Search results

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

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.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.

In this article, we examine the different causal chains leading to the crisis in the United States and around the world, emphasizing the market developments, political decisions, and organizational factors that led to the financial and economic meltdown. We argue that a series of political, regulatory, and organizational decisions and events prepared the ground for a major breakdown of financial and economic institutions, a “normal accident” that produced systemic reverberations across markets around the world. In the United States, political, regulatory, and organizational decisions made during the 1990s led to a situation of simultaneously high complexity and tight coupling in the financial system. The global economy also became more complex and tightly coupled during the 1990s, contributing to the rapid spread of the crisis across countries. We propose that solutions to the crisis will need to be tailored to the specific ways in which countries experienced the meltdown and the political preferences of interest groups and citizens. For the United States, the best approach would be to allow for a complex and innovative financial system but with a much reduced degree of coupling so as to avoid another financial normal accident.

This chapter aimed to find out which factors affected three Estonian case firms’ serial nonlinear internationalization before and during the Covid-19 pandemic. It also studied how these firms responded to the pandemic and what they learned from their previous business experience. It contributed to the literature on serial nonlinear internationalization and to studies on the (potential) impacts of Covid-19 on firms’ local and international activities by showing that these case firms exited and re-entered foreign markets and experienced considerable export fluctuations before 2020, during and after it. Thus, serial nonlinear internationalization should be perceived as something normal, and it can be expected to occur both during stable and unstable times: it does not only characterize economic crises, and it should not be seen as a failure sign. The pandemic affected the case firms’ internationalization considerably – for instance, via demand fluctuations and travel restrictions – but it also created some additional business opportunities: for example, by creating demand for some additional products and by increasing some customer groups’ interest for some existing products. These firms’ (international) business experience acquired before Covid-19 was also useful during the pandemic as they continued with some previous expansion activities. On the other hand, they also tried something new. As a result, all firms’ total turnover increased in 2020, and it might also increase in 2021, despite some problems in the first quarter.