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The purpose of this paper is to explore consumer patronage of extreme value retailers in the USA.

The paper employs a sample generated from Retail Forward panel data (n=1,107) to explore consumer patronage of extreme value retailers. Descriptive statistics and decision tree analysis chi‐square automatic interaction detector is used to evaluate the data.

The paper examines extreme value shoppers based on patronage frequency, expenditures, and primary reason for shopping in the extreme value format. Results suggest that although patronage frequency of extreme value stores is not increasing, expenditures for food/household essentials are increasing. Demographic characteristics (income and age) are revealed as predictors of the primary reason for patronizing the format.

Generalizations of the findings of this paper to markets outside the USA are limited due to the differences in consumers and retail formats available in various countries. Future research could investigate additional patronage motives of extreme value shoppers, predict propensity to shop in the format, and compare changes in the customer base over time as a response to changing economic conditions.

The findings of this paper provide extreme value retailers with information on patronage frequency, expenditure, and patronage motivations among extreme value shoppers. The results offer support for the development of competitive strategies within the extreme value segment. As competition in the retail industry continues to evolve and new retail formats emerge, understanding shoppers' reasons for patronizing extreme value retailers will be critical to performance.

This paper is unique because of the lack of attention to extreme value shoppers in the extant literature.

Extreme value model is one of the most important models that are applicable in air pollution data. This paper aims at introducing a new model of extreme value that is more suitable in environmental studies.

The parameters of the new model have been estimated by method of maximum likelihood. In order to relate to air pollution impacts, the new extreme value model was used, applied to carbon monoxide (CO) in parts per million (ppm) at several places in Malaysia. The objective of this analysis is to fit the extreme values with a new model and to examine its performance. Comparison of the new model with others is shown to illustrate the applicability of this new model.

The results show that the new model is the best fit using the method of maximum likelihood. The new model gives a significant impact of CO data, which gives the smallest standard error and p‐values. The new extreme value model is able to identify significantly problems of air pollution. The results presented by the new extreme value model can be used as an air quality management tool by providing the decision makers means to determine the required reduction of source.

The new extreme value model has mostly been applied in environmental studies for the statistical treatment of air pollution. The results of the numerical and simulated CO data indicate that the new model both is easy to use and can achieve even higher accuracy compared with other models.

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided.

Recent studies on the securities market’s differential pricing of earnings components have shown that cash flow from operations is more highly valued than total accruals and that moderate cash flow from operations has higher valuation than extreme total accruals. An interesting question that follows is whether these findings hold regarding the differential valuations of cash flow and current accruals. This study aims to extend prior research by addressing this issue in two ways. First, the authors examine the incremental information content of cash flow from operations beyond working capital from operations. Second, the authors assess the effect of extreme working capital from operations on the incremental information content of cash flow from operations. This study aims to extend prior research by addressing this issue in two ways.

This study adopts market-based accounting research to test its hypotheses and to achieve its objectives. Specifically, this study uses statistical associations between accounting data and stock returns to examine the incremental information content (value relevance) of cash flow and working capital from operations and the effect of extreme working capital from operations on the incremental information content of cash flow.

The results show that cash flow from operations is not more highly valued than current accruals (both being valued equivalently). However, moderate cash flow from operations has higher valuation than extreme current accruals (each is valued differently). Overall, these research findings indicate that cash flow becomes more important for valuation as accruals get “extreme”.

As accruals are unlikely to persist to be permanent across the years, these results can be interpreted as indicating that cash flow and accruals information are used jointly by investors, with one being more important than the other depending on the relative “extremeness” of each. Therefore, both are of value to the investor and both should be reported.

The paper contributes to the UK research on determining the preferred level of disaggregation of earnings components, i.e. operating cash flow, current accruals and non-current accruals. This would help investors to improve their investment and credit decisions.

