SummaryHyperhomocysteinemia is an established risk factor for atherosclerosis and vascular disease. Until the early nineties the relationship with venous thrombosis was controversial. At this moment ten case-control studies on venous thrombosis are published. We performed a meta-analysis of these reports.We performed a MEDLINE-search from 1984 through June 1997 on the keywords “homocysteine” or “hyperhomocysteinemia” and “venous thrombosis”, which yielded ten eligible case-control studies.We found a pooled estimate of the odds ratio of 2.5 (95% CI 1.8-3.5) for a fasting plasma homocysteine concentration above the 95th percentile or mean plus two standard deviations calculated from the distribution of the respective control groups. For the post-methionine increase in homocysteine concentration we found a pooled estimate of 2.6 (95% CI 1.6-4.4).These data from case-control studies support hyperhomocysteinemia as a risk factor for venous thrombosis. Further research should focus on the pathophysiology of this relationship and on the clinical effects of reducing homocysteine levels by vitamin supplementation.

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In this study we propose a semi-parametric, parsimonious Value at Risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods - ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in Value at Risk estimates. To the best of our knowledge, this paper is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gra-dient-boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. The neural network models are in general quite unstable.

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