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

Financial Factors, Macroeconomic Information and the Expectations Theory of the Term Structure of Interest Rates

In this paper we concentrate on the hypothesis that the empirical rejections of the Expectations Theory(ET) of the term structure of interest rates can be caused by improper modelling of expectations. Our starting point is an interesting anomaly found by Campbell-Shiller(1987), when by taking a VAR approach they abandon limited information approach to test the ET, in which realized returns are taken as a proxy for expected returns. We use financial factors and macroeconomic information to construct a test of the theory based on simulating investors' effort to use the model in `real time' to forecast future monetary policy rates. Our findings suggest that the importance of fluctuations of risk premia in explaining the deviation from the ET is reduced when some forecasting model for short-term rates is adopted and a proper evaluation of uncertainty associated to policy rates forecast is consideredExpectations Theory, Macroeconomic information in Finance

Breast cancer and communication: Monocentric experience of a self-assessment questionnaire

Background: The communication of the diagnosis of breast cancer induces to the patient a strong psychological trauma. Radiologists are at the forefront of communicating, either for a biopsy or the probable diagnosis of malignancy. This is a complex task, which requires the knowledge and application of correct “communicative models”, among which the SPIKES protocol rep-resents a fundamental reference. Design and methods: 110 patients, with a history of breast cancer, filled out a questionnaire consisting of six questions: five aimed at defining communication compliance with the SPIKES protocol, the sixth, consisting of six feelings, aimed at the knowledge of the next emotional state. Results: Regarding compliance with various “strategic points” of the SPIKES protocol, questionnaires show that 70% of patients reported no omissions, while the remaining 30% reported omissions relatively to perception (56%), emotions (23%), setting (13%), knowledge (6%) and invitation (2%). The results showed the existence of a correlation between the final emotional state and the correct application of the SPIKES protocol; in fact, patients who reacted with a positive final emotional state-reported greater adherence to the strategic points of the SPIKES protocol. Conclusions: In healthcare, knowing the communicative compliance of a team in giving “bad news” is fundamental, especially in breast cancer. The SPIKES protocol is recognized by the Literature as a fundamental reference able to affect “positively” the emotional state of patients. The proposed questionnaire is a valid tool to identify the weak points of communication and related criticalities, to improve clinical practice

Studying Axon-Astrocyte Functional Interactions by 3D Two-Photon Ca²⁺ Imaging: A Practical Guide to Experiments and "Big Data" Analysis.

Recent advances in fast volumetric imaging have enabled rapid generation of large amounts of multi-dimensional functional data. While many computer frameworks exist for data storage and analysis of the multi-gigabyte Ca <sup>2+</sup> imaging experiments in neurons, they are less useful for analyzing Ca <sup>2+</sup> dynamics in astrocytes, where transients do not follow a predictable spatio-temporal distribution pattern. In this manuscript, we provide a detailed protocol and commentary for recording and analyzing three-dimensional (3D) Ca <sup>2+</sup> transients through time in GCaMP6f-expressing astrocytes of adult brain slices in response to axonal stimulation, using our recently developed tools to perform interactive exploration, filtering, and time-correlation analysis of the transients. In addition to the protocol, we release our in-house software tools and discuss parameters pertinent to conducting axonal stimulation/response experiments across various brain regions and conditions. Our software tools are available from the Volterra Lab webpage at https://wwwfbm.unil.ch/dnf/group/glia-an-active-synaptic-partner/member/volterra-andrea-volterra in the form of software plugins for Image J (NIH)-a de facto standard in scientific image analysis. Three programs are available: <i>MultiROI_TZ_profiler</i> for interactive graphing of several movable ROIs simultaneously, <i>Gaussian_Filter5D</i> for Gaussian filtering in several dimensions, and <i>Correlation_Calculator</i> for computing various cross-correlation parameters on voxel collections through time

The Impact of Uncertainty Shocks under Measurement Error: A Proxy SVAR approach

A growing literature considers the impact of uncertainty using SVAR models that include proxies for uncertainty shocks as endogenous variables. In this paper we consider the impact of measurement error in these proxies on the estimated impulse responses. We show via a Monte-Carlo experiment that measurement error can result in attenuation bias in impulse responses. In contrast, the proxy SVAR that uses the uncertainty shock proxy as an instrument does not su¤er from this bias. Applying this latter method to the Bloom (2009) data-set results in impulse responses to uncertainty shocks that are larger in magnitude and more persistent than those obtained from a recursive SVAR

The use of XFEM to assess the influence of intra-cortical porosity on crack propagation

This study aimed at using eXtended finite element method (XFEM) to characterize crack growth through bone's intra-cortical pores. Two techniques were compared using Abaqus: (1) void material properties were assigned to pores; (2) multiple enrichment regions with independent crack-growth possibilities were employed. Both were applied to 2D models of transverse images of mouse bone with differing porous structures. Results revealed that assigning multiple enrichment regions allows for multiple cracks to be initiated progressively, which cannot be captured when the voids are filled. Therefore, filling pores with one enrichment region in the model will not create realistic fracture patterns in Abaqus-XFEM

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