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

Interactions Screenings Unearth Potential New Divisome Components in the Chlamydia-Related Bacterium, Waddlia chondrophila.

Chlamydiales order members are obligate intracellular bacteria, dividing by binary fission. However, Chlamydiales lack the otherwise conserved homologue of the bacterial division organizer FtsZ and certain division protein homologues. FtsZ might be functionally replaced in Chlamydiales by the actin homologue MreB. RodZ, the membrane anchor of MreB, localizes early at the division septum. In order to better characterize the organization of the chlamydial divisome, we performed co-immunoprecipitations and yeast-two hybrid assays to study the interactome of RodZ, using Waddlia chondrophila, a potentially pathogenic Chlamydia-related bacterium, as a model organism. Three potential interactors were further investigated: SecA, FtsH, and SufD. The gene and protein expression profiles of these three genes were measured and are comparable with recently described division proteins. Moreover, SecA, FtsH, and SufD all showed a peripheral localization, consistent with putative inner membrane localization and interaction with RodZ. Notably, heterologous overexpression of the abovementioned proteins could not complement E. coli mutants, indicating that these proteins might play different functions in these two bacteria or that important regulators are not conserved. Altogether, this study brings new insights to the composition of the chlamydial divisome and points to links between protein secretion, degradation, iron homeostasis, and chlamydial division

Disassembly of a Medial Transenvelope Structure by Antibiotics during Intracellular Division.

Chlamydiales possess a minimal but functional peptidoglycan precursor biosynthetic and remodeling pathway involved in the assembly of the division septum by an atypical cytokinetic machine and cryptic or modified peptidoglycan-like structure (PGLS). How this reduced cytokinetic machine collectively coordinates the invagination of the envelope has not yet been explored in Chlamydiales. In other Gram-negative bacteria, peptidoglycan provides anchor points that connect the outer membrane to the peptidoglycan during constriction using the Pal-Tol complex. Purifying PGLS and associated proteins from the chlamydial pathogen Waddlia chondrophila, we unearthed the Pal protein as a peptidoglycan-binding protein that localizes to the chlamydial division septum along with other components of the Pal-Tol complex. Together, our PGLS characterization and peptidoglycan-binding assays support the notion that diaminopimelic acid is an important determinant recruiting Pal to the division plane to coordinate the invagination of all envelope layers with the conserved Pal-Tol complex, even during osmotically protected intracellular growth

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

Are Rapid Population Estimates Accurate? A Field Trial of Two Different Assessment Methods.

Emergencies resulting in large-scale displacement often lead to populations resettling in areas where basic health services and sanitation are unavailable. To plan relief-related activities quickly, rapid population size estimates are needed. The currently recommended Quadrat method estimates total population by extrapolating the average population size living in square blocks of known area to the total site surface. An alternative approach, the T-Square, provides a population estimate based on analysis of the spatial distribution of housing units taken throughout a site. We field tested both methods and validated the results against a census in Esturro Bairro, Beira, Mozambique. Compared to the census (population: 9,479), the T-Square yielded a better population estimate (9,523) than the Quadrat method (7,681; 95% confidence interval: 6,160-9,201), but was more difficult for field survey teams to implement. Although applicable only to similar sites, several general conclusions can be drawn for emergency planning

Diverse Stress-Inducing Treatments cause Distinct Aberrant Body Morphologies in the Chlamydia-Related Bacterium, Waddlia chondrophila.

