Forecasting Government Bond Yields with Large Bayesian VARs

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. Focusing on the U.S., we provide an extensive study on the forecasting performance of our proposed model relative to most of the existing alternative speci.cations. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also evaluate the performance of the alternative forecasts when used within trading schemes or as a basis for portfolio allocation. We extensively check the robustness of our results via subsample analysis and via a data based Monte Carlo simulation. We .nd that: i) our proposed BVAR approach produces forecasts systematically more accurate than the random walk forecasts, though the gains are small; ii) some models beat the BVAR for a few selected maturities and forecast horizons, but they perform much worse than the BVAR in the remaining cases; iii) predictive gains with respect to the random walk have decreased over time; iv) di¤erent loss functions (i.e., "statistical" vs "economic") lead to di¤erent ranking of speci.c models; v) modelling time variation in term premia is important and useful for forecasting.Bayesian methods, Forecasting, Term Structure.

Forecasting Exchange Rates with a Large Bayesian VAR

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange rates tend to co-move, the use of a large set of them can contain useful information for forecasting. In addition, we adopt a driftless random walk prior, so that cross-dynamics matter for forecasting only if there is strong evidence of them in the data. We produce forecasts for all the 33 exchange rates in the panel, and show that our model produces systematically better forecasts than a random walk for most of the countries, and at any forecast horizon, including at 1-step ahead.Exchange Rates, Forecasting, Bayesian VAR

Assessing international commonality in macroeconomic uncertainty and its effects

This paper uses a large vector autoregression to measure international macroeconomic uncertainty and its effects on major economies. We provide evidence of significant commonality in macroeconomic volatility, with one common factor driving strong comovement across economies and variables. We measure uncertainty and its effects with a large model in which the error volatilities feature a factor structure containing time‐varying global components and idiosyncratic components. Global uncertainty contemporaneously affects both the levels and volatilities of the included variables. Our new estimates of international macroeconomic uncertainty indicate that surprise increases in uncertainty reduce output and stock prices, adversely affect labor market conditions, and in some economies lead to an easing of monetary policy

No-arbitrage priors, drifting volatilities, and the term structure of interest rates

We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no-arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no-change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no-arbitrage affine term structure model's requirements that yields obey a factor structure and that the factors follow a Markov process. In the term structure model, its cross-equation no-arbitrage restrictions on the factor loadings appear to play a marginal role in forecasting gains

Taste intensity and hedonic responses to simple beverages in gastrointestinal cancer patients

Changes in the taste of food have been implicated as a potential cause of reduced dietary intake among cancer patients. However, data on intensity and hedonic responses to the four basic tastes in cancer are scanty and contradictory. The present study aimed at evaluating taste intensity and hedonic responses to simple beverages in 47 anorectic patients affected by gastrointestinal cancer and in 55 healthy subjects. Five suprathreshold concentrations of each of the four test substances (sucrose in black current drinks, citric acid in lemonade, NaCl in unsalted tomato juice, and urea in tonic water) were used. Patients were invited to express a judgment of intensity and pleasantness ranging from 0 to 10. Mean intensity scores directly correlated with concentrations of sour, salty, bitter, and sweet stimuli, in both normals and those with cancer. Intensity judgments were higher in cancer patients with respect to sweet (for median and high concentrations, P < 0.05), salty (for all concentrations, P < 0.05), and bitter tastes (for median concentration, P < 0.01). Hedonic function increased with the increase of the stimuli only for the sweet taste. A negative linear correlation was found between sour, bitter, and salty concentrations and hedonic score. Both in cancer patients and in healthy subjects, hedonic judgments increased with the increase of the stimulus for the sweet taste (r 1/4 0.978 and r 1/4 0.985, P 1/4 0.004 and P 1/4 0.002, respectively), and decreased for the salty (r 1/4 ??0.827 and r 1/4 ??0.884, P 1/4 0.084 and P 1/4 0.047, respectively) and bitter tastes (r 1/4 ??0.990 and r 1/4 ??0.962, P 1/4 0.009 and P 1/4 0.001, respectively). For the sour taste, the hedonic scores remained stable with the increase of the stimulus in noncancer controls (r 1/4 ??0.785, P 1/4 0.115) and decreased in cancer patients (r 1/4 ??0.996, P 1/4 0.0001). The hedonic scores for the sweet taste and the bitter taste were similar in cancer patients and healthy subjects, and these scores were significantly higher in cancer patients than in healthy subjects for most of the concentrations of the salty taste and all the concentrations of the sour taste. The present study suggests that cancer patients, compared to healthy individuals, have a normal sensitivity, a normal likingfor pleasant stimuli, and a decreased dislike for unpleasant stimuli. Moreover, when compared to controls, they show higher hedonic scores for middle and high concentrations of the salty taste and for all concentrations of the sour taste. Further studies are needed to evaluate whether these changes observed in cancer patients translate into any alteration in dietary behavior and/or food preferences

Rock mass characterization by UAV and close-range photogrammetry: A multiscale approach applied along the vallone dell’elva road (Italy)

Geostructural rock mass surveys and the collection of data related to discontinues provide the basis for the characterization of rock masses and the study of their stability conditions. This paper describes a multiscale approach that was carried out using both non-contact techniques and traditional support techniques to survey certain geometrical features of discontinuities, such as their orientation, spacing, and useful persistence. This information is useful in identifying the possible kinematics and stability conditions. These techniques are extremely useful in the case study of the Elva valley road (Northern Italy), in which instability phenomena are spread across 9 km in an overhanging rocky mass. A multiscale approach was applied, obtaining digital surface models (DSMs) at three different scales: large-scale DSM of the entire road, a medium-scale DSM to assess portions of the slope, and a small-scale DSM to assess single discontinuities. The georeferenced point cloud and consequent DSMs of the slopes were obtained using an unmanned aerial vehicle (UAV) and terrestrial photogrammetric technique, allowing topographic and rapid traditional geostructural surveys. This technique allowed us to take measurements along the entire road, obtaining geometrical data for the discontinuities that are statistically representative of the rock mass and useful in defining the possible kinematic mechanisms and volumes of potentially detachable blocks. The main purpose of this study was to analyse how the geostructural features of a rock mass can affect the stability slope conditions at different scales in order to identify road sectors susceptible to different potential failure mechanisms using only kinematic analysis