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The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Lévy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices.

The authors apply tail-based and Lévy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Lévy models. As the Lévy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs.

Both EV and Lévy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance.

To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Lévy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.

This paper aims to compare two groups of stocks to analyze the efficiency of an ethical portfolio in comparison with a conventional portfolio.

Efficiency test by second-order stochastic dominance (SSD) approach is applied on two groups, which consist of 12 stocks. Ethical portfolio is chosen from the stocks complying with the participation banking rules. Conventional portfolio is selected from Borsa Istanbul (BIST) with choosing the corresponding stocks for each ethical stock according to the sector and market capitalization. All the stocks of both groups are pairwise SSD compared.

Both groups of 12 stocks are inefficient portfolios; however, a group of 7 stocks constitute an efficient ethical portfolio with the total weight of 50.82 per cent among the set of 12 ethical stocks. On the other hand, a group of 6 stocks constitute an efficient conventional portfolio, with the total weight of 45.16 per cent among the set of 12 conventional stocks. By pairwise SSD comparison of corresponding stocks from both groups, despite none of the conventional stocks dominate ethical stocks, four ethical stocks dominated the conventional ones.

Back-testing and comparison with benchmark BIST 100 Index have been done for the selected portfolios. According to back-testing results, groups of SSD efficient stocks outperformed the groups, from which they were selected. Furthermore, both SSD efficient portfolios have higher returns than benchmark index, BIST 100.

This paper measures different market risk impacts on options portfolios under the new Fundamental Review of the Trading Book (FRTB) regulation, issued in Basel and coming into effect in 2023.

This paper first suggests an algorithm for implementing the FRTB standardised approach via the sensitivities-based method to estimate a portfolio's risk capital and presents an illustration applied to an option position. Second, it proposes a methodology to estimate the expected shortfall in options portfolios from the FRTB internal models approach. In this regard, an application is developed to measure expected shortfall (ES) and value at risk (VaR) impacts under FRTB versus conventional VaR in a currency option position by considering stress scenarios from the 2007–9 and 2020–1 crises and back-testing procedures.

The suggested algorithm satisfactorily captures impacts via the sensitivities-based method, and higher risk capital demands are expected for emerging economies. Also, the planned FRTB methodology to measure ES and VaR is appropriate; in particular, historical metrics perform well. Astonishingly, their revealed impacts are more significant under the 2020–1 pandemic crisis than the 2007–9 financial crisis.

The proposals developed weave a communication bridge between the standardised and internal approaches of FRTB regulation, which can be scaled up technologically and institutionally.

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.

Purpose – This chapter aimed to investigate hedge funds market risk. One aims to go further the traditional measures of risk that underestimates it by introducing a more appropriate method to hedge funds. One demonstrates that daily hedge fund return distributions are asymmetric and leptokurtic. Furthermore, volatility clustering phenomenon and the existence of ARCH effects demonstrate that hedge funds volatility varies through time. These features suggest the modelisation of their volatility using symmetric (GARCH) and asymmetric (EGARCH and TGARCH) models used to evaluate a 1-day-ahead value at risk (VaR).

Methodology/Approach – The conditional variances were estimated under the assumption that residuals t follow the normal and the student law. The knowledge of the conditional variance was used to forecast 1-day-ahead VaR. The estimations are compared with the Gaussian, the student and the modified VaR. To sum up, 12 VaRs are computed; those based on standard deviation and computed with normal, student and cornish fisher quantile and those based on conditional volatility models (GARCH, TGARCH and EGARCH) computed with the same quantiles.

Findings – The results demonstrate that VaR models based on normal quantile underestimate risk while those based on student and cornish fisher quantiles seem to be more relevant measurements. GARCH-type VaRs are very sensitive to changes in the return process. Back-testing results show that the choice of the model used to forecast volatility has an importance. Indeed, the VaR based on standard deviation is not relevant to measure hedge funds risks as it fails the appropriate tests. On the opposite side, GARCH-, TGARCH- and EGARCH-type VaRs are accurate as they pass most of the time successfully the back-testing tests. But, the quantile used has a more significant impact on the relevance of the VaR models considered. GARCH-type VaR computed with the student and especially cornish fisher quantiles lead to better results, which is consistent with Monteiro (2004) and Pochon and Teïletche (2006).

Originality/Value of chapter – A large set of GARCH-type models are considered to estimate hedge funds volatility leading to numerous evaluation of VaRs. These estimations are very helpful. Indeed, public savings under institutional investors management then delegate to hedge funds are concerned. Therefore, an adequate risk management is required. Another contribution of this chapter is the use of daily data to measure all hedge fund strategies risks.

