Stochastic Volatility: Univariate and Multivariate Extensions

Stochastic volatility models, aka SVOL, are more difficult to estimate than standard time-varying volatility models (ARCH). Advances in the literature now offer well tested estimators for a basic univariate SVOL model. However, the basic model is too restrictive for many economic and finance applications. The use of the basic model can lead to biased volatility forecasts especially around crucial periods of high volatility. We extend the basic SVOL needs to allow for the leverage effect, through a correlation between observable and variance errors, and fat-tails in the conditional distribution. We develop a Bayesian Markov Chain Monte Carlo algorithm for this extended model. We also provide an algorithm to analyze a multivariate factor SVOL model. The method simultaneously performs finite sample inference and smoothing. We document the performance of the estimator and show why the extensions are warranted. We provide the researcher with a range of model diagnostics, such as the identification of outliers for stochastic volatility models or the assessment of the normality of the conditional distribution. We implement this methodology on a number of univariate financial time series. There is strong evidence of (1) non-normal conditional distributions for most series, and (2) a leverage effect for stock returns. We illustrate the robustness of the results to the choice of the prior distributions. These results have policy implications on decisions based upon prediction of volatility, especially when dealing with tail prediction as in risk management. Les modèles de volatilité stochastique, alias SVOL, sont plus durs à estimer que les modèles traditionnels de type ARCH. La littérature récente offre des estimateurs éprouvés pour un modèle SVOL univarié de base. Ce modèle est trop contraignant pour une utilisation en économie financière. Les prévisions de volatilité qu'il produit peuvent etre biaisées, particulièrement quand la volatilité est élevée. Nous généralisons le modèle de base en y ajoutant des effets de levier par le biais d'une corrélation entre les chocs observables et de variance, et la possibilité de distributions conditionnelles à queues épaisses. Nous développons un algorithme bayésien à chaînes markoviennes de Monte Carlo. Nous développons aussi un algorithme pour l'analyse d'un modèle SVOL multivarié à facteurs. Ces estimateurs permettent une inférence en échantillon fini pour les paramètres et les volatilités. Nous documentons les performances de l'estimateur et montrons que les extensions sont nécessaires. Nous testons la normalité des distributions conditionnelles. Cette méthode est mise en oeuvre sur plusieurs séries financières. Il y a une forte évidence (1) de distributions conditionnelles à queues épaisses, et (2) d'effets de levier pour les actifs financiers. Les résultats sont robustes et ont d'importantes implications sur les décisions fondées sur les prédictions de volatilité, particulièrement pour la gestion de risques.Stochastic volatility, ARCH, MCMC algorithm, leverage effect, risk management, fat-tailed distributions, Volatilité stochastique, ARCH, algorithme MCMC, effets de levier, gestion de risque, distributions à queues épaisses

Stochastic Volatility: Univariate and Multivariate Extensions

Les modèles de volatilité stochastique, alias SVOL, sont plus durs à estimer que les modèles traditionnels de type ARCH. La littérature récente offre des estimateurs éprouvés pour un modèle SVOL univarié de base. Ce modèle est trop contraignant pour une utilisation en économie financière. Les prévisions de volatilité qu'il produit peuvent etre biaisées, particulièrement quand la volatilité est élevée. Nous généralisons le modèle de base en y ajoutant des effets de levier par le biais d'une corrélation entre les chocs observables et de variance, et la possibilité de distributions conditionnelles à queues épaisses. Nous développons un algorithme bayésien à chaînes markoviennes de Monte Carlo. Nous développons aussi un algorithme pour l'analyse d'un modèle SVOL multivarié à facteurs. Ces estimateurs permettent une inférence en échantillon fini pour les paramètres et les volatilités. Nous documentons les performances de l'estimateur et montrons que les extensions sont nécessaires. Nous testons la normalité des distributions conditionnelles. Cette méthode est mise en oeuvre sur plusieurs séries financières. Il y a une forte évidence (1) de distributions conditionnelles à queues épaisses, et (2) d'effets de levier pour les actifs financiers. Les résultats sont robustes et ont d'importantes implications sur les décisions fondées sur les prédictions de volatilité, particulièrement pour la gestion de risques.Stochastic volatility models, aka SVOL, are more difficult to estimate than standard time-varying volatility models (ARCH). Advances in the literature now offer well tested estimators for a basic univariate SVOL model. However, the basic model is too restrictive for many economic and finance applications. The use of the basic model can lead to biased volatility forecasts especially around crucial periods of high volatility. We extend the basic SVOL needs to allow for the leverage effect, through a correlation between observable and variance errors, and fat-tails in the conditional distribution. We develop a Bayesian Markov Chain Monte Carlo algorithm for this extended model. We also provide an algorithm to analyze a multivariate factor SVOL model. The method simultaneously performs finite sample inference and smoothing. We document the performance of the estimator and show why the extensions are warranted. We provide the researcher with a range of model diagnostics, such as the identification of outliers for stochastic volatility models or the assessment of the normality of the conditional distribution. We implement this methodology on a number of univariate financial time series. There is strong evidence of (1) non-normal conditional distributions for most series, and (2) a leverage effect for stock returns. We illustrate the robustness of the results to the choice of the prior distributions. These results have policy implications on decisions based upon prediction of volatility, especially when dealing with tail prediction as in risk management

