Detecting and modelling nonlinearity in flexible exchange rate time series

The aim of this paper is to examine the appropriateness of nonlinear time series analysis as a framework in which to model the dynamics of exchange rates. This aim has been motivated by the questioning of the power of classical unit root tests, the accumulating amount of evidence which suggests that exchange rates follow some kind of nonlinear process, and the fact that standard asset pricing theories do not explain well the empirical observations of exchange rate movements. The paper has three major objectives. First, to test for the presence of unit roots in nominal exchange rate time series. Second, for those nominal exchange rate time series found to be stationary, to test for nonlinearity using both tests derived without a specific nonlinear alternative in mind and tests against a specific nonlinear model. Finally, we motivate the types of nonlinearity for which we test by examining a recently proposed nonlinear model of exchange rate dynamics.The authors are from the School of Finance and Economics, University of Technology, Sydney, Australia. We are extremely grateful to Alex Beliak and Alexander Khomin for their research assistance and help in developing our computer programs.     

Pigou in the 21st Century: a tribute on the occasion of the 100th anniversary of the publication of The Economics of Welfare

The year 2020 marks the centennial of the publication of Arthur Cecil Pigou’s magnum opus The Economics of Welfare. Pigou’s pricing principles have had an enduring influence on the academic debate, with a widespread consensus having emerged among economists that Pigouvian taxes or subsidies are theoretically desirable, but politically infeasible. In this article, we revisit Pigou’s contribution and argue that this consensus is somewhat spurious, particularly in two ways: (1) Economists are too quick to ignore the theoretical problems and subtleties that Pigouvian pricing still faces; (2) The wholesale skepticism concerning the political viability of Pigouvian pricing is at odds with its recent practical achievements. These two points are made by, first, outlining the theoretical and political challenges that include uncertainty about the social cost of carbon, the unclear relationship between the cost–benefit and cost-effectiveness approaches, distributional concerns, fragmented ministerial responsibilities, an unstable tax base, commitment problems, lack of acceptance and trust between government and citizens as well as incomplete international cooperation. Secondly, we discuss the recent political success of Pigouvian pricing, as evidenced by the German government’s 2019 climate policy reform and the EU’s Green Deal. We conclude by presenting a research agenda for addressing the remaining barriers that need to be overcome to make Pigouvian pricing a common political practice.

     

Markov chains under nonlinear expectation

In this paper, we consider continuous‐time Markov chains with a finite state space under nonlinear expectations. We define so‐called Q‐operators as an extension of Q‐matrices or rate matrices to a nonlinear setup, where the nonlinearity is due to model uncertainty. The main result gives a full characterization of convex Q‐operators in terms of a positive maximum principle, a dual representation by means of Q‐matrices, time‐homogeneous Markov chains under convex expectations, and a class of nonlinear ordinary differential equations. This extends a classical characterization of generators of Markov chains to the case of model uncertainty in the generator. We further derive an explicit primal and dual representation of convex semigroups arising from Markov chains under convex expectations via the Fenchel–Legendre transformation of the generator. We illustrate the results with several numerical examples, where we compute price bounds for European contingent claims under model uncertainty in terms of the rate matrix.     

Deep empirical risk minimization in finance: Looking into the future

Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning. These methods, designed to directly construct the optimal feedback representation of hedging or investment decisions, are analyzed in this framework demonstrating their effectiveness as well as their susceptibility to generalization error. Use of classical techniques shows that over-training renders trained investment decisions to become anticipative, and proves overlearning for large hypothesis spaces. On the other hand, nonasymptotic estimates based on Rademacher complexity show the convergence for sufficiently large training sets. These results emphasize the importance of synthetic data generation and the appropriate calibration of complex models to market data. A numerically studied stylized example illustrates these possibilities, including the importance of problem dimension in the degree of overlearning, and the effectiveness of this approach.     

The socio-economic determinants of terrorism and political violence in Western Europe (1994–2007)

The main objective of this paper is to empirically investigate the socio-economic causes of terrorism and political violence in a sample of 12 countries in Western Europe. First, we show that in western European countries the classical economic argument of opportunity cost is confirmed. That is, the larger is the set of current economic opportunities for individuals the lower is the likelihood or the willingness for them to be involved in a terrorist activity. Second, expected future economic growth seems to be associated with an increase in current terrorist activities. Eventually, our results also show that terrorist brutality (measured in people killed) is positively associated with real GDP per capita.