In contrast to earlier studies, the most recent studies on the incremental value relevance of earnings and cash flows from operations find that both earnings and cash flows have incremental value relevance beyond each other. An interesting question that follows is whether these findings hold after controlling the extremity of earnings and cash flows. This study, therefore, aims to examine the incremental value relevance of earnings and cash flows in the following four cases: moderate earnings and moderate cash flows, moderate earnings and extreme cash flows, extreme earnings and moderate cash flows and extreme earnings and extreme cash flows.

To evaluate the incremental value relevance (information content) of earnings and cash flows for each of the four cases mentioned above, we examine the statistical significance of the slope coefficients for regression of returns on both unexpected earnings and unexpected cash flows from operations.

The results show that (i) both moderate and extreme earnings have incremental value relevance beyond both moderate and extreme cash flows, (ii) moderate cash flows have incremental value relevance beyond both moderate and extreme earnings and (iii) extreme cash flows lack incremental value relevance beyond moderate earnings; however, they (extreme cash flows) have incremental value relevance beyond extreme earnings. These results suggest that earnings and cash flows have incremental value relevance. However, only in cases when cash flows are extreme and earnings are moderate, cash flows do not possess incremental value relevance. In further analysis, we find that the value relevance for cash flows and earnings decreases when they are extreme and transitory. Moreover, the value relevance for cash flows increases when they are moderate (not extreme) and the other competing measure (earnings) is transitory and extreme.

The results support the idea that earnings and cash flows from operations complement each other in explaining variation in returns. However, when cash flows are extreme and less informative, investors rely more on earnings in firm valuation, especially when earnings are moderate. Because earnings are unlikely to persist to be permanent across the years, these results can be interpreted as indicating that cash flows and earnings information are used jointly by investors.

In contrast to previous studies, we control for the extremity of earnings and cash flows when evaluating the incremental value relevance of earnings and cash flows from operations.

Modern property investment allocation techniques are typically based on recognised measures of return and risk. Whilst these models work well in theory under stable conditions, they can fail when stable assumptions cease to hold and extreme volatility occurs. This is evident in commercial property markets which can experience extended stable periods followed by large concentrated negative price fluctuations as a result of major unpredictable events. This extreme volatility may not be fully reflected in traditional risk calculations and can lead to ruin. The paper aims to discuss these issues.

This research studies 28 years of quarterly Australian direct commercial property market performance data for normal distribution features and signs of extreme downside risk. For the extreme values, Power Law distribution models were examined as to provide a better probability measure of large negative price fluctuations.

The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent, being by at least 30 per cent for the outermost data point. For the statistical outliers beyond 2 SD, a Power Law distribution can overcome many of the shortcomings of the standard deviation approach and therefore better measure the probability of ruin, being extreme downside risk.

In highlighting the challenges to measuring property market performance, analysis of extreme downside risk should be separated from traditional standard deviation risk calculations. In recognising these two different types of risk, extreme downside risk has a magnified domino effect with the tendency of bad news to come in crowds. Big price changes can lead to market crashes and financial ruin which is well beyond the standard deviation risk measure. This needs to be recognised and developed as there is evidence that extreme downside risk determinants are increasing by magnitude, frequency and impact.

Analysis of extreme downside risk should form a key part of the property decision process and be included in the property investment manager’s toolkit. Modelling techniques for estimating measures of tail risk provide challenges and have shown to be beyond traditional risk management practices, being too narrow and constraining a definition. Measuring extreme risk and the likelihood of ruin is the first step in analysing and dealing with risk in both an asset class and portfolio context.

Research on price extremes and overreactions as potential violations of market efficiency has a long tradition in investment literature. Arguably, very few studies to date have addressed this issue in cryptocurrencies trading. The purpose of this paper is to consider the extreme value modelling for forecasting COVID-19 effects on cryptocoin markets. Additionally, this paper examines the importance of technical trading indicators in predicting the extreme price behaviour of cryptocurrencies.

