The International Regulatory Regime on Capital Flows and Trade in Services

Capital controls and exchange restrictions are used to restrict international capital flows during economic crises. This paper looks at the legal implications of these restrictions and explores the current international regulatory framework applicable to international capital movements and current payments. It shows how international capital flows suffer from the lack of a comprehensive and coherent regulatory framework that would harmonize the patchwork of multilateral, regional, and bilateral treaties that currently regulate this issue.capital controls; exchange restrictions; international capital flows; economic crises

Hidden sovereign finance

Recent scandals in the sovereign debt market have highlighted the risks associated with hidden debt transactions. These are sovereign debt transactions in favour of a central government, substate entity or state-owned enterprise, whose entire existence or whose terms have not been fully disclosed in violation of local administrative or constitutional requirements. The phenomenon was first reported with regard to the Mozambique hidden loans case, but it likely extends to many other countries. The multifaceted nature of this phenomenon makes it difficult to provide a coherent picture. Lenders involved in hidden debt include private banks, state-owned banks, governments, and commodity traders. Products include loans, government guarantees, derivatives, and trade financing schemes linked to commodities exports. The goal of the paper is to provide a framework to analyse the legal and regulatory landscape applicable to these transactions with specific focus on the legal obligations of lenders. At present, there are a number of voluntary standards and guidelines for lenders. These include the UNCTAD’s Principles on Promoting Responsible Sovereign Lending and Borrowing, the recently approved IIF/G20 Voluntary Principles for Debt Transparency. This essay argues that hidden sovereign finance is a multifaceted legal problem, which presents three distinct components: the violation of the borrower’s laws governing public financing, the possible presence of corruption, and the active hiding of the transaction. This means that any effective policy to tame this phenomenon must rely on these three pillars: civil litigation in commercial courts, criminal prosecution of corruption, and loans disclosure to put sovereign finance under closer public scrutiny. In order to make the analysis simpler, I focus on English law and on the practice of English courts

Sustainable Finance and Sovereign Debt: The Illusion to Govern by Contract

Sovereign debt markets are rapidly venturing into the world of sustainable finance. Sovereign and sub-sovereign borrowers increasingly use ‘green’, ‘social’, and ‘sustainability’ bonds and loans to finance their domestic sustainability agenda. The rationale behind those instruments is to leverage the power of financial markets to incentivize public borrowers to pursue sustainability reforms and projects that would otherwise be difficult to implement. At the same time, given its clear objective to influence domestic policies, some see sovereign sustainable finance as an invasion of national sovereignty and a new form of private conditionality. This article assesses these claims. It sets out a theory of sovereign sustainable bonds that highlights the incentives of the two contractual parties—institutional investors and sovereign borrowers—to use finance as a tool for domestic sustainability reforms. I demonstrate that neither of the two parties has any incentive to use debt instruments to pursue a change in domestic policies and sustainability practices. A lack of financial incentives for investors and constitutional and political limitations on the sovereign borrower’s side make the environmental, social, and governance (ESG) contractual bargain very difficult to negotiate and implement. At the same time, both parties share the common goal of tapping into the ever-expanding ESG market. This results in a sustainable bond structure that only superficially addresses the sustainability objective for which it is marketed to investors

A deep learning approach to solve forward differential problems on graphs

We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabolic, and hyperbolic problems on graphs. A system of physics-informed neural network (PINN) models is used to solve the differential equations, by assigning each PINN model to a specific edge of the graph. Kirkhoff-Neumann (KN) nodal conditions are imposed in a weak form by adding a penalization term to the training loss function. Through the penalization term that imposes the KN conditions, PINN models associated with edges that share a node coordinate with each other to ensure continuity of the solution and of its directional derivatives computed along the respective edges. Using individual PINN models for each edge of the graph allows our approach to fulfill necessary requirements for parallelization by enabling different PINN models to be trained on distributed compute resources. Numerical results show that the system of PINN models accurately approximate the solutions of the differential problems across the entire graph for a broad set of graph topologies.Comment: 40 pages, 27 figure

Anderson acceleration with approximate calculations: applications to scientific computing

We provide rigorous theoretical bounds for Anderson acceleration (AA) that allow for efficient approximate calculations of the residual, which reduce computational time and memory storage while maintaining convergence. Specifically, we propose a reduced variant of AA, which consists in projecting the least squares to compute the Anderson mixing onto a subspace of reduced dimension. The dimensionality of this subspace adapts dynamically at each iteration as prescribed by computable heuristic quantities guided by the theoretical error bounds. The use of the heuristic to monitor the error introduced by approximate calculations, combined with the check on monotonicity of the convergence, ensures the convergence of the numerical scheme within a prescribed tolerance threshold on the residual. We numerically assess the performance of AA with approximate calculations on: (i) linear deterministic fixed-point iterations arising from the Richardson's scheme to solve linear systems with open-source benchmark matrices with various preconditioners and (ii) non-linear deterministic fixed-point iterations arising from non-linear time-dependent Boltzmann equations.Comment: 23 pages, 3 figures, 1 tabl

A deep learning approach for detection and localization of leaf anomalies

The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.Comment: 23 pages, 8 figure