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Motivated by the growing importance of the expected shortfall in banking and finance, this study aims to compare the performance of popular non-parametric estimators of the expected shortfall (i.e. different variants of historical, outlier-adjusted and kernel methods) to each other, selected parametric benchmarks and estimates based on the idea of forecast combination.

Within a multidimensional simulation setup (spanned by different distributional settings, sample sizes and confidence levels), the authors rank the estimators based on classic error measures, as well as an innovative performance profile technique, which the authors adapt from the mathematical programming literature.

The rich set of results supports academics and practitioners in the search for an answer to the question of which estimators are preferable under which circumstances. This is because no estimator or combination of estimators ranks first in all considered settings.

To the best of their knowledge, the authors are the first to provide a structured simulation-based comparison of non-parametric expected shortfall estimators, study the effects of estimator averaging and apply the mentioned profiling technique in risk management.

Motivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.

The authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).

The authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.

The study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.

In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue.

In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems.

This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study.

With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign institutional investors (FII). These subcategories include resource use, emission reduction and innovation under the environmental pillar, workforce, human rights, community and product responsibility under the social pillar and management, shareholders and CSR strategy under the governance pillar.

A machine learning technique known as “topic modeling” is used to analyse the current literature on ESG. To investigate the correlation between ESG scores and their subcategories with the investment decisions made by FII and to address concerns regarding multicollinearity and overfitting, a penalty-based regression model is employed.

The findings indicate that FIIs invest in firms with higher emission reduction and innovation scores under the environmental indicator. Additionally, firms with high human rights, community and product responsibility scores under the social indicator category have a positive relationship with FII investors. All subcategories of governance indicators, such as corporate social responsibility (CSR), strategy, shareholders and management scores, also positively impact FII investment. Of the three indicators, i.e. ESG, non-promoter FIIs give maximum weightage to governance indicators.

Since ESG is a contemporary topic, the findings on the relationship between different categories of ESG on FII investment will support managers in their FII investment. Also, the study will help the government frame policy decisions on ESG.

Previous studies have explored the impact of the overall ESG indicators on FII investments, but they have not specifically studied the influence of sub-indicators within these categories on investment decisions. By addressing this gap, the study enhances stakeholder theory by identifying and prioritizing the various subcategories of ESG indicators that impact FII investment decisions.

This chapter proposes that efforts to improve our understanding of factors affecting migrant health and longevity in the United States must consider migrants’ labor market incorporation and the structural conditions under which they work. I use public-use death certificate data to examine whether there is a mortality penalty for foreign-born workers in the secondary sector industries of agriculture and construction. I focus on the decade of the 1990s for two contextual and empirical reasons: (1) the decade was characterized by economic restructuring, restrictive immigration policy, increased migration, and dispersion of migrants to new geographic destinations; and (2) the 1990s is an opportunistic decade because 19 states coded the industry and occupation of the decedent during this time. These numerator mortality data and Census denominator data are used to compare all-cause mortality rates between working-age (16–64 years) US-born and foreign-born agricultural and construction workers, the overall foreign-born population, and foreign-born workers in health care – an industry where the foreign-born tend to work in well-paid occupations that are well-regulated by the state. The results show a clear mortality penalty for foreign-born workers in agriculture and construction compared to the overall foreign-born population and foreign-born healthcare workers. The results also show the mortality penalty for foreign-born secondary sector workers varies by industry. These findings support the argument that bringing work into our analyses is critical to understanding the contextual and structural factors affecting migrant health and survival.

The purpose of this paper is to examine the impacts of research and development (R&D) investment and environmental, social and governance (ESG) performance on green innovation performance. This paper also investigates the moderating effect of ESG performance between R&D investment and green innovation performance.

The study uses the data of 223 Chinese listed companies over the period 2015–2018. The ESG indices issued by SynTao Green Finance are used to measure ESG performance. Green innovation performance is measured by the total number of green patents, the number of green invention patents and the number of green non-invention patents. Finally, multiple regression analysis is applied to test the research hypotheses.

The results show that R&D investment has a positive impact on green innovation performance and ESG performance can increase the number of green invention patents. In addition, ESG performance moderates the relationship between R&D investment and green innovation performance.

The findings may help managers and policymakers in developing countries to make ecological innovation strategies to achieve corporate sustainability.

This is the first study to examine the impacts of R&D investment and ESG performance on green innovation performance in the context of China, an emerging market.

National differences in the demand for voluntary external audits have been linked to multiple factors, such as differences in a country's rate of growth, access to external credit, and institutional quality. Audits, however, also have a psychological cost, whose intensity is genetically and culturally hereditary. Using a sample of 3,072 private firms across 34 industries in seven countries, including five countries or regions from the former Soviet Comecon, we find that a country's share of firms choosing to undergo external audits is negatively related to the prevalence of carriers of the G allele in the mu-opioid receptor gene's A118G polymorphism, also known as the “social sensitivity” gene. Furthermore, the relationship between the prevalence of the social sensitivity gene and audits is fully mediated by a national culture's degree of collectivism. The results are statistically and economically highly significant and remain robust to the introduction of a set of confounding factors at the firm and country levels. Our results have practical relevance in recognizing psychological diversity when conducting audits and, more generally, preventing burnout in the workplace.