Estimation of value at risk by using gjr-garch copula based on block maxima

This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show VaR can estimate the risk of portfolio return reasonably because the model has captured the data properties. Data volatility can be accommodated by GJR-GARCH, Copula can capture dependence between stocks, and Block maxima can accommodate extreme tail behavior of the data.

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Value-at-risk performance in emerging and developed countries

International Journal of Managerial Finance ◽

10.1108/ijmf-10-2017-0244 ◽

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.

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

Journal of Computational and Applied Mathematics ◽

10.1016/j.cam.2011.01.044 ◽

2011 ◽

Vol 235 (11) ◽

pp. 3304-3314 ◽

Cited By ~ 18

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

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Value-at-Risk Estimation via a Semi-parametric Approach: Evidence from the Stock Markets

Handbook of Financial Econometrics and Statistics ◽

10.1007/978-1-4614-7750-1_51 ◽

2014 ◽

pp. 1399-1430 ◽

Cited By ~ 2

Author(s):

Cheng-Few Lee ◽

Jung-Bin Su

Keyword(s):

At Risk ◽

Stock Markets ◽

Value At Risk ◽

Risk Estimation ◽

Parametric Approach

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

Contemporary Economics ◽

10.5709/ce.1897-9254.230 ◽

2017 ◽

Vol 11 (1) ◽

pp. 91-106 ◽

Cited By ~ 1

Author(s):

Mahsa Gorji ◽

Rasoul Sajjad

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Estimation ◽

Egarch Model

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

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201765051687 ◽

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.

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