How Risky Is the Value at Risk?

The recent financial crisis has raised numerous questions about the accuracy of value-at-risk (VaR) as a tool to quantify extreme losses. In this paper we present empirical evidence from assessing the out-of-sample performance and robustness of VaR before and during the recent financial crisis with respect to the choice of sampling window, return distributional assumptions and stochastic properties of the underlying financial assets. Moreover we develop a new data driven approach that is based on the principle of optimal combination and that provides robust and precise VaR forecasts for periods when they are needed most, such as the recent financial crisis.Value at Risk, model risk, optimal forecast combination

Romanian Questionnaire to Assess the Prevalence of Occupational Hand Eczema among Healthcare Providers

Abstract Occupational skin diseases have an unknown prevalence in Romania, although they are considered the most frequent occupational diseases reported in Western European countries. Self-reported hand eczema among healthcare providers by questionnaire aims to estimate the prevalence of work-related hand eczema and associated risk factors in hospitals and outpatient units in Romania. The aim of this study is to discuss and to validate a questionnaire for surveying work-related skin diseases and exposure among healthcare providers

Neonicotinoid insecticides as emerging contaminants in agricultural soil

Using an LC-MS-MS method for detection of 6 neonicotinoid insecticides (imidacloprid, dinotefuran, acetamiprid, clothianidin, thiamethoxam, nitenpyram) was developed a new performant extraction method based on sonication treatment of soil samples, which were previously dried, grounded, homogenized, sieved (2 mm) and subjected to the selective extraction process with acetonitrile. Then the obtained extracts were diluted with ultrapure water (ratio 1: 100) and subjected to purification by Strata C18 SPE extraction using cartridges loaded with 200 mg/6 mL of octa-dodecyl-silica adsorbent phase. The entire methodology allowed obtaining quantification limits at trace level that varied in the range 0.3-0.9 ng/g and recoveries between 71.4% and 109.6%. In the agricultural soil samples, taken from the lands cultivated with wheat, corn, sunflower, beans, located in Prahova and Giurgiu counties (Romania), only four neonicotinoids out of the total of six were quantified imidacloprid (0.38 ng/g-56.9 ng/g), acetamiprid (1.7-7.2 ng/g), thiamethoxam (1.05-6.7 ng/g), clothianidin (1.1-1.5 ng/g)

Vier Essays über die Messung finanzieller Risiken

Diese Dissertation untersucht die Messung finanzieller Risiken und besteht aus vier eigenständigen Forschungspapieren über die Analyse, Modellierung und Vorhersage solcher Risiken in verschiedenen wirtschaftlichen Szenarien. Die gegenwärtigen Risikomaße ignorieren größtenteils das systematische Risiko, das durch Korrelationen von Finanzanlagen, Finanzmärkten oder Finanzinvestoren induziert wird und sind in zunehmendem Maße Modellrisiken ausgesetzt, die infolge von falschen Modellspezifikationen, Schätzrisiken oder Messfehlern entstehen. Vor diesem Hintergrund versucht diese Arbeit, die Risiken in der Messung finanzieller Risiken ausführlich zu analysieren. Darüber hinaus wird versucht, neue Ansätze zu entwickeln, welche hinreichend genau die systematischen Zusammenhänge zwischen verschiedenen finanziellen Variablen messen und den Einfluss von Modellrisiken auf die Präzision von daraus resultierenden Risikomodellen minimieren

Comparison of the Polyphenolic Profile of Medicago sativa L. and Trifolium pratense L. Sprouts in Different Germination Stages Using the UHPLC-Q Exactive Hybrid Quadrupole Orbitrap High-Resolution Mass Spectrometry

Identification and quantification of polyphenols in plant material are of great interest since they make a significant contribution to its total bioactivity. In the present study, an UPLC-Orbitrap-MS/MS approach using the variable data acquisition mode (vDIA) was developed and applied for rapid separation, identification, and quantification of the main polyphenolic compounds in Medicago sativa L. and Trifolium pratense L. sprouts in different germination stages. Based on accurate MS data and fragment ions identification strategy, a total of 29 compounds were identified by comparing their accurate masses, fragment ions, retention times, and literatures. Additionally, a number of 30 compounds were quantified by comparing to the reference standards. Data were statistically analysed. For both plant species, the sprouts of the third germination day are valuable sources of bioactive compounds and could be used in phytotherapy and nutrition. Although Trifolium pratense L. (Red Clover) is considered to be a reference for natural remedies in relieving menopause disorders, alfalfa also showed a high level of biological active compounds with estrogenic activity

How Risky is the Value at Risk?

The recent financial crisis has raised numerous questions about the accuracy of value-at-risk (VaR) as a tool to quantify extreme losses. In this paper we present empirical evidence from assessing the out-of-sample performance and robustness of VaR before and during the recent financial crisis with respect to the choice of sampling window, return distributional assumptions and stochastic properties of the underlying financial assets. Moreover we develop a new data driven approach that is based on the principle of optimal combination and that provides robust and precise VaR forecasts for periods when they are needed most, such as the recent financial crisis.publishe

Dynamic Modelling and Forecasting of Realized Covariance Matrices

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The model is based on a multivariate, fractionally integrated Autoregressive Moving Average (ARMA) process for the elements of the Cholesky factors of the observed matrix series. This approach allows for joint modelling of the whole covariance matrix and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions on the model. The model is particularly suited to capture the long memory, typically observed in volatility processes of financial assets. We describe the forecasting procedure and provide an empirical application

Modelling and forecasting multivariate realized volatility

This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical application of the model, which shows that it outperforms other approaches in the extant literature, both in terms of statistical precision as well as in terms of providing a superior mean-variance trade-off in a classical investment decision setting

Modelling and Forecasting Multivariate Realized Volatility

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model