Heavy-tailed value-at-risk analysis for Malaysian stock exchange

Cheong, Chin Wen

doi:10.1016/j.physa.2008.01.075

Cited by 21 publications

(8 citation statements)

References 20 publications

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“…Giot (2005) used GARCH model and Riskmetrics model with residuals following normal distribution and Student t distribution to study VaR of three stocks traded on NYSE in 15 and 30 min. Chin (2008) compared the power of VaR under quantile and nonlinear time-varying volatility, proposed a simple Pareto distribution to explain the fat tail property in the empirical distribution of return, and implemented the measure of non-parametric quantile estimation of VaR using interpolation method. Batten et al (2014) used the modified version of the multifractal model of asset returns (MMAR) with a series of asset returns data characterized by second.…”

Section: Shrinkage Estimationmentioning

confidence: 99%

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Sun¹,

Liu

et al. 2019

JRFM

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The literature on portfolio selection and risk measurement has considerably advanced in recent years. The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk measurement. In the summary of the characteristics of financial data, we summarize the literature on fat tail and dependence characteristic of financial data. In the portfolio selection model part, we cover three models: mean-variance model, global minimum variance (GMV) model and factor model. In the portfolio risk measurement part, we first classify risk measurement methods into two categories: moment-based risk measurement and moment-based and quantile-based risk measurement. Moment-based risk measurement includes time-varying covariance matrix and shrinkage estimation, while moment-based and quantile-based risk measurement includes semi-variance, VaR and CVaR.

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Section: Shrinkage Estimationmentioning

confidence: 99%

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Sun¹,

Liu

et al. 2019

JRFM

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“…Según Cheong (2008), las estimaciones no paramétricas tienen las ventajas de la simplicidad y -lo que es más importante-la no necesidad de un supuesto de distribución específico para la serie de rendimientos, mientras que el cálculo del VaR basado en un modelo paramétrico requiere estimaciones precisas de la volatilidad y un cuantil correspondiente a la distribución empírica. En esta línea, Sadegui y Shavvalpour (2006) afirman que los estudios previos sobre la estimación del VaR basada en modelos se han centrado principalmente en la caracterización de los hechos estilizados que se encuentran con frecuencia en los datos financieros, especifican la estructura variable de la volatilidad y la forma de la distribución del rendimiento de los activos.…”

Section: Breve Revisión De Literatura: Valor En Riesgounclassified

“…De allí que se hayan desarrollado modelos que incorporan propiedades adicionales, como la asimetría, la cual se refiere a los impactos diferenciales de choques positivos y negativos de igual magnitud en la volatilidad. Según Cheong (2008), esta propiedad es muy importante en el análisis de riesgo en el cual las inversiones en posiciones largas y cortas durante un período de tiempo determinado se basan en gran medida en los comportamientos de las colas inferiores y superiores.…”

Section: Breve Revisión De Literatura: Valor En Riesgounclassified

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Valoración de riesgo mediante modelos GARCH y simulación Montecarlo: evidencia del mercado accionario colombiano

Camargo¹,

Sarmiento²,

Gómez³

2019

Semest. Econ.

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Este documento evalúa el comportamiento de varios modelos de volatilidad en estimaciones de un día del valor en riesgo (VaR) de veinticuatro series de retornos de acciones en Colombia con diferentes distribuciones. Al considerar que todas las series de retornos presentan clúster de volatilidad y memoria de largo plazo, se utilizan modelos tipo GARCH que incluyen diferentes distribuciones: normal, T-Student y GED. Los hallazgos corroboran la dificultad de elegir un único modelo para el cálculo del VaR, pero validan el uso de modelos paramétricos con distribución normal y simulación Montecarlo en mercados financieros emergentes.

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“…(e.g. Ruppert, 2004;Jorion, 2007;Haas, 2009;Dimitrakopoulos et al, 2010;Chin, 2008). These various ways to estimate VaR can yield widely different results (Beder, 1995;Pritsker, 1997, etc.).…”

Section: Introductionmentioning

confidence: 99%

A New Perspective on Daily Value at Risk Estimates

Dryver¹,

Nathaphan²

2012

IJEF

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Daily value at risk (VaR) estimates are sometimes calculated as if the institution is only concerned about short-term performance or risk position. In reality though, a risk manager may not consider changing the investment allocation in the foreseeable future, and with a highly-leveraged position daily VaR could be very misleading in terms of true risk to the financial institution. This paper recommends looking at VaR, taking the possibility that a financial institution will use the same assest allocation over a longer period of time while borrowing at over night rates. Finally, the paper introduces a more conservative estimate than the traditional VaR estimates.

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Heavy-tailed value-at-risk analysis for Malaysian stock exchange

Cited by 21 publications

References 20 publications

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

Valoración de riesgo mediante modelos GARCH y simulación Montecarlo: evidencia del mercado accionario colombiano

A New Perspective on Daily Value at Risk Estimates

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