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Extreme metal originated in North America and Europe in the late-1980s, but Australia’s adoption of the form followed closely. One of the first Australian extreme metal (AEM) acts was Sadistik Exekution from Sydney, formed in 1985. Sadistik Exekution are notable for combining musical intensity with irreverent humour and parody. They introduced global extreme metal to the trope of the Australian larrikin: a once pejorative characterisation that has become a term of endearment in contemporary Australian culture, describing a defiant and jocular personality. This trope is evident in Sadistik Exekution’s work, but it has since proliferated more broadly throughout AEM, exemplified by more recent bands like Blood Duster and King Parrot. Their music, too, is inarguably intense and provocative, but is simultaneously mocking of the solemnity of its scene and lineage. This chapter will examine how bands like Sadistik Exekution, Blood Duster and King Parrot, through their public personas and musical and paramusical texts, have subverted extreme metal coding, thereby uncovering a uniquely Australian trajectory in extreme metal style and history.

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.

This paper deals with poverty decompositions into subgroups defined with respect to intervals of income and the robustness of comparisons of the absolute contribution of such groups to poverty. For instance, world poverty estimates by the World Bank often distinguish between the extreme poor whose incomes are lower than $1.25 a day (in PPP terms) and the other poor with incomes between $1.25 and $2.5 a day. Existing dominance conditions can tell whether overall poverty and extreme poverty have declined in a robust manner when comparing countries at two points of time, but they cannot say anything for the contribution of the non-extreme poor to overall poverty. In the present paper we propose stochastic generalized dominance criteria to perform robust poverty ordering when the focus is placed on some interval of the poverty domain. Using generated data based on grouped data from World Bank’s PovcalNet tool, the paper finally investigates whether the robust decline of extreme poverty around the world during the last decades was also accompanied by a decline of the contribution of non-extreme poverty.

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.

The presence of respondents with apparently extreme sensitivities in choice data may have an important influence on model results, yet their role is rarely assessed or even explored. Irrespective of whether such outliers are due to genuine preference expressions, their presence suggests that specifications relying on preference heterogeneity may be more appropriate. In this paper, we compare the potential of discrete and continuous mixture distributions in identifying and accommodating extreme coefficient values. To test our methodology, we use five stated preference datasets (four simulated and one real). The real data were collected to estimate the existence value of rare and endangered fish species in Ireland.

This chapter takes a closer look at outliers and extreme outliers identified in the data derived from a complete case treatment of missing values in the European and North American datasets and consistently observe significant negatively skewed distributions with high excess kurtosis across all industries. We then plot the density functions for return on assets (ROA) across different industries in the two datasets and find pervasive observations in the tails where negative returns and outlying observations constitute a frequent and recurring phenomenon. We analyze the persistency of outliers and find noticeable percentages of outlying over- and underperformers hovering around 3–6% dependent on industry context. We further analyze potential size effects associated with extreme negative skewness but do not find that (even sizeable) elimination of extreme values reduce the phenomenon. Finally, we analyze the percentage of firm observations that must be eliminated to reach at distributions that fulfill the characteristics of a normal distribution and reach at a substantial percentage of around 5–10% dependent on industry. To conclude, the often-assumed normally distributed performance outcomes are typically wrong and discards the substantial number of outliers in the samples.

This chapter first analyzes how the data-cleaning process affects the share of missing values in the extracted European and North American datasets. It then moves on to examine how three different approaches to treat the issue of missing values, Complete Case, Multiple Imputation Chained Equations (MICE), and K-Nearest Neighbor (KNN) imputations affect the number of firms and their average lifespan in the datasets compared to the original sample and assessed across different SIC industry divisions. This is extended to consider implied effects on the distribution of a key performance indicator, return on assets (ROA), calculating skewness and kurtosis measures for each of the treatment methods and across industry contexts. This consistently shows highly negatively skewed distributions with high positive excess kurtosis across all the industries where the KNN imputation treatment creates results with distribution characteristics that are closest to the original untreated data. We further analyze the persistency of the (extreme) left-skewed tails measured in terms of the share of outliers and extreme outliers, which shows consistent and rather high percentages of outliers around 15% of the full sample and extreme outliers around 7.5% indicating pervasive skewness in the data. Of the three alternative approaches to deal with missing values, the KNN imputation treatment is found to be the method that generates final datasets that most closely resemble the original data even though the Complete Case approach remains the norm in mainstream studies. One consequence of this is that most empirical studies are likely to underestimate the prevalence of extreme negative performance outcomes.

This contribution is an investigation into the palpable connection of extreme metal music and concepts of death, dying and mortality. Like other dark subcultures, metal has an intense infatuation with the macabre; many of its subcultures seek to uncover meaning through musical exploration into varying dark themes that emerge when thinking about death and what lies beyond the bounds of existence. From the abrasive and animalistic blood lust of death metal to the melancholic textures of black metal and the sonic void evoked in doom metal, extreme metal is a catalyst through which fans of the macabre can explore many perceptions and conceptions of corporeal fragility; the consuming pain of life, of death, and of knowing; and the existential notion of the ungraspable abyss. This chapter explores these varying conceptualisations of death in extreme metal culture, their sonic representations and their cathartic consolation: delving into the psychoanalytic reasoning and embodied sound of death.

Global temperature has risen by 1°C since 1900, while since the 1990s the Arctic has recently experienced an accelerated warming of about double the average rate of global warming. Nearly all climate scientists agree that the main cause of this temperature rise is ever-increasing accumulations of ‘greenhouse gases’, especially carbon dioxide and methane, within our atmosphere. Sea level rise could easily exceed one metre this century under ‘business as usual’. However, global warming is not just about rising temperatures, melting ice and rising sea levels, but it also affects the frequency and severity of many extreme weather events. Planetary warming is not a uniform process, can spring surprises in regional climate change and is probably linked with the tendency for Northern Hemisphere mid-latitudes to have more extreme (variously hot/cold/dry/wet) weather, especially during the recent period of rapid Arctic warming. There is overwhelming scientific evidence that human activity through enhanced greenhouse gas emissions is largely responsible for recent climate change and accompanying extreme weather, and we are already clearly seeing these changes. However, it is equally evident that, although initial remedial steps are being taken, finding an adequate solution will not be easy unless much larger changes are made to the way in which we all live. Limiting global warming to 1.5°C above pre-industrial temperatures would require global carbon dioxide emissions to decrease by approximately 40–60% by 2030 relative to 2010 levels. This can only be achieved through a collective solution that fully involves diverse communities, among them religious stakeholders.