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The aim of the paper is to explore the association between environmental performance and income level in the world economy in 2016. Data from Yale University and World Bank are used in a cross-country regression analysis comprising 166 countries. The gross Domestic Product per capita (based in purchased power parity, constant 2011 international dollars) in these countries is positively associated with the environmental performance index (EPI) calculated by Yale and Columbia University in 2016. Furthermore, the causality of this relationship is from GDP per capita to Environmental Performance and both Environmental Health (EH) and Ecosystem Vitality (EV) are positively associated with GDP per capita. Environmental Health (EH) is stronger related to GDP per capita, meaning that investments in public health, sanitation and infrastructure are increasing as countries develop.

This article aims to extend evaluation of the classic multifactor model of Carhart (1997) for the case of global equity indices and to expand analysis performed in Sakowski et. al. (2015). Our intention is to test several modifications of these models to take into account different dynamics of equity excess returns between emerging and developed equity indices. Proposed extensions include a volatility regime switching mechanism (using dummy variables and the Markov approach) and the fifth risk factor based on realized volatility of index returns. Moreover, instead of using data for stocks of a particular market (which is a common approach in the literature), we check performance of these models for weekly data of 81 world investable equity indices in the period of 2000-2015. Such an approach is proposed to estimate an equity risk premium for a single country. Empirical evidence reveals important differences between results for classical models estimated on single stocks (either in international or US-only frameworks) and models evaluated for equity indices. Additionally, we observe substantial discrepancies between results for developed countries and emerging markets. Finally, using weekly data for the last 15 years we illustrate the importance of model risk and data overfittng effects when drawing conclusions upon results of multifactor models. [ABSTRACT FROM AUTHOR]