scholarly journals Non-extensive value-at-risk estimation during times of crisis

2021 ◽  
pp. 2150099
Author(s):  
Ahmad Hajihasani ◽  
Ali Namaki ◽  
Nazanin Asadi ◽  
Reza Tehrani
Keyword(s):  
At Risk ◽  
Financial Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Specific Pattern ◽  
Gaussian Probability Density Function ◽  
Confidence Levels ◽  
Control Instrument ◽  
The Difference ◽  
Non Gaussian

Value-at-risk (VaR) is a crucial subject that researchers and practitioners extensively use to measure and manage uncertainty in financial markets. Although VaR is a standard risk control instrument, there are criticisms about its performance. One of these cases, which has been studied in this research, is the VaR underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies, and the estimated VaRs by typical models are lower than the real values. A potential approach that can be used to describe the non-Gaussian behavior of return series is the Tsallis entropy framework and nonextensive statistical methods. This paper has used the nonextensive models for analyzing financial markets’ behavior during crisis times. By applying the q-Gaussian probability density function for emerging and mature markets over 20 years, we can see a better VaR estimation than the regular models, especially during crisis times. We have shown that the q-Gaussian models composed of VaR and Expected Shortfall (ES) estimate risk better than the standard models. By comparing the ES, VaR, [Formula: see text]-VaR and [Formula: see text]-ES for emerging and mature markets, we see in confidence levels more than 0.98, the outputs of q models are more real, and the [Formula: see text]-ES model has lower errors than the other ones. Also, it is evident that in the mature markets, the difference of VaR between normal condition and nonextensive approach increases more than one standard deviation during times of crisis. Still, in the emerging markets, we cannot see a specific pattern. The findings of this paper are useful for analyzing the risk of financial crises in different markets.

scholarly journals Return distribution and value at risk estimation for BELEX15

2011 ◽  
Vol 21 (1) ◽  
pp. 103-118 ◽  
Author(s):  
Dragan Djoric ◽  
Emilija Nikolic-Djoric
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Var Model ◽  
Ratio Test ◽  
Sample Period ◽  
Return Distribution ◽  
Model Accuracy ◽  
Confidence Levels

The aim of this paper is to find distributions that adequately describe returns of the Belgrade Stock Exchange index BELEX15. The sample period covers 1067 trading days from 4 October 2005 to 25 December 2009. The obtained models were considered in estimating Value at Risk ( VaR ) at various confidence levels. Evaluation of VaR model accuracy was based on Kupiec likelihood ratio test.

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

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.

scholarly journals ESTIMATING VALUE-AT-RISK BASED ON NON-NORMAL DISTRIBUTIONS

2015 ◽  
Vol 3 ◽  
pp. 188-195 ◽  
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
At Risk ◽  
Risk Factor ◽  
Value At Risk ◽  
Likelihood Method ◽  
Normal Distributions ◽  
Confidence Levels ◽  
Portfolio Returns ◽  
Distribution Parameters ◽  
Financial Portfolios ◽  
Daily Changes

The article presents a comparative study of parametric linear value-at-risk (VaR) models used for estimating the risk of financial portfolios. We illustrate how to adjust VaR for auto-correlation in portfolio returns. The article presents static and dynamic methodology to compute VaR, based on the assumption that daily changes are independent and identically distributed (normal or non-normal) or auto-correlated in terms of the risk factor dynamics. We estimate the parametric linear VaR over a risk horizon of 1 day and 10 days at 99% and 95% confidence levels for the same data. We compare the parametric VaR and a VaR obtained using Monte Carlo simulations with historical simulations and use the maximum likelihood method to calibrate the distribution parameters of our risk factors. The study investigated whether the parametric linear VaR applies to contemporary risk factor analysis and pertained to selected foreign rates.

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.

Sign in / Sign up

Export Citation Format

Share Document