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This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

The purpose of this paper is to investigate the possibility of monitoring stress on stock markets from the perspective of a central counterparty (CCP). Due to their balanced positions, CCPs are exposed to extreme price movements in both directions; thus, the major risk for them derives from extreme returns and market illiquidity. The authors examined the connection of the stress alarms of return- and liquidity-based measures to find an objective basis for stress measurement.

The authors defined two types of stress measures: indicators based on extreme returns and liquidity. It is suggested that the stress indicators should be based on the existing risk management methodology that examines different risk measure oversteps. The stress signals of the past nine years on the German stock market were analyzed. The authors investigated the connection between the chosen stress measures to obtain a robust measure for alarming stress.

Although extreme returns and illiquidity are both characteristics of stress, the correlation of returns- and liquidity-based stress indicators is low when taking daily values. On the other hand, the moving averages of the indicators correlate significantly in the case of measures of downward and upward extreme returns and liquidity measured by the relative spread. The results are robust enough to be used for monitoring stress periods.

This paper contributes to understanding the characteristics of stress periods and points to the fact that stress signals measured by different aspects can also differ within the same asset class. The moving averages of returns- and relative spread-based indicators, however, could provide a cost-effective quantitative support for the risk management of a CCP and make the margin calculation predictable for clearing members as well.

The cross-quantilogram analysis is employed. The latter can assess the temporal association between two stationary time series at different parts of their joint distribution. Data are daily prices and trading volumes from the futures markets of five agricultural commodities, namely, corn, hard red wheat, oats, rice and soybeans.

The objective to the present work is to investigate for directional predictability between returns and volume (and vice versa) in the futures markets of agricultural commodities.

The empirical results reveal evidence, weak as well as strong, that extreme low values of returns are likely to lead high levels of volume. There is also weak evidence that extreme low values of volume are likely to precede high values of returns, except for the futures markets of oats where there is very strong evidence that low values of volume are likely to lead high values of returns. For the commodity of soybeans, there is very strong evidence that extreme high levels of volume are likely to lead high values of returns, but they are very short lived.

Agricultural futures have been recently characterized by increased volatility leading hedgers to be looking for diversification. The present findings suggest that when price crashes occur, investors who suffer losses wish to sell, increasing this way the trading activity. Concurrently, the results reveal that extreme low levels of trading volume might signal a possible price turn around for traders.

This is the first study that employs the quantilogram approach in order to investigate for potential predictability from returns to volume and from volume to returns, in the futures markets of agricultural commodities.

In this paper, we show that there is a negative premium for MAX stocks in the Korean stock market. However, there is no evidence that the MAX effect overwhelms the effects of idiosyncratic risk. When we control for idiosyncratic risk, the negative relationship between extreme returns and future returns is less robust. Rather, the cross-effect of the extreme returns and the idiosyncratic risk factors explains the negative premium. Furthermore, our results are not fully explained by the exposure to the market timing and economic state. Overall, both the extreme return and idiosyncratic risk effects appear to coexist in the Korean stock market, but they are not independently.

Previous research suggests that monthly commodity futures returns are like equity returns and recommend long-only portfolio positions. A follow-up question is whether the distributions of daily returns on commodity futures are fat-tailed, just like equity returns. This question has important implication for commodity futures traders because futures trade positions are marked to the market daily. The Extreme Value Theory (EVT) is used to test whether the distributions of the commodity futures returns are fat-tailed with finite variance. The results suggest that not all commodity futures returns have a fat-tail distribution and the tails of the distributions of commodity futures returns generally are smaller than the tails of the distribution of equity returns.

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 apply safety‐first portfolio principles in an environment where financial risk exists because of the probability of terrorist attacks, where the catastrophic events of September 11, 2001 (911) are the focal point of the analysis.

Safety‐first portfolios of US equities bilaterally combined with 12 developed and emerging region global equity indices are obtained for 911. Extreme value theory and safety‐first principles are used to optimize these portfolios for US risk‐averse investors. The actual performances of all portfolios in the post‐911 period are compared to the optimal results. The robustness of the results is examined by replicating the analysis for the period following July 7, 2006, when no actual terrorist attacks occurred on US soil.

