Bayesian Nonparametric Calibration and Combination of Predictive Distributions

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author

A Bayesian multi-factor model of instability in prices and quantities of risk in U.S. financial markets

This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach advocated by Fama and MacBeth (1973). The second approach extends previous work by Ouysse and Kohn (2010) and is based on a Bayesian approach to modelling the latent process followed by risk exposures and idiosynchratic volatility. Our application to monthly, 1979-2008 U.S. data for stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window modelling of time-varying betas yields results that are unreasonable. There is evidence that all the portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results as most portfolios do not appear to have been misspriced and a few risk premia are precisely estimated with a plausibile sign. Real consumption growth risk turns out to be the only factor that is persistently priced throughout the sample.Econometric models ; Stochastic analysis ; Financial markets

L’analyse comparative d’interactions médiatiques dans une perspective interculturelle. L’exemple de l’emploi des formes d’adresse en français et en italien

Cette contribution propose les résultats d’une recherche contrastive réalisée au sein d’une équipe pluriculturelle constituée dans le cadre du laboratoire ICAR (CNRS-Université Lyon 2). L’article présente d’abord les principes théoriques et méthodologiques de l’approche comparative interculturelle et se focalise ensuite sur l’emploi et le rôle des formes nominales d’adresse (« FNA », C. Kerbrat-Orecchioni 2010) dans des interactions radiophoniques françaises et italiennes. La comparaison porte d’une part sur la forme et la fréquence des procédés allocutifs, d’autre part sur leurs fonctions dans la gestion de l’interaction, l’expression de la valeur illocutoire des énoncés et la construction de la relation interpersonnelle. Les résultats de l’étude semblent ouvrir la voie à des hypothèses interprétatives fondées sur l’articulation entre les phénomènes discursifs envisagés et la dimension culturelle

Measuring sovereign contagion in Europe

This paper analyzes sovereign risk shift-contagion, i.e. positive and significant changes in the propagation mechanisms, using bond yield spreads for the major eurozone countries. By emphasizing the use oftwo econometric approaches based on quantile regressions (standard quantile regression and Bayesianquantile regression with heteroskedasticity) we find that the propagation of shocks in euro\u2019s bond yieldspreads shows almost no presence of shift-contagion in the sample periods considered (2003\u20132006,Nov. 2008\u2013Nov. 2011, Dec. 2011\u2013Apr. 2013). Shock transmission is no different on days with big spreadchanges and small changes. This is the case even though a significant number of the countries in our sample have been extremely affected by their sovereign debt and fiscal situations. The risk spillover amongthese countries is not affected by the size or sign of the shock, implying that so far contagion has remainedsubdued. However, the US crisis does generate a change in the intensity of the propagation of shocks inthe eurozone between the 2003\u20132006 pre-crisis period and the Nov. 2008\u2013Nov. 2011 post-Lehman one,but the coefficients actually go down, not up! All the increases in correlation we have witnessed overthe last years come from larger shocks and the heteroskedasticity in the data, not from similar shockspropagated with higher intensity across Europe. These surprising, but robust, results emerge becausethis is the first paper, to our knowledge, in which a Bayesian quantile regression approach allowing forheteroskedasticity is used to measure contagion. This methodology is particularly well-suited to dealwith nonlinear and unstable transmission mechanisms especially when asymmetric responses to signand size are suspected

Evaluating real-time forecasts in real-time

The accuracy of real-time forecasts of macroeconomicvariables that are subject to revisions may crucially depend on thechoice of data used to compare the forecasts against. We put forwarda flexible time-varying parameter regression framework to obtainearly estimates of the final value of macroeconomic variables basedupon the initial data release that may be used as actuals in currentforecast evaluation. We allow for structural changes in theregression parameters to accommodate benchmark revisions anddefinitional changes, which fundamentally change the statisticalproperties of the variable of interest, including the relationshipbetween the final value and the initial release. The usefulness ofour approach is demonstrated through an empirical applicationcomparing the accuracy of forecasts of US GDP growth rates from theSurvey of Professional Forecasters and the Greenbook.forecast evaluation;Bayesian estimation;structural breaks;data revision;parameter uncertainty

Predictive gains from forecast combinations using time-varying model weights

Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated, characterized by low predictability, structural instability, and fat tails, which is typical for many financial-economic time series. Sensitivity of results with respect to misspecification of the number of included predictors and the number of included models is explored. Given the set up of our experiments, time varying model weight schemes outperform other averaging schemes in terms of predictive gains both when the correlation among individual forecasts is low and the underlying data generating process is subject to structural locations shifts. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs.Bayesian model averaging;forecast combination;stock return predictability;time-varying weight combination

Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information

We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging.Term structure of interest rates; Nelson-Siegel model; Affine term structure model; forecast combination; Bayesian analysis

Amphiphile Adsorption on Rigid Polyelectrolytes

A theory is presented which quantitatively accounts for the cooperative adsorption of cationic surfactants to anionic polyelectrolytes. For high salt concentration we find that the critical adsorption concentration (CAC) is a bilinear function of the polyion monomer and salt concentrations, with the coefficients dependent only on the type of surfactant used. The results presented in the paper might be useful for designing more efficient gene delivery systems

A New monthly indicator of global real economic activity

In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian’s index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and predictability properties of the indicators. World steel production is proven to be the best monthly indicator of global economic activity in terms of our econometric properties. Kilian’s index of global real economic activity also accurately predicts World GDP growth rates. When extending the analysis to an out-ofsample exercise, both Kilian’s index of global real economic activity and the World steel production produce accurate forecasts for World GDP, confirming evidence provided by the econometric properties. Specifically, a forecast combination of the three indices produces statistically significant gains up to 40% at nowcast and more than 10% at longer horizons relative to an autoregressive benchmark