scholarly journals Modelling Asymmetric Dependence Using Copula Functions: An Application to Value-at-Risk in the Energy Sector

2009 ◽  
Author(s):  
Andrea Bastianin
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Energy Sector ◽  
Copula Functions ◽  
Asymmetric Dependence

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.

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

2021 ◽  
Vol 5 (2) ◽  
pp. 405-414
Author(s):  
Hasna Afifah Rusyda ◽  
Fajar Indrayatna ◽  
Lienda Noviyanti
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Joint Probability ◽  
Extreme Value Distribution ◽  
Copula Functions ◽  
Tail Distribution ◽  
Block Maxima ◽  
Dependent Model ◽  
Portfolio Return

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.

scholarly journals Penerapan Metode GARCH-Vine Copula untuk Estimasi Value at Risk (VaR) pada Portofolio

2018 ◽  
Vol 7 (2) ◽  
pp. 63-77 ◽  
Author(s):  
Herida Okta Pintari ◽  
Retno Subekti
Keyword(s):  
At Risk ◽  
Linear Correlation ◽  
Value At Risk ◽  
Marginal Distribution ◽  
Measurement Instrument ◽  
Copula Function ◽  
Copula Functions ◽  
Non Linear ◽  
Vine Copula ◽  
T Distribution

Salah satu alat ukur yang digunakan untuk menghitung risiko portofolio adalah Value at Risk (VaR). Beberapa metode pengukuran VaR mengasumsikan return berdistribusi normal dan ukuran dependensi antar saham menggunakan korelasi linear. Faktanya, asumsi normalitas pada data finansial jarang terpenuhi dan terdapat indikasi adanya heteroskedastisitas. Selain itu, kebergantungan antar saham yang non-linear tidak sesuai apabila diukur dengan korelasi linear. Penyimpangan ini menyebabkan tidak validnya estimasi VaR. Tujuan dari penelitian ini adalah untuk mengetahui penerapan metode GARCH-Vine Copula untuk estimasi VaR pada portofolio. Vine Copula adalah fungsi distribusi multivariat yang menggabungkan distribusi marginal return univariat dalam portofolio, dan dapat menggambarkan struktur kebergantungan non-linearnya. Vine Copula dapat dilakukan dengan menentukan dekomposisi Vine Copula dan fungsi keluarga copulanya. Dekomposisi Vine Copula dilakukan dengan menggunakan C-Vine dan D-Vine Copula. Kemudian dengan menggunakan fungsi copula keluarga Archimedean, yaitu Clayton, Gumbel, dan Frank dapat ditentukan distribusi bersamanya. Pembentukan distribusi marginal menggunakan model GARCH berdistribusi Student-t digunakan untuk mengatasi adanya heteroskedastisitas. Hasil penerapan dari tiga saham perbankan, yaitu BBNI, BBRI, dan BMRI periode 26 Agustus 2013 hingga 20 November 2017 diperoleh model D-Vine Copula dengan fungsi copula Frank adalah model terbaik untuk memodelkan data, dengan nilai VaR sebesar 1,86%, 2,56%, dan 4,49% dari dana investasi pada tingkat kepercayaan 90%, 95%, dan 99%. [One of the measurement instrument that are used to calculate the risk of portfolio is Value at Risk (VaR). Several methods of measuring VaR assumes normal and the size of dependencies return between the stock using a linear correlation. Basically, the assumption normal in financial data is violated and the possibility of heteroscedasticity is indicated. In addition, dependences non-linear is not appropriate when measured with a linear correlation. This deviation causes invalidity VaR estimation. The purpose of this research is to know the application of GARCH-Vine Copula method for estimation of VaR on portfolio. Vine Copula is a multivariate distribution function that combines the univariate marginal distribution of return in portfolio, and it can describe the structure of dependencies non-linear. Vine Copula can be done by determining the decomposition of Vine Copula and its copula family function. Vine Copula decomposition is using C-Vine and D-Vine Copula. Then by using the copula function of the Archimedean family, namely Clayton, Gumbel, and Frank can be determined the joint distribution. The facts, the formation of the marginal distribution of GARCH model using the student-t distribution used to overcome the presence of heteroscedasticity. The result of the application of these stocks namely BBNI, BBRI, and BMRI from 26 August 2013 to 20 November 2017 has shown model D-Vine Copula copula functions with Frank is the best one to model the data. So, the VaR estimation at 90%, 95%, and 99% confidence levels are 1,86%, 2,56%, and 4,49% respectively of the invested funds.]

Extremwerttheorie und Value-at-Risk

2015 ◽  
Vol 44 (5) ◽  
pp. 259-267
Author(s):  
Frank Schuhmacher ◽  
Benjamin R. Auer
Keyword(s):  
At Risk ◽  
Value At Risk

Value at Risk

Controlling ◽  
2004 ◽  
Vol 16 (7) ◽  
pp. 425-426
Author(s):  
Mischa Seiter ◽  
Sven Eckert
Keyword(s):  
At Risk ◽  
Value At Risk
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