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It is widely accepted that equity return volatility increases more following negative shocks rather than positive shocks. However, much of value‐at‐risk (VaR) analysis relies on the assumption that returns are normally distributed (a symmetric distribution). This article considers the effect of asymmetries on the evaluation and accuracy of VaR by comparing estimates based on various models.

The aim of this paper is to fill a gap in the foreign‐exchange trading risk‐management literature and particularly from the perspective of emerging and illiquid markets, such as in the context of the Moroccan foreign‐exchange market.

This paper, demonstrates a constructive approach, for the management of trading risk exposure of foreign‐exchange securities, which takes into account proper adjustments for the illiquidity of both long and short trading positions. The approach is based on the renowned concept of value at risk (VaR) along with the innovation of a software tool utilizing matrix‐algebra and other optimization techniques.

Several case studies, on the Moroccan Dirham, were achieved with the objective of setting‐up a practical framework of trading risk measurement, management and control reports, in addition to the inception of a practical procedure for the calculation of optimum VaR limits structure.

In this work, the risk‐management procedures that are discussed will aid financial markets' participants, regulators and policymakers, operating within emerging economies, in founding sound and proactive policies to handle foreign‐exchange trading risk exposures. The document includes comprehensive theory, analyses sections, conclusions and recommendations, and full real‐world foreign‐exchange trading risk‐management reports.

Although a substantial literature has examined the statistical and economic meaning of VaR models, this article provides real‐world techniques and optimum asset allocation strategies that are useful for trading portfolios in emerging and illiquid financial markets. This is with the objective of setting‐up the basis of a proactive methodology/procedure for the measurement, management and control of foreign‐exchange exposures in the day‐to‐day trading operations.

The main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.

The authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.

The wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.

This study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.

The study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.

To the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.

The purpose of this article is to introduce a new method of estimating risk as an alternative to value at risk (VaR), drawing on the risk assessment literature in environmental science.

A commonly used and accepted measure of market risk is VaR, defined as the difference between initial portfolio value and a probabilistic lower bound B on the portfolio value at time T. To take account of situations where the portfolio value may fall below B prior to time T, an an alternative to VaR is proposed based on first passage time distributions.

It is argued that the resulting expected minimum portfolio value over the time frame T provides a clear alternative measure of market risk. Analytical expressions are obtained and numerical comparisons given when the distribution of portfolio returns is lognormal.

Analytical results are presented for lognormal distributions for returns. Results for other models can be easily obtained from simulation.

The new measure of risk recognizes that investors might be sensitive to risks of decline in the value of a portfolio at any time within a given time horizon, not just at the end of the anticipated period of investment. The expected minimum portfolio value measures the largest loss that is expected at some stage over that period.

A new measure of risk is presented that arises from literature on risk assessment in environmental science. It is complementary to VaR for assessing risk.

Recent literature discusses the persistence of skewness and tail risk in hedge fund returns. The aim of this paper is to suggest an alternative skewness measure, Azzalini's skewness parameter delta, which is derived as the normalized shape parameter from the skew‐normal distribution. The paper seeks to analyze the characteristics of this skewness measure compared with other indicators of skewness and to employ it in some typical risk and performance measurements.

The paper first provides an overview of the skew‐normal distribution and its mathematical formulation. Then it presents some empirical estimations of the skew‐normal distribution for hedge fund returns and discusses the characteristics of using delta with respect to classical skewness coefficients. Finally, it illustrates how delta can be used in risk management and in a performance measurement context.

The results highlight the advantages of Azzalini's skewness parameter delta, especially with regard to its interpretation. Delta has a limpid financial interpretation as a skewness shock on normally distributed returns. The paper also derives some important characteristics of delta, including that it is more stable than other measures of skewness and inversely related to popular risk measures such as the value‐at‐risk (VaR) and the conditional value‐at‐risk (CVaR).

