We use new fully functional methods to describe and study the dynamics of the short-term interest rate process in continuous-time. The suggested procedure exploits the spatial properties, embodied in the local time process, of the diffusion of interest, and is robust against deviations from stationarity. Our results indicate that the misspecification of a standard constant elasticity of variance model with linear mean-reverting drift cannot be attributed to the nonlinear behavior of the infinitesimal first moment of the short-term interest rate process at high rates. Rather, it should be attributed to the martingale nature of the process over most of its empirical range (i.e., between 3% and about 15%).

There are two variance components embedded in the returns constructed using high frequency asset prices: the time-varying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moments of high frequency return data recorded at different frequencies, we provide a simple and robust technique to identify both variance components. In the context of a volatility-timing trading strategy, we show that careful (optimal) separation of the two volatility components of the observed stock returns yields substantial utility gains.

We propose a functional estimation procedure for homogeneous stochastic differential equations based on a discrete sample of observations and with minimal requirements on the data generating process. We show how to identify the drift and diffusion function in situations where one or the other function is considered a nuisance parameter. The asymptotic behavior of the estimators is examined as the observation frequency increases and as the time span lengthens. We prove almost sure consistency and weak convergence to mixtures of normal laws, where the mixing variates depend on the chronological local time of the underlying diffusion process, that is the random time spent by the process in the vicinity of a generic spatial point. The estimation method and asymptotic results apply to both stationary and nonstationary recurrent processes.

The component of the volatility of total factor productivity (TFP) that is orthogonal to the dividend price ratio is shown to have long-run predictive ability for excess market returns. This finding implies that TFP volatility should also predict real cash flows and/or real interest rates: it is found to mainly predict real cash flows through inflation. A model with endogenous growth, Epstein-Zin preferences and price rigidities reconciles both TFP volatility-driven long-run predictability and its real implications. Within the model, we justify the similar (to that of TFP volatility) predictive ability of a low-frequency notion of market volatility as well as the cross-sectional pricing of TFP volatility risk in alternative asset classes.

The gCube System - AquaMaps Species View Portlet<br> --------------------------------------------------<br> <br> Species view explorer portlet for AquaMaps suite<br> <br> <br> This software is part of the gCube Framework (https://www.gcube-system.org/): an<br> open-source software toolkit used for building and operating Hybrid Data<br> Infrastructures enabling the dynamic deployment of Virtual Research Environments<br> by favouring the realisation of reuse oriented policies.<br> <br> The projects leading to this software have received funding from a series of <br> European Union programmes including: <br> * the Sixth Framework Programme for Research and Technological Development - <br> DILIGENT (grant no. 004260); <br> * the Seventh Framework Programme for research, technological development and <br> demonstration - D4Science (grant no. 212488), D4Science-II (grant no. <br> 239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine <br> (grant no. 283644); <br> * the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), <br> EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant <br> no. 654119), SoBigData (grant no. 654024);<br> <br> <br> Version<br> --------------------------------------------------<br> <br> 1.3.3-4.0.0-130288 (2016-11-27)<br> <br> Please see the file named "changelog.xml" in this directory for the release notes.<br> <br> <br> <br> Authors<br> --------------------------------------------------<br> <br> * Fabio Sinibaldi (fabio.sinibaldi-AT-isti.cnr.it) Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). <br> <br> Maintainers<br> -----------<br> <br> * Fabio Sinibaldi (fabio.sinibaldi-AT-isti.cnr.it) Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). <br> <br> <br> <br> Download information<br> --------------------------------------------------<br> <br> Source code is available from SVN: <br> http://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/user/aquamapsspeciesview<br> <br> Binaries can be downloaded from the gCube website: <br> https://www.gcube-system.org/<br> <br> Installation<br> --------------------------------------------------<br> <br> Installation documentation is available on-line in the gCube Wiki:<br> https://wiki.gcube-system.org/gcube/index.php/AquaMaps_Suite<br> <br> Documentation <br> --------------------------------------------------<br> <br> Documentation is available on-line in the gCube Wiki:<br> https://wiki.gcube-system.org/gcube/index.php/AquaMaps_Suite<br> https://wiki.gcube-system.org/gcube/index.php/AquaMaps_Suite<br> <br> <br> Support <br> --------------------------------------------------<br> <br> Bugs and support requests can be reported in the gCube issue tracking tool:<br> https://support.d4science.org/projects/gcube/<br> <br> <br> Licensing<br> --------------------------------------------------<br> <br> This software is licensed under the terms you may find in the file named "LICENSE" in this directory.<br>

We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed—without being forced—to vary across frequencies. Frequency-specific systematic risk is captured by a notion of spectral beta. We show that traditional factor models restrict the spectral betas to be constant across frequencies. The restriction can hide horizon-specific pricing effects that spectral factor models are designed to reveal. We illustrate how the methods may lead to economically meaningful dimensionality reduction in the factor space.