1,484 research outputs found
Combination of multivariate volatility forecasts
This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite sample properties of the GMM estimator of the combination weights are investigated by Monte Carlo simulations. Finally, in order to give an appraisal of the economic implications of the combined volatility predictor, the results of an application to tactical asset allocation are presented.Multivariate GARCH, Forecast Combination, GMM, Portfolio Optimization
On nonlocally interacting metrics, and a simple proposal for cosmic acceleration
We propose a simple, nonlocal modification to general relativity (GR) on
large scales, which provides a model of late-time cosmic acceleration in the
absence of the cosmological constant and with the same number of free
parameters as in standard cosmology. The model is motivated by adding to the
gravity sector an extra spin-2 field interacting nonlocally with the physical
metric coupled to matter. The form of the nonlocal interaction is inspired by
the simplest form of the Deser-Woodard (DW) model, ,
with one of the Ricci scalars being replaced by a constant , and gravity
is therefore modified in the infrared by adding a simple term of the form
to the Einstein-Hilbert term. We study cosmic expansion
histories, and demonstrate that the new model can provide background expansions
consistent with observations if is of the order of the Hubble expansion
rate today, in contrast to the simple DW model with no viable cosmology. The
model is best fit by and . We also compare the
cosmology of the model to that of Maggiore and Mancarella (MM),
, and demonstrate that the viable cosmic histories
follow the standard-model evolution more closely compared to the MM model. We
further demonstrate that the proposed model possesses the same number of
physical degrees of freedom as in GR. Finally, we discuss the appearance of
ghosts in the local formulation of the model, and argue that they are
unphysical and harmless to the theory, keeping the physical degrees of freedom
healthy.Comment: 47 pages in JCAP style, 7 figures. Some discussions extended in
response to referee's comments. Version accepted for publication in JCA
The Usage of Credit Cards: An Empirical Analysis on Italian Households Panel Data
Credit cards, both as mean of payment and borrowing, rise many economic issues. The credit card services can be viewed as a two-sided network platform affected by indirect network externalities. The distribution of prices faced by the two sides influences market participation and the overall volume of demand. Consumers may hold or use credit cards from multiple networks leads to a ‘multi-homing’ effect that is of great importance in determining the outcome of the industry. Moreover, some studies show that the multiple credit cards can be seen as a device to access to more financing, making family bankruptcy more likely. In this paper we model the number of credit cards held by a panel of Italian household over the period 1991-2010 using demographic, socio-economic and geographical variables as potential predictors and panel data techniques for count data. The reached results can be of interest for implementing market strategies in credit card industry and, in particular, to investigate peculiar effect such as “multihoming” and “co-holding”. Keywords: Credit cards, Panel data, Count-data model
Corporate Governance, Investment, Profitability and Insolvency Risk: Evidence from Italy
The research aims to study the structural and functional characteristics of food and beverage companies, focusing on corporate governance, investment and financing decisions, innovation, profitability, and risk of insolvency. The analysis is based on a mixed type investigation method carried out on a random stratified sample of 274 firms.
The empirical findings reveal that a large prevalence of companies is owned by a single person or by a limited number of partners (often of the same family). Owners and their families centralize decision-making power. The prevalence of companies made investment in innovation. The investments are mainly financed (78%) by the self-financing or by shareholders' capital. The investigation of the causal relationships that link corporate risk, profitability, and the propensity to invest and innovate with the other explanatory variables of business management highlighted further significant aspects
Governance, Innovation, Profitability, and Credit Risk: Evidence from Italian manufacturing firms
The research focuses attention on companies in the chemical-plastic sector, investigating corporate governance,
investment and financing decisions, innovation, profitability, and credit risk. The investigation method used is
mixed. The data and information were extracted from official databases and from a structured questionnaire. The
stratified random method was used for sampling. 178 firms are included in the sample. The results show a
prevalence of companies owned by a single person or a limited number of shareholders (in some cases of the same
family), where the owners centralize decision-making power. Companies have a strong propensity to invest in
innovation. Investments are mainly financed by self-financing or equity capital. The analysis of the causal
relationships highlights further significant aspects relating to credit risk, profitability and innovation
Doubly multiplicative error models with long- and short-run components
We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating long-run, respectively, short-run features in the data. Three such models are considered, the SPLINE-MEM which fits a spline to the slow-moving pattern of volatility, the Component-MEM which uses daily data for both components, and the MEM-MIDAS which exploits the logic of MIxed-DAta Sampling (MIDAS) methods. The parameters are estimated by the Generalized Method of Moments (GMM), for which we establish the theoretical properties and the equivalence with the Quasi Maximum Likelihood (QML) estimator under a Gamma assumption. The empirical application involves the S&P 500, NASDAQ, FTSE 100, DAX, Nikkei and Hang Seng indices: irrespective of the market, the DMEM’s generally outperform the HAR and other relevant GARCH-type models
Concepts and tools for nonlinear time series modelling
Tools and approaches are provided for nonlinear time series
modelling in econometrics. A wide range of topics is covered,
including probabilistic properties, statistical inference
and computational methods. The focus is on the applications but
the ideas of the mathematical arguments are also provided.
Techniques and concepts are
illustrated by various examples, Monte Carlo experiments and a real application
Concepts and tools for nonlinear time series modelling
Tools and approaches are provided for nonlinear time series
modelling in econometrics. A wide range of topics is covered,
including probabilistic properties, statistical inference
and computational methods. The focus is on the applications but
the ideas of the mathematical arguments are also provided.
Techniques and concepts are
illustrated by various examples, Monte Carlo experiments and a real application
Authentication of Sorrento walnuts by NIR spectroscopy coupled with different chemometric classification strategie
Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics. The aim of the present study is to develop a non-destructive and shelf-life compatible method, capable of discriminating common walnuts from those harvested in Sorrento (a town in Southern Italy), considered a high quality product. Two-hundred-and-twenty-seven walnuts (105 from Sorrento and 132 grown in other areas) were analyzed by near-infrared spectroscopy (both whole or shelled), and classified by Partial Least Squares-Discriminant Analysis (PLS-DA). Eventually, two multi-block approaches have been exploited in order to combine the spectral information collected on the shell and on the kernel. One of these latter strategies provided the best results (98.3% of correct classification rate in external validation, corresponding to 1 misclassified object over 60). The present study suggests the proposed strategy is a suitable solution for the discrimination of Sorrento walnuts. © 2020 by the authors
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