Conditional variance forecasts for long-term stock returns

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon

Ranking Risks

Dr. Fischhoff considers the role of government in helping citizens manage risks. He then offers a general procedure for risk ranking and concludes by discussing what can be done with a list of risks

Perceived Risks Versus Actual Risks: Managing Hazards through Negotiation

The author describes what she calls the Expert-Judgment Strategy , finding that, because it discounts lay perceptions of Risk, it interferes with the acceptance of important but Risky technologies

Comparing Risks Thoughtfully

Dr. Finkel argues that comparing risks is neither impossible nor immoral - but is nonetheless very difficult. He then discusses two major pitfalls of making such comparisons, one commonly cited and one routinely ignored, before sketching a framework for improving them

Special Issue “Machine Learning in Insurance”

Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field

Energy Security Risks and Risks Mitigation: an Overview

Central to the goals of competitiveness and sustainable development is the issue of energy. Is there enough? Where will it come from? What will it costs? Is its production and use environmentally sustainable? Many of these questions are now discussed under the general topic commonly referred to as “energy security.” This paper discusses more specifically what is meant by “energy security” and provide some reasons why it has been so difficult to forge a common approach to its achievement. A key factor includes substantively different views amongst countries on the optimal role of the market mechanism, the private sector, and governments either as an owner or regulator. Although different types of insecurity are discussed the focus in this paper is on the long-term physical availability of energy supplies. Interestingly, it appears that long-term security is not just an issue for consuming nations but also for the supplying nations due to concerns that future markets might not exist which would justify massive long-term investments today. However, long-term commitments by consuming and producing nations could lower the risks faced by each. In order to achieve increased security, the consuming countries need to diversify the types of energy used and their geographical sources, yet in many ways the trends have been the opposite as the geographic concentration of energy reserves, especially oil and gas are projected to increase. The ability of countries to increase alternative sources, such as renewables or nuclear, have considerable potential in the long term but in the medium term are limited by technological, environmental and political constraints. For oil and gas which will remain for the near future the most important energy sources, the most immediate problem is not one of insufficient supplies under the ground, but the lack of either government resources for public development or of a sufficiently investment friendly environment for private sector development in those countries that have the reserves. In addition there are a number of other complementary issues that need to be addressed such as improving the transport infrastructure and enhancements for research activities and technology transfer.competitiveness, Europe, transition economies, environmental policy, energy security, sustainable development

Food safety risks

Foodborne diseases are responsible for 420,000 deaths each year, one-third of them in Africa. Food safety issues are usually attributed to traditional food systems, but the development of modern food systems in low-income (LI) and lower middle-income (LMI) countries brings with it new risks. The setting of private standards can counterbalance the poor capacity of States to enforce safety regulations, but can also contribute to a dual system that concentrates the flow of unsafe products on the most vulnerable section of the population. Unsafe food not only poses significant public health risks but also contributes to large food losses and insecurity, and creates barriers to trade. Dealing with it requires a multisectoral approach

GSE Risks

This article was originally presented as a speech to the St. Louis Society of Financial Analysts, St. Louis, Missouri, January 13, 2005.Government-sponsored enterprises ; Risk

Risks of Large Portfolios

Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of such a risk estimator for large portfolios is largely unknown, and a simple inequality in the previous literature gives an infeasible upper bound for the estimation error. In addition, numerical studies illustrate that this upper bound is very crude. In this paper, we propose factor-based risk estimators under a large amount of assets, and introduce a high-confidence level upper bound (H-CLUB) to assess the accuracy of the risk estimation. The H-CLUB is constructed based on three different estimates of the volatility matrix: sample covariance, approximate factor model with known factors, and unknown factors (POET, Fan, Liao and Mincheva, 2013). For the first time in the literature, we derive the limiting distribution of the estimated risks in high dimensionality. Our numerical results demonstrate that the proposed upper bounds significantly outperform the traditional crude bounds, and provide insightful assessment of the estimation of the portfolio risks. In addition, our simulated results quantify the relative error in the risk estimation, which is usually negligible using 3-month daily data. Finally, the proposed methods are applied to an empirical study