Aims to investigate the accuracy of parametric, nonparametric, and semiparametric methods in predicting the one‐day‐ahead value‐at‐risk (VaR) measure in three types of markets (stock exchanges, commodities, and exchange rates), both for long and short trading positions.

The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance, and power transformation of conditional variance.

Based on back‐testing measures and a loss function evaluation method, finds that the modeling of the main characteristics of asset returns produces the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions.

Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns.

The behavior of the risk management techniques is examined for both long and short VaR trading positions; to the best of one's knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, a two‐stage model selection is implemented in contrast with the most commonly used back‐testing procedures to identify a unique model. Finally, parametric, nonparametric, and semiparametric techniques are employed to investigate their performance in a unified environment.

The purpose of this paper is to use filtered extreme‐value theory (EVT) model to forecast one of the main emerging market stock returns and compare the predictive performance of this model with other conditional volatility models.

This paper employs eight filtered EVT models created with conditional quantile to estimate value‐at‐risk (VaR) for the Istanbul Stock Exchange. The performances of the filtered EVT models are compared to those of generalized autoregressive conditional heteroskedasticity (GARCH), GARCH with student‐t distribution, GARCH with skewed student‐t distribution, and FIGARCH by using alternative back‐testing algorithms, namely, Kupiec test, Christoffersen test, Lopez test, Diebold and Mariano test, root mean squared error (RMSE), and h‐step ahead forecasting RMSE.

The results indicate that filtered EVT performs better in terms of capturing fat‐tails in stock returns than parametric VaR models. An increase in the conditional quantile decreases h‐step ahead number of exceptions and this shows that filtered EVT with higher conditional quantile such as 40 days should be used for forward looking forecasting.

The research results show that emerging market stock return should be forecasted with filtered EVT and conditional quantile days lag length should also be estimated based on forecasting performance.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

To overcome the high manual effort of assessments for teaching personnel, e‐assessment systems are used to assess students using information systems (IS). The purpose of this paper is to propose an extension of EASy, a system for e‐assessment of exercises that require higher‐order cognitive skills. The latest module allows assessing programming exercises in conjunction with particular test‐driven‐development and back‐to‐back testing.

EASy was developed following a design science research approach. To prove the effectiveness of the approach, the authors discuss findings from a survey that was conducted with almost 200 students from a programming lecture and present quantitative and qualitative findings.

Most students reflected positively on using EASy. EASy proves to be a versatile tool and the extension meets the authors' aims. Several details require further investigation, most notably usability and the support of tutors.

E‐assessment is a field that requires much future research to enable commercial‐scale systems for assessment of higher‐order cognitive skills. The authors' research is currently limited in the number of exercise types the system supports.

EASy is a research tool despite being used in actual lectures. It is not yet a general e‐assessment solution.

While EASy is a research prototype, its usage in lectures demonstrates the practicability of using e‐assessment. EASy currently is one of the few systems with advanced capabilities. The paper strongly contributes to the knowledge base on building e‐assessment systems; thus, it is relevant both for practitioners seeking to establish e‐assessment and to researchers trying to understand the future needs towards comparable systems.

This paper aims to indirectly evaluate the accuracy of various volatility models using a value-at-risk (VaR) approach and to investigate the relationship between the accuracy of volatility modelling and investments performance in the financialized commodity markets.

This paper uses the VaR back-testing approach at six different commodities, seven different volatility models and five different time horizons.

This paper finds that the moving average (MA) VaR model tends to be the best for oil, copper, wheat and corn (long horizon) whereas the exponential generalized autoregressive conditional heteroscedastic (E-GARCH) VaR model tends to be the best for gold, silver and corn (short horizon). Our findings indicate that MA volatility model should be used for oil, copper, wheat and corn (for longer time horizons) commodities whereas E-GARCH volatility model should be used for gold, silver and corn (for short time horizons) commodities. We also find that there is a positive relationship between an accurate VaR performance and commodity return. This indicates that a good job in modelling volatility will be rewarded by higher returns in financialized commodity markets.

This paper indirectly evaluates the accuracy of volatility model via VaR measure and investigates the relationship between the accuracy of volatility and investments performance in financialized commodity markets. This paper contributes to the literature by offering VaR approach in evaluating volatility model performance and reporting the importance of performing accurate volatility modelling in financialized commodity markets.