paper is to use several forms of estimation.

Estimation Methods for Value at Risk

2016 ◽  
pp. 283-356 ◽  
Author(s):  
Saralees Nadarajah ◽  
Stephen Chan
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Estimation Methods
Sign in / Sign up

Export Citation Format

Share Document