Search results

Dietary advice is given with the aim of improving health, either on a national scale, or at an individual level. The NACNE and COMA Reports have recommended changes in the British diet, and recent change has also occurred in dietary advice and treatment for conditions such as diabetes and bowel disease. However it is important to know how the general public, without nutritional training, view the health qualities of foods, and what influences these views, particularly as people assess food generally in non‐nutritional terms. Without this knowledge effective nutritional education cannot be undertaken.

Defines the number of recorded cases of Bovine Spongiform Encephalopathy (BSE) in the UK as comprising those reported to the Ministry of Agriculture, Fisheries and Food (MAFF) and checked by them using histopathological techniques. Proposes that, if it is assumed that BSE is a similar condition to other mammalian transmissible spongiform encephalopathies (TSEs), and if other specific assumptions are made, it is possible to estimate the true number of cases of BSE and, hence, the number of human beings who have been infected in the UK. States that approximately 6.87 per cent of cattle born in 1988 became infected with BSE, with lower numbers in antecedent years, and that BSE cases reported in the UK represent approximately 23 per cent of the cattle which have become infected and are hence potentially infective to other animals, including man. Discloses the fact that TSEs of animals, of which BSE is one, can be transmitted to a mean of at least 70 per cent of other species and that oral transmission has been successful. Uses the potential levels of infectivity of the bovine products present in human food in the UK from 1984 to 1997, together with data as to individual diets within the population, to assess the number of people who would be expected to have eaten the minimum potentially infective dose or more. Discusses the possible effects on human health.

The National Advisory Committee on Nutrition Education, NACNE for short, was the brain child of the British Nutrition Foundation and the Health Education Council. It was formed in 1979, and as well as the two parent bodies now includes nutrition educationists, others from the academic world, representatives of the food industry, the Department of Health and Social Security, the Ministry of Agriculture, Fisheries and Food, and also the Scottish Health Education Group.

The purpose of this paper is to present findings from the Trans Mental Health Study (McNeil et al., 2012) – the largest survey of the UK trans population to date and the first to explore trans mental health and well-being within a UK context. Findings around suicidal ideation and suicide attempt are presented and the impact of gender dysphoria, minority stress and medical delay, in particular, are highlighted.

This represents a narrative analysis of qualitative sections of a survey that utilised both open and closed questions. The study drew on a non-random sample (n=889), obtained via a range of UK-based support organisations and services.

The study revealed high rates of suicidal ideation (84 per cent lifetime prevalence) and attempted suicide (48 per cent lifetime prevalence) within this sample. A supportive environment for social transition and timely access to gender reassignment, for those who required it, emerged as key protective factors. Subsequently, gender dysphoria, confusion/denial about gender, fears around transitioning, gender reassignment treatment delays and refusals, and social stigma increased suicide risk within this sample.

Due to the limitations of undertaking research with this population, the research is not demographically representative.

The study found that trans people are most at risk prior to social and/or medical transition and that, in many cases, trans people who require access to hormones and surgery can be left unsupported for dangerously long periods of time. The paper highlights the devastating impact that delaying or denying gender reassignment treatment can have and urges commissioners and practitioners to prioritise timely intervention and support.

The first exploration of suicidal ideation and suicide attempt within the UK trans population revealing key findings pertaining to social and medical transition, crucial for policy makers, commissioners and practitioners working across gender identity services, mental health services and suicide prevention.

Copula modeling enables the analysis of multivariate count data that has previously required imposition of potentially undesirable correlation restrictions or has limited attention to models with only a few outcomes. This article presents a method for analyzing correlated counts that is appealing because it retains well-known marginal distributions for each response while simultaneously allowing for flexible correlations among the outcomes. The proposed framework extends the applicability of the method to settings with high-dimensional outcomes and provides an efficient simulation method to generate the correlation matrix in a single step. Another open problem that is tackled is that of model comparison. In particular, the article presents techniques for estimating marginal likelihoods and Bayes factors in copula models. The methodology is implemented in a study of the joint behavior of four categories of US technology patents. The results reveal that patent counts exhibit high levels of correlation among categories and that joint modeling is crucial for eliciting the interactions among these variables.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

Motivated by the growing importance of the expected shortfall in banking and finance, this study aims to compare the performance of popular non-parametric estimators of the expected shortfall (i.e. different variants of historical, outlier-adjusted and kernel methods) to each other, selected parametric benchmarks and estimates based on the idea of forecast combination.

Within a multidimensional simulation setup (spanned by different distributional settings, sample sizes and confidence levels), the authors rank the estimators based on classic error measures, as well as an innovative performance profile technique, which the authors adapt from the mathematical programming literature.

The rich set of results supports academics and practitioners in the search for an answer to the question of which estimators are preferable under which circumstances. This is because no estimator or combination of estimators ranks first in all considered settings.

To the best of their knowledge, the authors are the first to provide a structured simulation-based comparison of non-parametric expected shortfall estimators, study the effects of estimator averaging and apply the mentioned profiling technique in risk management.

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their pricing an estimation of the dependence structure of defaults, which is known to exhibit tail dependence as reflected in observed default contagion. A popular model with this property is the (Student's) t-copula; unfortunately there is no fast method to calibrate the degree of freedom parameter.

In this paper, within the framework of Schönbucher's copula-based trigger-variable model for basket CDS pricing, we propose instead to calibrate the full multivariate t distribution. We describe a version of the expectation-maximization algorithm that provides very fast calibration speeds compared to the current copula-based alternatives.

The algorithm generalizes easily to the more flexible skewed t distributions. To our knowledge, we are the first to use the skewed t distribution in this context.

Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors consider asymptotic trimming in such a setting. Specifically, the authors propose a bias-corrected tail-trimmed estimator such that it is consistent and has finite variance. The authors show that the proposed estimator is asymptotically normal, and has good finite-sample properties in a Monte Carlo study.