欧洲单一金融市场中的最后贷款人(LLR)

本文研究在欧盟单一金融市场中有效发挥最后贷款人功能所面临的挑战.简要分析了欧盟金融业的一些特点,这些特点可能增加欧盟系统性金融风险;讨论了预防和解决金融问题的欧盟金融稳定构架的复杂性(最后贷款人的运作也包括在这个构架之中);论述了当系统性金融风波影响不止一个欧盟成员国时,如何实现最后贷款人功能.最后提出了提高现有构架效率所面临的挑战和可行的途径.     

Pairs trading under transaction costs using model predictive control

Model predictive control with proportional transactions costs provides a good approximation to the optimal trading strategy     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.     

Tipping the scale? The workings of monetary policy through trade

The monetary policy entails demand‐augmenting and diverting effects, and its impact on the trade balance—and on other countries—depends on the magnitude of these opposing effects. Using U.S. data and a sign‐restricted structural vector autoregressive identification, we investigate the importance of these effects. Overall, the results indicate that a monetary loosening (tightening) leads to a strengthening (weakening) of the overall trade balance, indicating that demand diversion dominates. The paper also explores changes in the effects following the global financial crisis, reflecting the impaired monetary transmission mechanism.     

Interplay between ethanol and electric vehicles as low carbon mobility options for passengers in the municipality of São Paulo

The Brazilian cities as well as many of the large urban centers in the world continue to expand, increasing the demand for mobility and transport, while, at the same time, the same cities are investing in greenhouse gas (GHG) mitigation to avoid climate change. Brazil's urbanization rate increased from 26% in 1940 to almost 70% in 1980. During this period, the Brazilian population tripled and the urban population multiplied by seven. In 2010, the transport sector in São Paulo accounted for 71% of the total emissions released by the energy sector. Ethanol has been considered a fuel with less greenhouse gas emissions, when compared with fossil fuels. However, ethanol production would have to double to meet the expected demand. Electric vehicles (EVs) market is expanding around the world, and is also an option to reduce the transport emissions, if powered by clean electricity. To assess whether the adoption of EVs might bring more benefits than the current ethanol, we develop prospective scenarios supported by the Long-range Energy Alternatives Planning (LEAP) simulation tool, taking a bottom-up tank-to-wheel approach to consider the CO₂ emissions of car in São Paulo. The scenario considering a substitution of 25% of gasoline-powered cars by EV in 2030 showed a reduction in energy consumption and CO₂ emissions, around 15% and 26% respectively in that year in comparison with 2015. We discuss the interplay between ethanol and EV, also considering emission coefficients from life cycle analysis conducted in Brazil, and concluded EV will have higher positive impact on climate change mitigation than ethanol.     

The strategic strength of weak signal analysis

Foresight can be seen as a social cognition process involving a complex set of methods and interactive processes intended to assist policy in becoming more adaptive and forward-oriented in unpredictable environments. As a form of foresight raw material, “weak signals” can be thought of as gross, unstructured, fragmented, incomplete and inadvertent environmental data that may be refined into valuable information regarding context and further be articulated into strategically actionable knowledge. As advanced indicators that precede significant discrete one-off events and/or novel developments in the rate and direction of trends, their analysis has the potential to facilitate the real-time alignment between organisational decision-making and changing external circumstances. These predictors of future change pose fundamental problems of identification and interpretation and represent a challenge to established mental models. Thus, the practical significance of weak signals is that they can be transformed into meaningful insight for policy action. Such a value, however, does not materialise automatically. Realising this potential requires a degree of tolerance and fluidity of the collective cognitive frameworks by which weak signals can be apprehended, assessed and acted upon. This paper aims at covering the scope of perceptions and actions typically involved in the tracing and tracking of this shaping process.     

Non‐compliance with the Minimum Wage Law when Completely New Minimum Wage Laws Are Established: The Case of South Africa

Abstract: Using the case of South Africa, we study compliance with the minimum wage law when completely new minimum wage levels are established. By even the best estimate, the compliance rate in South Africa would not exceed 40 per cent for the agricultural sector and 30 per cent for the domestic worker sector. The compliance rates by subpopulation groups reveal that the compliance rate is low where the cost to comply with the minimum wage law is high, implying that government inspections are not done where they are likely to have the highest payoff. However, if the government tries to enforce the policy more strictly, the poor/less skilled would be adversely affected.