This paper decomposes the daily-time series returns of four cryptocurrency returns into potential maximum gains (PMGs) and potential maximum losses (PMLs) at first and then tests their lead–lag relations under an econometric framework. This paper also investigates the non-random properties of cryptocoins by computing the incremental explanatory power of PML–PMG modelling with technical trading indicators controlled. Besides, this paper executes an event study to identify significant changes caused by COVID-19-related events, which is capable of analysing the cryptocoin market overreactions.

The findings of this paper produce the evidence of both market overreactions and trend persistence in the potential gains and losses from coins trading. Extreme price behaviour explains volatility and price trends in crypto markets before and after the outbreak of a pandemic that substantiate the non-random walk behaviour of crypto returns. The presence of technical trading indicators as control variables in the extreme value regressions significantly improves the predictive power of models. COVID-19 crisis affects the market efficiency of cryptocurrencies that improves the usefulness of extreme value predictions with technical analysis.

This paper strongly supports for the robustness of technical trading strategies in cryptocurrency markets. However, the “beast is moving quick” and uncertainty as to the new normalcy about the post-COVID-19 world puts constraint on making best predictions.

The paper contributes substantially to our understanding of the pricing efficiency of cryptocurrency markets after the COVID-19 outbreak. The findings of continuing return predictability and price volatility during COVID-19 show that profitable investment opportunities for cryptocoin traders are prevailing in pandemic times.

The paper is unique to understand extreme return reversals behaviour of cryptocurrency markets regarding events related to COVID-19 breakout.

From a regulatory point of view, as explained by Dimson and Marsh [1994, 1995], the amount of capital required by a financial institution to ensure an acceptably small probability of failure should depend on the risk associated with the assets detained in its portfolio. Dimson and Marsh [1994] conduct an empirical study on long and short equity trading books of securities firms acting as market makers. They consider different existing regulations: the comprehensive approach, as applied in the United States by the Securities and Exchange Commission; the building‐block approach, as proposed by the Basle Committee on Banking Supervision, and incorporated in the European Community [1992] Capital Adequacy Directive (CAD); and the portfolio approach, which in the U.K. forms part of the rules of the Securities and Futures Authority [1992]. All three methods are compared via the position risk requirement (PRR) that determines the amount of capital that financial institutions have to put aside. As shown by the authors in their empirical study, the methods proposed by the international regulators are barely related to the risk of the portfolios! Only for the national U.K. rules, the PRR and the risk of a portfolio show positive correlation.

This study examines problems that may occur when conventional Value‐at‐Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as the variance‐covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long‐term VaR forecasts are desired. Extreme Value Theory (EVT) is proposed to overcome these problems. The application of EVT is illustrated by an example from the German hog market. Multi‐period VaR forecasts derived by EVT are found to deviate considerably from standard forecasts. We conclude that EVT is a useful complement to traditional VaR methods.

Extreme high temperatures are a significant feature of global climate change and have become more frequent and intense in recent years. These pose a significant threat to both human health and economic activity, and thus are receiving increasing research attention. Understanding the hazards posed by extreme high temperatures are important for selecting intervention measures targeted at reducing socioeconomic and environmental damage.

In this study, detrended fluctuation analysis is used to identify extreme high-temperature events, based on homogenized daily minimum and maximum temperatures from nine meteorological stations in a major grassland region, Hulunbuir, China, over the past 56 years.

Compared with the commonly used functions, Weibull distribution has been selected to simulate extreme high-temperature scenarios. It has been found that there was an increasing trend of extreme high temperature, and in addition, the probability of its indices increased significantly, with regional differences. The extreme high temperatures in four return periods exhibited an extreme low hazard in the central region of Hulunbuir, and increased from the center to the periphery. With the increased length of the return period, the area of high hazard and extreme high hazard increased. Topography and anomalous atmospheric circulation patterns may be the main factors influencing the occurrence of extreme high temperatures.

These results may contribute to a better insight in the hazard of extreme high temperatures, and facilitate the development of appropriate adaptation and mitigation strategies to cope with the adverse effects.