The contribution of the paper is to apply the skew‐normal distribution to a large sample of hedge fund returns. It also illustrates that using Azzalini's skewness parameter delta as a skewness measure has some advantages over classical skewness coefficients. The use of the skew‐normal and related distributions is a relatively new, but growing, field in finance and not much has been published on the topic. Skewness itself, however, has been the subject of a great deal of research. Therefore, the results contribute to three fields of research: skewed distributions, risk measurement, and hedge fund performance.

It is the purpose of this article to empirically test the risk parameters for larger foreign‐exchange portfolios and to suggest real‐world policies and procedures for the management of market risk with the aid of value at risk (VaR) methodology. The aim of this article is to fill a void in the foreign‐exchange risk management literature and particularly for large portfolios that consist of long and short positions of multi‐currencies of numerous developed and emerging economies.

In this article, a constructive approach for the management of risk exposure of foreign‐exchange securities is demonstrated, which takes into account proper adjustments for the illiquidity of both long and short trading/investment positions. The approach is based on the renowned concept of VaR along with the innovation of a software tool utilizing matrix‐algebra and other optimization techniques. Real‐world examples and reports of foreign‐exchange risk management are presented for a sample of 40 distinctive countries.

A number of realistic case studies are achieved with the objective of setting‐up a practical framework for market risk measurement, management and control reports, in addition to the inception of a practical procedure for the calculation of optimum VaR limits structure. The attainment of the risk management techniques is assessed for both long and short proprietary trading and/or active investment positions.

The main contribution of this article is the introduction of a practical risk approach to managing foreign‐exchange exposure in large proprietary trading and active investment portfolios. Key foreign‐exchange risk management methods, rules and procedures that financial entities, regulators and policymakers should consider in setting‐up their foreign‐exchange risk management objectives are examined and adapted to the specific needs of a model of 40 distinctive economies.

Although a substantial literature has examined the statistical and economic meaning of VaR models, this article provides real‐world techniques and optimum asset allocation strategies for large foreign‐exchange portfolios in emerging and developed financial markets. This is with the objective of setting‐up the basis of a methodology/procedure for the measurement, management and control of foreign‐exchange exposures in the day‐to‐day trading and/or asset management operations.

This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied.

In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method.

It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms.

This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc.

This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy.

This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

The purpose of this paper is to provide empirical evidence that Sukuk are different from conventional bonds from risk perspective. This study is about the comparative risk analysis of Sukuk and conventional bonds in Pakistan.

Sample consists of 15 Sukuk and 30 Term Finance Certificates issued in Pakistan. Value at risk is deployed by using delta normal approach to calculate risk. Two portfolios are formed separately with equal investment of ₹3m to explore the maximum loss an investor would have in portfolio of Sukuk and conventional bonds separately.

Results revealed that Sukuk are less risky and more stable instrument as compared to conventional bonds. Risk and stability of Sukuk are explained with diversification theory and liquidity perspective. It is found that correlation among most of Sukuk securities are less or negative, which help in diversifying their risk. However, the attribute of stability can be due to the few days of trading in case of Sukuk comparatively.

Literature has explored the operational differences between conventional and Islamic bonds on theoretical basis. However, few studies explain their differences empirically especially with respect to risk in case of Pakistan where debt market is developing. Therefore, the originality of this research lies within its comparative investigation of risk for two securities that are different from their operational perspectives.

The Basel II Accord requires that banks and other authorized deposit‐taking institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure value‐at‐risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realized losses exceed the estimated VaR. The purpose of this paper is to address the question of risk management of risk, namely VaR of VIX futures prices.

The authors examine how different risk management strategies performed before, during and after the 2008‐2009 global financial crisis (GFC).

The authors find that an aggressive strategy of choosing the supremum of the univariate model forecasts is preferred to the other alternatives, and is robust during the GFC.

The paper examines how different risk management strategies performed before, during and after the 2008‐2009 GFC, and finds that an aggressive strategy of choosing the supremum of the univariate model forecasts is preferred to the other alternatives, and is robust during the GFC.

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.

The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.

In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.

The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.