Optimal ex ante (ex post) safety‐first portfolios on 911 have high (low) US weights, and on July 7, 2006 low US weights. The differences are attributed to changes in market projections and/or conditions. In all cases, wealth is preserved even without the ex post optimal portfolios.

Safety‐first portfolio optimization can protect wealth given financial risks of extreme events like terrorist attacks.

The paper shows that quantitative assessments of financial risk are feasible, even though uncertainty with experts' risk assessments of extreme events such as 911 exists because of limited historical data and low probability of occurrence. The results are useful to investors developing international diversification strategies to protect wealth given the risks of terrorist attacks.

This study aims to examine investors’ herd behaviour for various calendar events and size-based stock portfolios in Pakistan. The authors consider three calendar effects, crisis (COVID-19 and financial crisis 2018–19), announcement of political news and popular calendar anomalies (month-of-the-year and day-of-the-week), and investigate the impact of stock size on calendar effect in terms of investors’ herd behaviour.

The study uses non-linear specification to capture herd behaviour using firm-level daily data for 496 stocks listed on Pakistan Stock Exchange over the period 2001–2020.

The results indicate herd formation during periods of COVID-19, financial crisis, political news announcements and January (month-of-the-year). The authors also observe significant herding for the biggest and smallest size stocks over complete period. However, the authors find more pronounced herding in big stocks during January as compared to the more noticeable herding in small stocks over complete period. The findings suggest that herding in small stocks is not the main cause of January herding and hint on the prevalence of significant institutional herding during January.

The stock prices destabilize because of the mimicking behaviour during crisis periods, days of political announcements and month of January. Implementation of insider trading laws and transparent information environment can help in reducing these effects and increasing market efficiency.

The authors consider the recent COVID period in our analysis. In addition, we provide new evidence on the possible impact of stock size on calendar effect in terms of herd behaviour, which, to the best of the authors’ knowledge, has not yet been documented in literature.

The purpose of this paper is to examine positive‐feedback (PF) behavior and its relationship to momentum profitability and information uncertainty.

Using the behavioral function of rational traders and feedback traders, the authors jointly estimate the mean and conditional variance equations of the GARCH model to derive the positive‐ and non‐positive‐feedback coefficients, respectively. In each six‐month period, the number of PF stocks were then calculated as a fraction of the total number of stocks in that period. The authors then investigate whether day‐to‐day PF trading activities vary across different momentum portfolios by calculating the percentage of PF stocks in each decile portfolio.

This study finds that about 9.4 per cent of stocks exhibit PF trading activities and that these activities have a more profound effect on stocks with a higher level of information uncertainty. The finding shows that the percentage of stocks with PF trading is higher in the portfolios of extreme losers than in the portfolios of extreme winners. The evidence suggests that stocks exhibiting PF trading activities subsequently experience significantly higher momentum returns.

This paper presents evidence to test whether a relationship exists between short‐term PF trading and future momentum profitability. Since PF traders tend to chase price movements, PF trading is more likely to cause stock prices to further diverge from the firm's fundamentals and, therefore, give rise to stock return momentum. This phenomenon appears to be more profound in this study when there is a higher level of information uncertainty.

Herd behavior has been studied herein and tested based on primary respondents from Indian markets.

The paper expounds the empirical evidence by applying the cross-sectional absolute deviation method and reporting on herd behavior among decision-makers who are engaged in trading in the Indian stock market. Further, the study attempts to analyze the market-wide herding in the Indian stock market using 2230 daily, 470 weekly and 108 monthly observations of Nifty 50 stock returns for a period of nine years from April 1, 2009 to March 31, 2018 during the normal market conditions, extreme market conditions and in both increasing and decreasing market conditions.

In a span of a decade witnessing different market cycles, the authors’ results exhibit that there is no evidence of herding in any market condition in Indian stock market primarily due to the dominance of institutional investors and secondly because of low market participation by individual investors.

The results reveal that there is no impact of herd behavior on the stock returns in the Indian equity market during the normal market conditions. It highlights that the participation of individuals who are more prone to herding is more evident for short-run investments, contrary to long-term holdings.