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Purpose – This chapter aims to investigate the stock market comovements between Mexico and the world capital market using nonlinear modeling tools.

Methodology/approach – We apply recent nonlinear cointegration and nonlinear error correction models (NECMs) to investigate the comovements between stock prices over the recent period.

Findings – While the previous literature only highlights some evidence of time-varying comovements, our chapter aims to specify the mechanism characterizing the comovement process through the comparison of two nonlinear error correction models (NECMs). It shows a nonlinear relationship between stock prices that are activated per regime.

Originality – Studying the integration hypothesis between stock markets over the recent financial crisis, our findings highlight strong evidence of significant comovements that explain the global collapse of stock markets in 2008–2009.

Theory and method are inherently intertwined in the creation and maintenance of most areas of scientific inquiry. The organizational sciences, in general, and the occupational stress area, in particular, are no exceptions. In this paper, we argue that an implicit supposition of linear independent–dependent variable forms has driven both theory and method, and as such, presents a characterization of organizational science and stress scholarship that is incomplete at best. We also review stress literature that has acknowledged the potential for nonlinear stressor–strain associations and offer empirical examples of both restricted and non-restricted nonlinearity. We conclude by offering prescriptions for scholars conducting research that extends beyond the examination of linear forms exclusively.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

We investigate predictive abilities of nonlinear models for stock returns when density forecasts are evaluated and compared instead of the conditional mean point forecasts. The aim of this paper is to show whether the in-sample evidence of strong nonlinearity in mean may be exploited for out-of-sample prediction and whether a nonlinear model may beat the martingale model in out-of-sample prediction. We use the Kullback–Leibler Information Criterion (KLIC) divergence measure to characterize the extent of misspecification of a forecast model. The reality check test of White (2000) using the KLIC as a loss function is conducted to compare the out-of-sample performance of competing conditional mean models. In this framework, the KLIC measures not only model specification error but also parameter estimation error, and thus we treat both types of errors as loss. The conditional mean models we use for the daily closing S&P 500 index returns include the martingale difference, ARMA, STAR, SETAR, artificial neural network, and polynomial models. Our empirical findings suggest the out-of-sample predictive abilities of nonlinear models for stock returns are asymmetric in the sense that the right tails of the return series are predictable via many of the nonlinear models, while we find no such evidence for the left tails or the entire distribution.

Purpose – The purpose of this chapter is to investigate the linear and nonlinear short- and long-run relationships between the real price of oil and the US real effective exchange rate.

Methodology/approach – We use recent linear and nonlinear econometric techniques over the period 1973–2009.

Findings – Our main findings are that (i) there is significant evidence that both variables contain a unit root; (ii) the oil price and the US exchange rate are strongly linked in the short run; and finally (iii) there are some signs of nonlinearity in the oil–exchange rate relationship.

Originality – Using recent econometric techniques, we show that exchange rates are not a fundamental determinant of oil prices but exchange rate changes help to better forecast oil prices in the short run.

This chapter presents an overview of the Bitcoin and its impacts on the environment and economics from the viewpoint of carrying out a systematic analysis of the literature related to the environmental and economic effect of digital currency. It is aimed to summarize and critically examine the points of view regarding Bitcoin mining, considering its effects on global warming and the social environment, employing peer-reviewed data associated through literatures. As a result, this study provides the chance to analyze the set of knowledge regarding the effects of the Bitcoin mining procedure on the ecosystem in regard to energy use and CO₂ emissions regarding unit root tests and causality test based on nonlinear models. The results show that there exists a nonlinear causal relationship between statistics on Bitcoin mining and the CO₂ emissions. The results also imply that Bitcoin remains to be a tool utilized in the economic environment for a range of objectives despite high energy consumption and some negative environmental impact within the scope of renewable energy; hence, authorities would take Bitcoin mining impacts into account to reduce CO₂ emissions.

International business (IB) research has mostly concentrated on two forms of internationalization: the gradual/step-by-step approach, and the “international new venture”/“born global” approach. The existence of nonlinearity – substantial “jumps” in international intensity – has received relatively modest attention. This paper addressed nonlinear internationalization processes: partial and complete de- and re-internationalization and the internationalization of born-again globals and born-again internationals. It concludes that nonlinear internationalization is neither an irregular deviation nor an exceptional case of linear internationalization but that linear internationalization is an exceptional case of nonlinear internationalization.