Models and Priors for Multivariate Stochastic Volatility

Discrete time stochastic volatility models (hereafter SVOL) are noticeably harder to estimate than the successful ARCH family of models. In this paper, we develop methods for finite sample inference, smoothing, and prediction for a number of univariate and multivariate SVOL models. Specifically, we model fat-tailed and skewed conditional distributions, correlated errors distributions (leverage effect), and two multivariate models, a stochastic factor structure model and a stochastic discount dynamic model. We specify the models as a hierarchy of conditional probability distributions: p(data/volatilities), p(volatilities/ parameters) and p(parameters). This hierarchy provides a natural environment for the construction of stochastic volatility models that depart from standard distributional assumptions. Given a model and the data, inference and prediction are based on the joint posterior distribution of the volatilities and the parameters which we simulate via Markov chain Monte Carlo (MCMC) methods. Our approach also provides a sensitivity analysis for parameter inference and an outlier diagnostic. Our framework, therefore, provides a general perspective on specification and implementation of stochastic volatility models. We apply various extensions of the basic SVOL model to many financial time series. We find strong evidence of non-normal conditional distributions for stock returns and exchange rates. We also find some evidence of correlated errors for stock returns. These departures from the basic model affect persistence and therefore should be incorporated if the model is used for variance prediction. Les modèles de volatilité stochastique (ci-après) SVOL sont singulièrement plus difficiles à estimer que les modèles de type ARCH qui connaissent un grand succès. Dans cet article, nous développons des méthodes en échantillons finis pour l'inférence et la prédiction, ceci pour un nombre de modèles SVOL univariés et multivariés. Plus précisément nous modélisons des distributions conditionnelles non-normales, des modèles avec effets de levier, et deux modèles multivariés; un modèle a structure de facteurs et un modèle d'escompte dynamique. Nous spécifions les modèles par une hiérarchie de distributions conditionnelles : p(données|volatilités), p(volatilités|paramètres), et p(paramètres). Cette hiérarchie fournit un environnement naturel pour l'élaboration de modèles de volatilité stochastique plus généraux que le modèle de base. Pour un modèle et un échantillon, l'inférence et la prédiction sont fondées sur la distribution postérieure jointe des volatilités et des paramètres que nous simulons avec des méthodes de Chaînes de Markov et de Monte Carlo (MCMC). Notre approche fournit aussi une analyse de sensitivité pour les paramètres et une analyse pour les outliers. Le cadre d'estimation fournit donc une perspective générale sur la spécification et l'implémentation des modèles de volatilité stochastique. Nous appliquons plusieurs extensions du modèle SVOL de base à de nombreuses séries financières. Il y a une forte évidence de non-normalité des distributions conditionnelles. Il y aussi une certaine évidence de corrélation des erreurs pour les retours sur actions. Ces élaborations du modèle de base ont une influence sur la persistance et devraient être incorporées en vue de prédictions de volatilité.Stochastic volatility; Forecasting and smoothing; Metropolis algorithm, Volatilité stochastique ; Inférence et prédiction ; Algorythme Metropolis

Transient sex-related changes in the mice hypothalamo–pituitary–adrenal (HPA) axis during the acute phase of the inflammatory process

