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Many large companies in Europe include mainly men in supervisory boards and the women quota is often lower than 20%. In Germany an optional women quota of 30% in supervisory boards was proposed for capital-market-oriented companies in 2016. Some assume that without a gender quota the earnings of enterprises would shrink as male and female members in supervisory teams do not work in such a harmonized and structured way. Others think that a women quota in supervisory boards should be requested by law and should not remain optional. In this context, conducting research and analyzing the impact of the women’s presence in supervisory boards on the success of companies appear as a necessary topic. The present chapter looks at the companies of EURO STOXX 50 in the year 2015 and their success and tries to establish whether this success can be related to the percentage of female members in supervisory positions. It replicates in this way the study of Binder, Alonso-Almeida, and Bremser (2016) which analyzed the relationship between female’s representation in the management board (executive board) and firm performance (measured by earnings before taxes – EBT) of the EURO STOXX 50 companies in 2014. It is in the same time an extension of the original study as the supervisory board is brought under scrutiny.

Purpose – The purpose of this chapter is to investigate the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements.

Methodology/Approach – We use a biorhythm approach characterized by the seasonal affective disorder (SAD) variable to study the impact of emotion on energy markets. Normal times and periods of extreme price movements are approximated by OLS and quantile estimations, respectively.

Findings – We use European energy forward prices of oil, gas, coal, and electricity. European equity future index (Dow Jones Euro Stoxx 50) and euro/dollar US exchange rate are used as control variables for economic and financial environment. Estimating OLS and quantile regressions, we find that seasonal patterns have a significant impact during extreme volatility periods only. Further investigations reveal that the SAD effect is significant during periods of price decrease, but insignificant during price increase times. The out-of-sample predictive ability properties show that our “SAD model” outperforms significantly the pure “macroeconomic” one.

Originality/Value of chapter – This topic is novel in energy finance since I use psychological background theory to understand energy price dynamics. I illustrate the relevance of our approach by comparing the out-of-sample predictive ability of our model against macroeconomic one. My results could be considered to improve energy porfolio allocation.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

Financial derivatives markets are a relatively new development globally. In the USA, the first commodity derivatives trading began in Chicago at the Chicago Board of Trade in 1849. However, the first financial derivatives trading did not begin until 1972, when the Chicago Mercantile Exchange began trading futures contracts on seven foreign currencies. These were the world's first official financial futures contracts. In Europe, the oldest financial derivatives market was the London International Financial Futures Exchange, or LIFFE, which began trading financial futures in 1982.

The world has been witnessing a new dreadful disease since the latter part of 2019. The disease known as the novel coronavirus disease often referred to as COVID-19 originated from the Chinese city of Wuhan in the Hubei province and has since spread across the world resulting in the World Health Organisation to declare it a global pandemic. Whilst it appears obvious that the pandemic continues to generate several impacts, knowledge of the true idea, nature and extent of the impacts is scanty, partly because the disease is novel, ongoing and an antidote is yet to be found for it as well as the fact that no or little systematic studies have been conducted into the impacts and the results codified. This study, therefore, explores the global overview of the environmental and socioeconomic impacts of COVID-19 based on evidence in the literature. The chapter found that several measures such as imposition of partial or total lockdowns, social distancing and stay-at-home policies, wearing of face masks and the use of gloves and hand sanitizers have been instituted to contain the pandemic since its outbreak. Apart from 4,766,468 infections with 318,201 deaths, which had occurred as at 19 May 2020 and are still counting, the measures instituted have resulted in increase in domestic energy consumption, generation of waste pollution, contraction in production, loss of income and jobs, disruption in market activities and depreciation in asset prices and values across the various sectors of the world's economy, increases in domestic violence and limited access to health services among others. Conversely, the pandemic has partly resulted in positive outcomes such as reduction in greenhouse gas emissions and boost in the operations of the ITC and other allied industries. However, in broad terms, it is a huge threat to sustainable development (SD) and gains made in that regard over the years are eroding. Furthermore, although there have been interventions from governments, United Nations and other international development organisations to ease the adverse impacts, more such interventions and efforts will be required to put the SD agenda on track.

Exchange traded funds (ETFs) are one of the most innovative financial products listed on exchanges. As reflected by the size of the market, they have become popular among both retail and institutional investors. The original ETFs were simple and easy to understand; however, recent products, such as leveraged, inverse, and synthetic ETFs, are more complex and have additional dimensions of risk. The additional risks, complexity, and reduced transparency have resulted in heightened attention by regulators. This chapter aims to increase understanding of how ETFs function in the market and can potentially impact financial stability and market volatility.

We discuss the evolution of ETFs, growing regulatory concerns, and the various responses to these concerns.

We find that concerns related to systemic risk and excess volatility, suitability for retail investors, lack of transparency and liquidity, securities lending and counterparty exposure are being addressed by both market participants and policy makers. There has been a shift toward multiple counterparties, overcollateralization, disclosure of collateral holdings and index holdings.

The analysis contained in this chapter provides an understanding of the role of ETFs in the financial markets and the global economy that should be valuable to market participants, investors, and policy makers.

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.