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The purpose of this paper is to present the Greek value at risk (VaR) legislation framework and to highlight some of its major deficiencies, using not only theoretical scenarios but also empirical evidence. Moreover, this paper does not only highlight the VaR legislation’s framework deficiencies but also suggests legal interventions for its revision and a new-alternative, flexible and simple-to-be-applied filtered estimation method which improves the VaR evaluations.

The Greek legislation framework suggests that for the daily VaR to be estimated, a minimum data set of the previous year (250 observations) at the 99 per cent confidence level should be considered. This approach may lead to inaccurate VaR estimations, for example, when after a long-term growth period, there is a sudden recession period, because the data input is not representative to the current financial environment. Taking into serious consideration that high volatility periods are linked to a financial crisis, it is assumed that volatility could be an indicator for the financial environment representation. The conventional historical VaR back-tested results suggest that the specific methodology should be revised, especially during the high volatility period. For the newly suggested filtered VaR, the data sample is divided into several regimes depending on the volatility range. The filtered VaR estimation process applies the conventional historical methodology but uses different historical data input depending on the current volatility. This new approach improves the VaR estimation by reducing the VaR daily violations.

The findings regarding the current legislation framework suggest that the financial analysts in Greece have a motivation to adopt a relative VaR approach for risk asset class portfolios (e.g. Greek domestic equity mutual funds), which enables them to bear increased risk without presenting it to the investors. For lower risk portfolios, the absolute VaR may be useful for increased risk bearing strategies. The stricter VaR approaches are preferred to be adopted because stricter VaR estimations are linked to a reduced number of violations. The filtered volatility approach improves the VaR estimations (fewer violations are relative to the conventional approach).

This methodology is designed to be applied for the VaR estimation, but it could be partly applied in other fields of the financial analysis study.

The suggested methodology could present efficient VaR estimation without using sophisticated procedures or expensive VaR systems. Therefore, it could be easily applied by the risk analysts. Moreover, the overview of the Greek legislation’s framework could be useful not only for the Greek regulators but also for the authorities in countries with a similar regulation.

The newly proposed methodology is so accurate and simple to apply that it could have far-reaching impact on practitioners. Finally, this is the first paper that examines the Greek VaR legislation framework in detail.

A misplaced reliance on value at risk (VaR) has been focused on in the media as one of the main reasons for the current financial crisis, and the recently published Turner Review by the UK Financial Services Authority concurs. The purpose of this paper is to present an introductory overview of VaR and its weaknesses which will be easily understood by non‐technical readers.

Simple numerical examples utilising real and simulated data are employed to reinforce the main arguments.

This paper explains that some of the main approaches employed by banks for computing VaR have serious weaknesses. These weaknesses have contributed to the current financial crisis.

Consistent with the introductory nature of this paper, the empirical research is limited to simple examples.

The evidence here suggest that if VaR is to play a major role under future financial regulation then research is required to develop improved estimation techniques and backtesting procedures.

This paper differs from many academic papers on VaR by assuming only a very basic knowledge of mathematics and statistics.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

The purpose of this paper is to compare the performance of commonly used value at risk (VaR) estimation methods for equity indices from both developed countries and emerging markets.

In addition to traditional time‐series models, this paper examines the recently developed nonparametric kernel estimator (KE) approach to predicting VaR. KE methods model tail behaviors directly and independently of the overall return distribution, so are better able to take into account recent extreme shocks.

The paper compares the performance and reliability of five major VaR methodologies, using more than 26 years of return data on 37 equity indices. Through back‐testing of the resulting models on a moving window and likelihood ratio tests, it shows that KE models produce remarkably good VaR estimates and outperform the other common methods.

Financial assets are known to have irregular return patterns; not only the volatility but also the distributions themselves vary over time. This analysis demonstrates that a nonparametric approach (the KE method) can generate reliable VaR estimates and accurately capture the downside risk.

The paper evaluates the performance of several common VaR estimation approaches using a comprehensive sample of empirical data. The paper also reveals that kernel estimation methods can achieve remarkably reliable VaR forecasts. A detailed and complete investigation of nonparametric estimation methods will therefore significantly contribute to the understanding of the VaR estimation processes.

The purpose of this paper is to discuss two important extensions to the well‐known value‐at‐risk (VaR) methodology, namely extreme value theory (EVT) and expected shortfall (ES). Both of these extensions address the weaknesses of VaR, in particular the methodology's tendency to systematically underestimate risk of extreme market events.

The theory of VaR and the two extensions are reviewed and the methodology is evaluated in light of the Basel II regulatory framework that calls for the use of VaR by financial institutions.

The paper clarifies the use of VaR and its extensions to make practitioners more aware of the pitfalls and how to address them. It is recommended that the two extended measures of extreme event risk (i.e. EVT and ES) be included into every risk manager's information pool.