The potential role of endogenous sex hormones in regulating hypothalamo–pituitary–adrenal (HPA) axis function was investigated after a single injection of endotoxin in adult (8 week old) BALB/c mice of both sexes. The effect of LPS on plasma ACTH, corticosterone (B), testosterone and oestradiol (E) levels and on anterior pituitary (AP) ACTH and adrenal B contents at different times after treatment was studied. The results indicate that: (a) basal B but not ACTH plasma levels were significantly higher in female than in male mice; (b) LPS significantly increased both ACTH and B plasma levels over the baseline 2 h after injection, both hormone levels being higher in female than in male mice; (c) although plasma ACTH concentrations recovered the basal value at 72 h after LPS in animals of both sexes, plasma B levels returned to the baseline only at 120 h after treatment; (d) E plasma levels significantly increased 2 h after LPS and returned to the baseline at 72 h post-treatment, in both sexes; (e) at 2 h after LPS, testosterone plasma levels significantly decreased in male mice and increased in female mice, recovering the baseline level at 120 and 72 h after LPS, respectively; (f) AP ACTH content was similar in both sexes in basal condition and it was significantly diminished 72 h post-treatment without sex difference; whereas AP ACTH returned to basal content 120 h after LPS in males, it remained significantly decreased in females; (g) basal adrenal B content was higher in female than in male mice, and it significantly increased in both sexes 2 h post-LPS, maintaining this sex difference. Whereas adrenal B returned to basal content 72 h after treatment in male mice, it remained significantly enhanced up to 120 h post-LPS in female animals. The data demonstrate the existence of a clear sexual dimorphism in basal condition and during the acute phase response as well as in the recovery of the HPA axis function shortly after infection

Numerical and experimental investigations of a microwave interferometer for the negative ion source SPIDER

The electron density close to the extraction grids and the co-extracted electrons represent a crucial issue when operating negative ion sources for fusion reactors. An excessive electron density in the plasma expansion region can indeed inhibit the negative ion production and introduce potentially harmful electrons in the accelerator. Among the set of plasma and beam diagnostics proposed for SPIDER upgrade, a heterodyne microwave (mw) interferometer at 100 GHz is being explored as a possibility to measure electron density in the plasma extraction region. The major issue in applying this technique in SPIDER is the poor accessibility of the probing microwave beam through the source metal walls and the long distance of 4 m at which mw modules should be located outside the vacuum vessel. Numerical investigations in a full-scale geometry showed that the power transmitted through the plasma source apertures was sufficient for the microwave module sensitivity. An experimental proof-of-principle of the setup was then performed. The microwave system was tested on an experimental full-scale test-bench mimicking SPIDER viewports accessibility constraints, including the presence of a SPIDER-like plasma. The outcome of first tests revealed that, despite the geometrical constraints, in certain conditions, the electron density measurements are possible. The main issue arises from decoupling the one-pass signal from spurious multipaths generated by mw beam reflections, requiring signal cross correlation analysis. These preliminary tests demonstrate that despite the 4 m distance between the mw modules and the presence of metal walls, plasma density measurement is possible when the 80-mm diameter ports are available. In this contribution, we discuss the numerical simulations, the preliminary experimental tests and suggest design upgrades of the interferometric setup to enhance signal transmission

Massive Increase, Spread, and Exchange of Extended Spectrum {beta}-Lactamase-Encoding Genes Among Intestinal Enterobacteriaceae in Hospitalized Children With Severe Acute Malnutrition in Niger.

Background. From the time of CTX-M emergence, extended-spectrum β-lactamase-producing enterobacteria (ESBL-E) have spread worldwide in community settings as well as in hospitals, particularly in developing countries. Although their dissemination appears linked to Escherichia coli intestinal carriage, precise paths of this dynamic are largely unknown. Methods. Children from a pediatric renutrition center were prospectively enrolled in a fecal carriage study. Antibiotic exposure was recorded. ESBL-E strains were isolated using selective media from fecal samples obtained at admission and, when negative, also at discharge. ESBL-encoding genes were identified, their environments and plasmids were characterized, and clonality was assessed with polymerase chain reaction-based methods and pulsed-field gel electrophoresis for E. coli and Klebsiella pneumoniae. E. coli strains were subjected to multilocus sequence typing. Results. The ESBL-E carriage rate was 31% at admission in the 55 children enrolled. All children enrolled received antibiotics during hospitalization. Among the ESBL-E-negative children, 16 were resampled at discharge, and the acquisition rate was 94%. The bla(CTX-M-15) gene was found in >90% of the carriers. Genetic environments and plasmid characterization evidenced the roles of a worldwide, previously described, multidrug-resistant region and of IncF plasmids in CTX-M-15 E. coli dissemination. Diversity of CTX-M-15-carrying genetic structures and clonality of acquired ESBL E. coli suggested horizontal genetic transfer and underlined the potential of some ST types for nosocomial cross-transmission. Conclusions. Cross-transmission and high selective pressure lead to very high acquisition of ESBL-E carriage, contributing to dissemination in the community. Strict hygiene measures as well as careful balancing of benefit-risk ratio of current antibiotic policies need to be reevaluated

The Anticancer Peptide TAT-RasGAP317-326 Exerts Broad Antimicrobial Activity.