Chlamydiae, such as Chlamydia trachomatis and Chlamydia pneumoniae, can cause chronic infections. It is believed that persistent forms called aberrant bodies (ABs) might be involved in this process. AB formation seems to be a common trait of all members of the Chlamydiales order and is caused by distinct stress stimuli, such as β-lactam antibiotics or nutrient starvation. While the diverse stimuli inducing ABs are well described, no comprehensive morphological characterization has been performed in Chlamydiales up to now. We thus infected mammalian cells with the Chlamydia-related bacterium Waddlia chondrophila and induced AB formation using different stimuli. Their morphology, differences in DNA content and in gene expression were assessed by immunofluorescence, quantitative PCR, and reverse transcription PCR, respectively. All stimuli induced AB formation. Interestingly, we show here for the first time that the DNA gyrase inhibitor novobiocin also caused appearance of ABs. Two distinct patterns of ABs could be defined, according to their morphology and number: (i) small and multiple ABs versus (ii) large and rare ABs. DNA replication of W. chondrophila was generally not affected by the different treatments. Finally, no correlation could be observed between specific types of ABs and expression patterns of mreB and rodZ genes

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

FtsZ-independent septal recruitment and function of cell wall remodelling enzymes in chlamydial pathogens.

The nature and assembly of the chlamydial division septum is poorly defined due to the paucity of a detectable peptidoglycan (PG)-based cell wall, the inhibition of constriction by penicillin and the presence of coding sequences for cell wall precursor and remodelling enzymes in the reduced chlamydial (pan-)genome. Here we show that the chlamydial amidase (AmiA) is active and remodels PG in Escherichia coli. Moreover, forward genetics using an E. coli amidase mutant as entry point reveals that the chlamydial LysM-domain protein NlpD is active in an E. coli reporter strain for PG endopeptidase activity (ΔnlpI). Immunolocalization unveils NlpD as the first septal (cell-wall-binding) protein in Chlamydiae and we show that its septal sequestration depends on prior cell wall synthesis. Since AmiA assembles into peripheral clusters, trimming of a PG-like polymer or precursors occurs throughout the chlamydial envelope, while NlpD targets PG-like peptide crosslinks at the chlamydial septum during constriction

Cedratvirus lausannensis - digging into Pithoviridae diversity.

Amoeba-infecting viruses have raised scientists' interest due to their novel particle morphologies, their large genome size and their genomic content challenging previously established dogma. We report here the discovery and the characterization of Cedratvirus lausannensis, a novel member of the Megavirales, with a 0.75-1 µm long amphora-shaped particle closed by two striped plugs. Among numerous host cell types tested, the virus replicates only in Acanthamoeba castellanii leading to host cell lysis within 24 h. C. lausannensis was resistant to ethanol, hydrogen peroxide and heating treatments. Like 30 000-year-old Pithovirus sibericum, C. lausannensis enters by phagocytosis, releases its genetic content by fusion of the internal membrane with the inclusion membrane and replicates in intracytoplasmic viral factories. The genome encodes 643 proteins that confirmed the grouping of C. lausannensis with Cedratvirus A11 as phylogenetically distant members of the family Pithoviridae. The 575,161 bp AT-rich genome is essentially devoid of the numerous repeats harbored by Pithovirus, suggesting that these non-coding repetitions might be due to a selfish element rather than particular characteristics of the Pithoviridae family. The discovery of C. lausannensis confirms the contemporary worldwide distribution of Pithoviridae members and the characterization of its genome paves the way to better understand their evolution

Cell wall precursors are required to organize the chlamydial division septum.

Members of the Chlamydiales order are major bacterial pathogens that divide at mid-cell, without a sequence homologue of the FtsZ cytokinetic tubulin and without a classical peptidoglycan cell wall. Moreover, the spatiotemporal mechanisms directing constriction in Chlamydia are not known. Here we show that the MreB actin homologue and its conserved regulator RodZ localize to the division furrow in Waddlia chondrophila, a member of the Chlamydiales order implicated in human miscarriage. RodZ is recruited to the septal site earlier than MreB and in a manner that depends on biosynthesis of the peptidoglycan precursor lipid II by the MurA enzyme. By contrast, crosslinking of lipid II peptides by the Pbp3 transpeptidase disperses RodZ from the septum. Altogether, these findings provide a cytological framework for understanding chlamydial cytokinesis driven by septal cell wall synthesis