A multivariate generalized independent factor GARCH model with an application to financial stock returns

We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one

A Note on the Pseudo-Spectra and the Pseudo-Covariance Generating Functions of ARMA Processes

Although the spectral analysis of stationary stochastic processes has solid mathematical foundations, this is not the case for non-stationary stochastic processes. In this paper, the algebraic foundations of the spectral analysis of non-stationary ARMA processes are established. For this purpose the Fourier Transform is extended to the field of fractions of polynomials. Then, the Extended Fourier Transform pair pseudo-covariance generating function / pseudo-spectrum, analogous to the Fourier Transform pair covariance generating function / spectrum,is defined. The new transform pair is well defined for stationary and non-stationary ARMA processes. This new approach can be viewed as an extension of the classical spectral analysis. It is shown that the frequency domain has some additional algebraic advantages over the time domain.

A multivariate generalized independent factor GARCH model with an application to financial stock returns

We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one.ICA, Multivariate GARCH, Factor models, Forecasting volatility

Exploring ICA for time series decomposition

In this paper, we apply independent component analysis (ICA) for prediction and signal extraction in multivariate time series data. We compare the performance of three different ICA procedures, JADE, SOBI, and FOTBI that estimate the components exploiting either the non-Gaussianity, or the temporal structure of the data, or combining both, non-Gaussianity as well as temporal dependence. Some Monte Carlo simulation experiments are carried out to investigate the performance of these algorithms in order to extract components such as trend, cycle, and seasonal components. Moreover, we empirically test the performance of those three ICA procedures on capturing the dynamic relationships among the industrial production index (IPI) time series of four European countries. We also compare the accuracy of the IPI time series forecasts using a few JADE, SOBI, and FOTBI components, at different time horizons. According to the results, FOTBI seems to be a good starting point for automatic time series signal extraction procedures, and it also provides quite accurate forecasts for the IPIs.ICA, Signal extraction, Multivariate time series, Forecasting

Introduction to nonlinearities, business cycles, and forecasting

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Introduction to nonlinearities, business cycles, and forecasting.

Forecasting; Business cycles;

Do interrelated financial markets help in forecasting stock returns?

The interest in studying the interrelationships among financia! markets is c!ear, specially for banks and financial institutions. Nevertheless there are not conclusive studies on this respect. In this paper we analyze the predictive power of the obvious random walk model for stock prices when compared with other univariate and multivariate alternatives that exploit the presence of common stochastic trends in the data. We address several issues: First, can we find one (or more) common growth factors that help us in improving the forecast accuracy of the stock price indexes? And second, within the family of unobserved components models, is there any one particularly specification for the trend well suited for explaining and forecasting financial stock market data

Las negociaciones sobre el Brexit y Gibraltar. Perspectiva del Ministerio de Asuntos Exteriores, UE y Cooperación de España

La salida del Reino Unido de la Unión Europea, decidida en referéndum por el pueblo británico, ha arrastrado consigo fuera de la UE a la colonia de Gibraltar. Con el Brexit, el régimen privilegiado del que Gibraltar disfrutaba en la UE y que ha permitido la prosperidad económica de éste tocará a su fin. En 300 años de historia y de contencioso hispano-británico sobre Gibraltar el golpe más duro para el Peñón no ha venido de España sino del Reino Unido, la potencia administradora del territorio