Antibiotic resistance has become a major health issue. Nosocomial infections and the prevalence of resistant pathogenic bacterial strains are rising steadily. Therefore, there is an urgent need to develop new classes of antibiotics effective on multi-resistant nosocomial pathogenic bacteria. We have previously shown that a cell-permeable peptide derived from the p120 Ras GTPase-activating protein (RasGAP), called TAT-RasGAP317-326, induces cancer cell death, inhibits metastatic progression, and sensitizes tumor cells to various anti-cancer treatments in vitro and in vivo. We here report that TAT-RasGAP317-326 also possesses antimicrobial activity. In vitro, TAT-RasGAP317-326, but not mutated or truncated forms of the peptide, efficiently killed a series of bacteria including Escherichia coli, Acinetobacter baumannii, Staphylococcus aureus, and Pseudomonas aeruginosa. In vivo experiments revealed that TAT-RasGAP317-326 protects mice from lethal E. coli-induced peritonitis if administrated locally at the onset of infection. However, the protective effect was lost when treatment was delayed, likely due to rapid clearance and inadequate biodistribution of the peptide. Peptide modifications might overcome these shortcomings to increase the in vivo efficacy of the compound in the context of the currently limited antimicrobial options

A new Manifestation of Atomic Parity Violation in Cesium: a Chiral Optical Gain induced by linearly polarized 6S-7S Excitation

We have detected, by using stimulated emission, an Atomic Parity Violation (APV) in the form of a chiral optical gain of a cesium vapor on the 7S - 6P$_{3/2}$ transition,consecutive to linearly polarized 6S-7S excitation. We demonstrate the validity of this detection method of APV, by presenting a 9% accurate measurement of expected sign and magnitude. We underline several advantages of this entirely new approach in which the cylindrical symmetry of the set-up can be fully exploited. Future measurements at the percent level will provide an important cross-check of an existing more precise result obtained by a different method.Comment: 4 pages, 2 figure

Developing, delivering and documenting rehabilitation in a multi-centre randomised controlled surgical trial: experiences from the ProFHER trial

OBJECTIVES: A rigorous approach to developing, delivering and documenting rehabilitation within randomised controlled trials of surgical interventions is required to underpin the generation of reliable and usable evidence. This article describes the key processes used to ensure provision of good quality and comparable rehabilitation to all participants of a multi-centre randomised controlled trial comparing surgery with conservative treatment of proximal humeral fractures in adults. METHODS: These processes included the development of a patient information leaflet on self-care during sling immobilisation, the development of a basic treatment physiotherapy protocol that received input and endorsement by specialist physiotherapists providing patient care, and establishing an expectation for the provision of home exercises. Specially designed forms were also developed to facilitate reliable reporting of the physiotherapy care that patients received. RESULTS: All three initiatives were successfully implemented, alongside the measures to optimise the documentation of physiotherapy. Thus, all participating sites that recruited patients provided the sling immobilisation leaflet, all adhered to the physiotherapy protocol and all provided home exercises. There was exemplary completion of the physiotherapy forms that often reflected a complex patient care pathway. These data demonstrated equal and high access to and implementation of physiotherapy between groups, including the performance of home exercises. CONCLUSION: In order to increase the validity and relevance of the evidence from trials of surgical interventions and meet international reporting standards, careful attention to study design, conduct and reporting of the intrinsic rehabilitation components is required. The involvement of rehabilitation specialists is crucial to achieving this. Cite this article: Bone Joint Res 2014;3:335–40

The Relationship of Left Ventricular Trabeculation to Ventricular Function and Structure Over a 9.5-Year Follow-Up The MESA Study

Left ventricular (LV) trabeculation is highly variable among individuals and is increased in some diseases (e.g., congenital heart disease or cardiomyopathies), but its significance in population-representative individuals is unknown