A multivariate FGD technique to improve VaR computation in equity markets

Abstract.: It is difficult to compute Value-at-Risk (VaR) using multivariate models able to take into account the dependence structure between large numbers of assets and being still computationally feasible. A possible procedure is based on functional gradient descent (FGD) estimation for the volatility matrix in connection with asset historical simulation. Backtest analysis on simulated and real data provides strong empirical evidence of the better predictive ability of the proposed procedure over classical filtered historical simulation, with a resulting significant improvement in the measurement of ris

A multivariate FGD technique to improve VaR computation in equity markets

We present a multivariate, non-parametric technique for constructing reliable daily VaR predictions for individual assets belonging to a common equity market segment, which takes also into account the possible dependence structure between the assets and is still computationally feasible in large dimensions. The procedure is based on functional gradient descent (FGD) estimation for the volatility matrix (see Audrino and Bühlmann, 2002) in connection with asset historical simulation and can also be seen as a multivariate extension of the filtered historical simulation method proposed by Barone-Adesi et al. (1998). Our FGD algorithm is very general and can be further adapted to other multivariate problems dealing with (volatility) function estimation. We concentrate our empirical investigations on the Swiss pharmaceutical and the US biotechnological equity market and we collect, using statistical and economical backtests, strong empirical evidence of the better predictive potential of our multivariate strategy over other univariate techniques, with a resulting significant improvement in the measurement of risk

Average conditional correlation and tree structures for multivariate GARCH models

We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions without resorting to any variance reduction technique. We back-test the models on a six-dimensional exchange-rate time series using different goodness-of-fit criteria and statistical tests. We collect empirical evidence of their strong predictive power, also in comparison to alternative benchmark procedures

Introduction: COVID-19 Models and the Difficult Balance between Methods and Values

The article addresses some of the most urgent and pressing issues emerging from the pandemic, tackling them from both an epistemological and an epidemiological point of view, with a focus on different forms of modeling and the impacts they can have in the construction of a common understanding of the disease and its transmission

Introduction: COVID-19 Models and the Difficult Balance between Methods and Values

The COVID-19 pandemic had an unprecedented impact not only on the socio-economic and political conditions worldwide but also on the practices of the scientific community and on the public image of science itself. The scientific community suddenly found itself in the spotlight and was pressured to rapidly produce evidence applicable to the management of the present health crisis. This in turn had some unexpected consequences, among which an increase of the publication speed and sometimes a decrease of the quality of peer review (see e.g., Chan 2020). At the same time, the public discussion of scientific issues related to COVID-19 among an audience often lacking the appropriate knowledge of the characteristics of modern science (e.g., critical reasoning, hypothetical nature of research, the role of uncertainty, \u2026 ), was associated with the emergence of extreme stances in the population

Knowledge Gaps and Research Priorities on the Health Effects of Heatwaves: A Systematic Review of Reviews

Although extreme weather events have played a constant role in human history, heatwaves (HWs) have become more frequent and intense in the past decades, causing concern especially in light of the increasing evidence on climate change. Despite the increasing number of reviews suggesting a relationship between heat and health, these reviews focus primarily on mortality, neglecting other important aspects. This systematic review of reviews gathered the available evidence from research syntheses conducted on HWs and health. Following the PRISMA guidelines, 2232 records were retrieved, and 283 reviews were ultimately included. Information was extracted from the papers and categorized by topics. Quantitative data were extracted from meta-analyses and, when not available, evidence was collected from systematic reviews. Overall, 187 reviews were non-systematic, while 96 were systematic, of which 27 performed a meta-analysis. The majority evaluated mortality, morbidity, or vulnerability, while the other topics were scarcely addressed. The following main knowledge gaps were identified: lack of a universally accepted definition of HW; scarce evidence on the HW-mental health relationship; no meta-analyses assessing the risk perception of HWs; scarcity of studies evaluating the efficacy of adaptation strategies and interventions. Future efforts should meet these priorities to provide high-quality evidence to stakeholders

A Cardiovascular Risk Score for Use in Occupational Medicine

Cardiovascular disease is one of the most frequent causes of long-term sickness absence from work. The study aims to develop and validate a score to assess the 10-year risk of unsuitability for work accounting for the cardiovascular risk. The score can be considered as a prevention tool that would improve the cardiovascular risk assessment during health surveillance visits under the assumption that a high cardiovascular risk might also translate into high risk of unsuitability for work. A total of 11,079 Italian workers were examined, as part of their scheduled occupational health surveillance. Cox proportional hazards regression models were employed to derive risk equations for assessing the 10-year risk of a diagnosis of unsuitability for work. Two scores were developed: the CROMA score (Cardiovascular Risk in Occupational Medicine) included age, sex, smoking status, blood pressure (systolic and diastolic), body mass index, height, diagnosis of hypertension, diabetes, ischemic heart disease, mental disorders and prescription of antidiabetic and antihypertensive medications. The CROMB score was the same as CROMA score except for the inclusion of only variables statistically significant at the 0.05 level. For both scores, the expected risk of unsuitability for work was higher for workers in the highest risk class, as compared with the lowest. Moreover results showed a positive association between most of cardiovascular risk factors and the risk of unsuitability for work. The CROMA score demonstrated better calibration than the CROMB score (11.624 (p-value: 0.235)). Moreover, the CROMA score, in comparison with existing CVD risk scores, showed the best goodness of fit and discrimination