Selection of Value‐at‐Risk models

Sarma, Mandira; Thomas, Susan; Shah, Ajay

doi:10.1002/for.868

Cited by 161 publications

(134 citation statements)

References 13 publications

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“…We also use a newly introduced loss function that incorporates not only the number of violations but also their magnitude. This differs from previous approaches (see Lopez (1998), Blanco and Ihle (1999) and Sarma et al (2003)) as it is not biased on the number of violations. Instead it considers equally the number of violations and their average magnitude.…”

Section: Introductionmentioning

confidence: 63%

“…The weakness of Christoffersen tests is that they are unable to distinguish between different, but close, alternative models (see Sarma et al (2003), Cakici and Foster (2003) and Fantazzini (2009)). Therefore, in order to further distinguish our models we follow another approach based on loss functions.…”

Section: Resultsmentioning

confidence: 99%

“…Different kinds of loss functions have already been proposed by Lopez (1998), Blanco and Ihle (1999) and Sarma et al (2003). However, all functions proposed in these papers depend crucially on the number of exceptions.…”

Section: Proposed Loss Functionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

Sermpinis

Laws

Dunis³

2013

The European Journal of Finance

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The motivation for this paper is to investigate the use of a promising class of neural network models, Psi Sigma, when applied to the task of forecasting the one day ahead Value at Risk (VaR) of the oil Brent and gold bullion series using Open-High-Low-Close data. In order to benchmark our results we also consider VaR forecasts from two different neural network designs, the Multilayer Perceptron (MLP) and the Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an Extreme Value Theory model (EVT) along with some traditional techniques such as an ARMA-GJR (1,1) model and the Riskmetrics volatility. The forecasting performance of all models for computing the VaR of the Brent oil and the gold bullion is examined over the period September 2001 to August 2010 using the last year and half of data for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms such as the Christoffersen tests, the violation ratio and our proposed loss function that considers not only the number of violations but also their magnitude.Our results show that the Psi Sigma outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations with small magnitude.

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Section: Introductionmentioning

confidence: 63%

Section: Resultsmentioning

confidence: 99%

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Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

Sermpinis

Laws

Dunis³

2013

The European Journal of Finance

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“…Function (41) includes only the fact of exceptions, and does not take into account the size of the losses arising from an extremal market movement. The second loss function proposed by Lopez (1999) contains both the magnitude and the number of exceptions: Regulatory loss (RL) is expressed as follows (Sarma et al, 2003):…”

Section: Weighted Averaged Combining Optimized Using Exponential Weigmentioning

confidence: 99%

Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts

Ratuszny

2016

DEM

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A b s t r a c t. The aim of the research is to compare VaR methods/models for commodities. For risk measurement Conditional Autoregressive Value at Risk models (CAViaR), implied quantile model and encompassing method are used. The aim is to check whether simultaneous use of information both from historical time series and regarding markets' expectation can improve accuracy of forecasts. For this purpose four methods of combining forecasts are used: a simple average combining, an unrestricted linear combination, a weighted averaged combining and a weighted averaged combining using exponential weighting. In the case of the commodities neither the encompassing method nor the combining forecast method improve VaR forecasts. The method of choosing the most adequate model leads to simple CAViaR-SAV model as the source of most optimal measure of risk forecasts. The Kupiec test, the Christoffersen and the Dynamic Quantile test indicate the model as an adequate to forecast VaR for gold and oil for short positions at the 0.01 and the 0.05 significance level, and for a long position at the 0.05 significance level.K e y w o r d s: CAViaR, VaR, encompassing method, combined forecasts, commodities J E L Classification: C22, G17.

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“…We extended the forecast evaluation approach of Lopez (1999) and Sarma et al (2003) as the ES was introduced in the second stage by creating a loss function that calculated the difference between the actual and the expected loss when a violation occurred. For all the best-performing models of the first stage, we implemented Hansen's (2005) superior predictive ability (SPA) test to evaluate their differences statistically.…”

Section: Evaluate Var and Es Forecastsmentioning

confidence: 99%

Backtesting VaR models:a two-stage procedure

Angelidis¹,

Degiannakis²

2007

JRMV

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Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely accepted. A two-stage backtesting procedure is proposed in order a model that not only forecasts VaR but also predicts the loss beyond VaR to be selected. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets (US stock, gold and dollar-pound exchange rate markets), long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models.

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Selection of Value‐at‐Risk models

Cited by 161 publications

References 13 publications

Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts

Backtesting VaR models:a two-stage procedure

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