Trade Policies and the Highly Indebted Countries

In the early 1980s, faced with a mounting debt crisis, mosthighly indebted developing countries increased trade barriersto save foreign exchange—but in the last three to fouryears, they have reversed course. Almost all of these countrieshave undergone real devaluations, and many have undertaken significantliberalizations, so much so that some (Bolivia, Costa Rica,Jamaica, Mexico, Morocco, and Uruguay) are less protectionistthan before the debt crisis. But industrial countries have imposed new nontariff barriersagainst imports from highly indebted countries. Canada, Australia,the European Community, and the United States have greatly increasedthe use of countervailing duties and antidumping actions. Thismakes it more difficult for highly indebted countries to payoff their debts, and ultimately rebounds on creditor governmentsand banks.     

Frankenstein unbound : Towards a legal definition of artificial intelligence

The Frankenstein myth of creature turning on creator is centuries if not millenia old. But only recently under the impact of the cybernetic revolution has this fantasy entered the realm of the possible. This paper explores the legal ramifications of Artificial Intelligence (AI) with specific emphasis on “humanoid” criminality. Following a review of the actual (or theoretically proven) powers of artificially intelligent machine automata and the likely advances to be made in the future, four general categories of AI harmful behaviour are suggested, with illustrations from cybernetic research and science fiction. An analysis is made of the jurisprudential principles underlying several legal categories already existent, upon which future cybernetic law may be based.     

Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions

Given the advances in online data acquisition systems, statistical learning models are increasingly used to forecast wind speed. In electricity markets, wind farm production forecasts are needed for the day-ahead, intra-day, and real-time markets. In this work, we use a spatiotemporal model that leverages wind dynamics to forecast wind speed. Using a priori knowledge of the wind direction, we propose a maximum likelihood estimate of the inverse covariance matrix regularized with a hierarchical sparsity-inducing penalty. The resulting inverse covariance estimate not only exhibits the benefits of a sparse estimator, but also enables meaningful sparse structures by considering wind direction. A proximal method is used to solve the underlying optimization problem. The proposed methodology is used to forecast six-hour-ahead wind speeds in 20-minute time intervals for a case study in Texas. We compare our method with a number of other statistical methods. Prediction performance measures and the Diebold–Mariano test show the potential of the proposed method, specifically when reasonably accurate estimates of the wind directions are available.     

The Evolving Role of the Actuary in Financial Reporting of Insurance

Abstract

The demands that financial reporting of insurance companies present to actuaries are great and growing. With the prospects of change in the rules for financial reporting becoming more likely and insurance products becoming more complex, it is desirable to examine the evolving roles of the actuary and the actuarial profession. This paper describes these changes and the value that actuaries bring to financial reporting. The challenges presented are significant. As the methods of assessing and managing risk change are becoming more complex, the best efforts of the profession and individual actuaries will be needed to ensure that the actuary’s role is enhanced and expanded. Not only will the techniques used evolve, but the audiences served by the actuary will become even more demanding. The actuarial profession is better situated than other professions to meet these demands.     

Top-floor discounts in residential buildings: Evidence from South Korea

This study assesses the phenomenon of resale price discounts for top-floor units of residential buildings, or “top-floor discounts.” Using 1.9 million resale transactions of apartment units from South Korea, we find that top-floor discounts range from 1.3% to 8.3% of resale prices. We hypothesize that top-floor discounts exist because of larger heating/cooling costs for these units relative to those on lower levels, resulting from relatively more extreme variation in indoor temperatures unique to top-floor apartment units. Using a household-level survey data on energy consumption and exploiting variations in weather conditions and roof material across time and regions throughout South Korea, we provide evidence in support of our hypothesis.     

Youth unemployment in Nigeria: nature,causes and solutions

Quality & Quantity - This study investigates the nature and causes of youth unemployment in Nigeria, with the aim of proffering evidence-based workable solutions as policy recommendation. Its...