http://hdl.handle.net/10016/12 Tue, 21 May 2013 07:27:40 GMT 2013-05-21T07:27:40Z http://hdl.handle.net/10016/16969 Title: Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain Author(s): Pino, Gabriel; Tena, Juan de Dios; Espasa, Antoni [espasa] Abstract: We study the performance of different modelling strategies for 969 and 600 monthly price indexes disaggregated by sectors and geographical areas in Spain, regions, and in the EA12, countries, in order to obtain a detailed picture of inflation and relative sectoral prices through geographical areas for each economy, using the forecasts from those models. The study also provides a description of the spatial cointegration restrictions which could be useful for understanding price setting within an economy. We use spatial bi-dimensional vector equilibrium correction models, where the price indexes for each sector are allowed to be cointegrated with prices in neighbouring areas using different definitions of neighbourhood. We find that geographical disaggregation forecasts are very reliable on a regional level in Spain as they improve the forecasting accuracy of headline inflation relative to alternative methods. Geographical disaggregation forecasts are also reliable for the EA12 but only because derived headline inflation forecasting is not significantly worse than alternative forecasts. These results show that regional analysis within countries is appropriate in the euro area. These highly disaggregated forecasts can be used for competitive and other type of macro and regional analysis Tue, 30 Apr 2013 22:00:00 GMT http://hdl.handle.net/10016/16969 2013-04-30T22:00:00Z http://hdl.handle.net/10016/16967 Title: A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection Author(s): Virbickaite, Audrone; Ausín, Concepción; Galeano, Pedro Abstract: We use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model to estimate the time-varying volatilities of financial returns. The ADCC-GJR-GARCH model takes into consideration the asymmetries in individual assets volatilities, as well as in the correlations. The errors are modeled using a flexible location-scale mixture of infinite Gaussian distributions and the inference and estimation is carried out by relying on Bayesian non-parametrics. Finally, we carry out a simulation study to illustrate the flexibility of the new method and present a financial application using Apple and NASDAQ Industrial index data to solve a portfolio allocation problem Tue, 30 Apr 2013 22:00:00 GMT http://hdl.handle.net/10016/16967 2013-04-30T22:00:00Z http://hdl.handle.net/10016/16966 Title: One for all : nesting asymmetric stochastic volatility models Author(s): Mao, Xiuping; Ruiz, Esther [ortega]; Veiga, Helena Abstract: This paper proposes a new stochastic volatility model to represent the dynamic evolution of conditionally heteroscedastic series with leverage effect. Although there are already several models proposed in the literature with the same purpose, our main justification for a further new model is that it nests some of the most popular stochastic volatility specifications usually implemented to real time series of financial returns. We derive closed-form expressions of its statistical properties and, consequently, of those of the nested specifications. Some of these properties were previously unknown in the literature although the restricted models are often fitted by empirical researchers. By comparing the properties of the restricted models, we are able to establish the advantages and limitations of each of them. Finally, we analyze the performance of a MCMC estimator of the parameters and volatilities of the new proposed model and show that it has appropriate finite sample properties. Furthermore, estimating the new model using the MCMC estimator, one can correctly identify the restricted specifications. All the results are illustrated by estimating the parameters and volatilities of simulated time series and of a series of daily S&P500 returns Tue, 30 Apr 2013 22:00:00 GMT http://hdl.handle.net/10016/16966 2013-04-30T22:00:00Z http://hdl.handle.net/10016/16965 Title: Bayesian multivariate Bernstein polynomial density estimation Author(s): Zhao, Yanyun; Ausín, Concepción; Wiper, Michael P. Abstract: This paper introduces a new approach to Bayesian nonparametric inference for densities on the hypercube, based on the use of a multivariate Bernstein polynomial prior. Posterior convergence rates under the proposed prior are obtained. Furthermore, a novel sampling scheme, based on the use of slice sampling techniques, is proposed for estimation of the posterior predictive density. The approach is illustrated with both simulated and real data examples Fri, 31 May 2013 22:00:00 GMT http://hdl.handle.net/10016/16965 2013-05-31T22:00:00Z