The use of GARCH models in VaR estimation

Angelidis, Timotheos; Benos, Alexandros; Degiannakis, Stavros

doi:10.1016/j.stamet.2004.08.004

Cited by 218 publications

(144 citation statements)

References 45 publications

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“…The econometric and the computational advances enable financial economists and scholars to use more complicated procedures than the three mainstream methods, in order to achieve more accurate VaR estimations. Some of the advanced econometric approaches in recent years are: the filtered historical simulation [15], extreme value theory [16], Ozun et al [17], non-parametric Kernel Estimators [18], GARCH family modeling [19], Degiannakis et al [20], Markov Switching Regime [21], copulas [22], while a significant amount of technical literature that presents the VaR modeling has been documented [23], Alexander [24].…”

Section: Value At Risk: Practitioners Side Regulatorymentioning

confidence: 99%

Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?

Vasileiou¹

2017

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The purpose of this paper is to suggest a new approach that improves the conventional Historical Value-at-Risk (HVaR) estimations' accuracy and can be easily applied by anyone. The main assumption for the newly suggested method is "the more representative to the financial conditions the data inputs are, the better the VaR estimations". Volatility is assumed to be the criterion for the "representative to the financial conditions" definition. In practice, the newly suggested approach does not use the previous x days observations as data inputs in the estimation process (as the HVaR does), but the last x filtered volatility (fv) observations of a "representative to the current financial conditions dataset". Depending on the volatility value, each observation is classified in several regimes, from which the VaR is estimated depending on the examined day's volatility. This way the HVaR approach is more historical. The empirical findings using data from the US and the Eurozone stock market show that the newly suggested filtered volatility approach not only significantly improves the VaR estimations, but also makes these estimations much more representative of the real financial conditions. The results using the filtered volatility approach are comparable to some previously documented VaR estimations that adopt advanced econometric models. In this point, we should note advanced econometric models have the drawbacks that are not usually applied in financial markets industry because of their complexity. The newly suggested approach: (i) popularizes some of the most advanced econometric techniques, (ii) improves the VaR estimations accuracy, and (iii) enables financial risk analysts and portfolio managers to estimate the risk-return under several volatility regimes in order to help them to apply their desired investment strategy. Finally, this paper not only examines accuracy using the traditional/conventional tests [1], but also suggests some new measures for the comparison amongst different VaR models and their ability to accurately estimate the real financial risk.

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Section: Value At Risk: Practitioners Side Regulatorymentioning

confidence: 99%

Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?

Vasileiou¹

2017

TEL

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“…En este trabajo se comparará el comportamiento de los modelos que emplean volatilidades estimadas por el procedimiento RM con los modelos que incorporan volatilidades estimadas con modelos GARCH(1,1) que constituyen una metodología Stupariu, Patricia; Ruiz, Juan Rafael; Vilariño, Ángel Sobre los modelos GARCH existe también una extensa literatura que recoge sus características y aplicaciones en el análisis de series financieras de rentabilidades, destacando su buena capacidad predictiva (Angelidis et al, 2004;Bera & Higgins, 1993;Bhattacharyya, 2012;Engle, 2001;McMillan & Kambouroudis, 2009;So & Yu, 2006). …”

Section: Elección De Los Modelos Y La Carteraunclassified

Modelos VaR para calcular el capital mínimo regulatorio por riesgo de mercado

2015

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Fecha de recepción: Abril 2015 Fecha de aceptación: Junio 2015 RESUMENLa revisión de la regulación del riesgo de mercado de Basilea III contempla reemplazar los modelos VaR con una nueva métrica para el cómputo de los requerimientos mínimos de capital. En este trabajo se calcularán los requerimientos de capital por riesgo de mercado para una cartera de acciones del índice S&P500, entre el periodo 2000-2014 en base a la metodología RiskMetrics y alternativamente con modelos GARCH(1,1). Los resultados obtenidos muestran que el capital regulatorio calculado en base a las normas de Basilea II cubre en todo momento las pérdidas de la cartera. PALABRAS CLAVE:Valor en riesgo, Basilea, regulación financiera, riesgo de mercado. ABSTRACTThe undergoing overhaul of the Basel III market risk regulatory framework addresses the possibility of replacing VaR models with an alternative method for calculating minimum capital requirements. This paper will calculate the regulatory capital for a hypothetical equity portfolio of 20 of the main stocks in the S&P500, between 2000 and 2014. The RiskMetrics methodology and GARCH(1,1) models are used to estimate volatilities, covariances and correlations. Our results show that the regulatory capital calculated using Basel II rules is at all times above realized portfolio losses.

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“…total loss function is preferred over the other models. In the literature, different loss functions were proposed (see Lopez, 1998Lopez, , 1999Blanco and Ihle, 1998;Sarma, Thomas andShah, 2003, Caporin, 2003;Angelidis, Benos and Degiannakis, 2004). In this paper, the loss functions used to compare the accurate VaR forecasts are as follows: − The regulatory loss function -RL (Lopez, 1999) 7 : …”

Section: Backtesting Var Estimatesmentioning

confidence: 99%

The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures

Górka

2010

DEM

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The use of GARCH models in VaR estimation

Cited by 218 publications

References 45 publications

Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?

Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?

Modelos VaR para calcular el capital mínimo regulatorio por riesgo de mercado

The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures

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