A compact review of these approaches and their regulatory connection has not previously been compiled. This review is of particular value to risk managers and policy markers given the turbulent market conditions of the past year.

The purpose of this paper is to provide proactive risk management techniques and strategies that can be applied to trading and investment portfolios in emerging and Islamic illiquid financial markets, such as the Moroccan foreign exchange and stock markets.

This paper demonstrates a practical approach for the measurements, management and control of market risk exposure for financial portfolios that contain illiquid foreign exchange and equity securities. This approach is based on the renowned concept of value‐at‐risk (VAR) along with the innovation of a software tool utilizing matrix‐algebra technique.

In order to illustrate the proper use of VAR and stress‐testing methods, real‐world examples and feasible reports of risk management are presented for the Moroccan financial markets. To this end, several case studies were achieved with the objective of creating a realistic framework of trading risk measurement and control reports in addition to the inception of procedures for the calculation of VAR limits.

The versatile risk management procedures that are discussed in this work will be of value to financial entities, regulators and policymakers operating within the context of emerging and Islamic markets. The risk management procedures that are outlined in this paper will aid in setting‐up of realistic policies for the management of trading/investment risk exposures in illiquid markets. The document includes comprehensive theory, analyses sections, conclusions and recommendations, and full viable risk management reports.

Even though considerable literatures have investigated the statistical and economic significance of VAR models, this article provides real‐world techniques and optimum asset allocation strategies that are useful for trading/investment portfolios in emerging and Islamic financial markets. This is with the objective of setting‐up the basis of a proactive methodology/procedure for the measurement, management and control of equity and foreign exchange exposures in the day‐to‐day trading/investment operations.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

The last few years in the financial markets have shown great instability and high volatility. In order to capture the amount of risk a financial firm takes on in a single trading day, risk managers use a technology known as value‐at‐risk (VaR). There are many methodologies available to calculate VaR, and each has its limitations. Many past methods have included a normality assumption, which can often produce misleading figures as most financial returns are characterized by skewness (asymmetry) and leptokurtosis (fat‐tails). The purpose of this paper is to provide an overview of VaR and describe some of the most recent computational approaches.

This paper compares the Student‐t, autoregressive conditional heteroskedastic (ARCH) family of models, and extreme value theory (EVT) as a means of capturing the fat‐tailed nature of a returns distribution.

Recent research has utilized the third and fourth moments to estimate the shape index parameter of the tail. Other approaches, such as extreme value theory, focus on the extreme values to calculate the tail ends of a distribution. By highlighting benefits and limitations of the Student‐t, autoregressive conditional heteroskedastic (ARCH) family of models, and the extreme value theory, one can see that there is no one particular model that is best for computing VaR (although all of the models have proven to capture the fat‐tailed nature better than a normal distribution).

This paper details the basic advantages, disadvantages, and mathematics of current parametric methodologies used to assess value‐at‐risk (VaR), since accurate VaR measures reduce a firm's capital requirement and reassure creditors and investors of the firm's risk level.

The purpose of this paper is to originate a proactive approach for the quantification and analysis of liquidity risk for trading portfolios that consist of multiple equity assets.

The paper presents a coherent modeling method whereby the holding periods are adjusted according to the specific needs of each trading portfolio. This adjustment can be attained for the entire portfolio or for any specific asset within the equity trading portfolio. This paper extends previous approaches by explicitly modeling the liquidation of trading portfolios, over the holding period, with the aid of an appropriate scaling of the multiple‐assets' liquidity‐adjusted value‐at‐risk matrix. The key methodological contribution is a different and less conservative liquidity scaling factor than the conventional root‐t multiplier.

The proposed coherent liquidity multiplier is a function of a predetermined liquidity threshold, defined as the maximum position which can be unwound without disturbing market prices during one trading day, and is quite straightforward to put into practice even by very large financial institutions and institutional portfolio managers. Furthermore, it is designed to accommodate all types of trading assets held and its simplicity stems from the fact that it focuses on the time‐volatility dimension of liquidity risk instead of the cost spread (bid‐ask margin) as most researchers have done heretofore.

Using more than six years of daily return data, for the period 2004‐2009, of emerging Gulf Cooperation Council (GCC) stock markets, the paper analyzes different structured and optimum trading portfolios and determine coherent risk exposure and liquidity risk premium under different illiquid and adverse market conditions and under the notion of different correlation factors.

This paper fills a main gap in market and liquidity risk management literatures by putting forward a thorough modeling of liquidity risk under the supposition of illiquid and adverse market settings. The empirical results are interesting in terms of theory as well as practical applications to trading units, asset management service entities and other financial institutions. This coherent modeling technique and empirical tests can aid the GCC financial markets and other emerging economies in devising contemporary internal risk models, particularly in light of the aftermaths of the recent sub‐prime financial crisis.