Analyzing value at risk and expected shortfall methods: the use of parametric, non-parametric, and semi-parametric models
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Date
2014-08-25
Authors
Huang, Xinxin
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Abstract
Value at Risk (VaR) and Expected Shortfall (ES) are methods often used to measure market risk. Inaccurate and unreliable Value at Risk and Expected Shortfall models can lead to underestimation of the market risk that a firm or financial institution is exposed to, and therefore may jeopardize the well-being or survival of the firm or financial institution during adverse markets. The objective of this study is therefore to examine various Value at Risk and Expected Shortfall models, including fatter tail models, in order to analyze the accuracy and reliability of these models.
Thirteen VaR and ES models under three main approaches (Parametric, Non-Parametric and Semi-Parametric) are examined in this study. The results of this study show that the proposed model (ARMA(1,1)-GJR-GARCH(1,1)-SGED) gives the most balanced Value at Risk results. The semi-parametric model (Extreme Value Theory, EVT) is the most accurate Value at Risk model in this study for S&P 500.
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Risk Management, Volatility Estimate, Value at Risk, GARCH, ARMA, General Error Distribution (GED), ARMA(1,1)-GJR-GARCH(1,1)-SGED, Extreme Value Theory (EVT), General Pareto Distribution (GPD), Expected Shortfall (ES), Conditional Tail Expectation (CTE), Conditional Value at Risk (CVaR)