Guillermo Ferreira, Jorge A. Muñoz-Mendoza, Jorge Arratia-Llancao, Jorge Mateu, Miguel Flores-Sánchez, Francisco J. Rodríguez-Cortés
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引用次数: 0
Abstract
We propose a state-space approach for a non-parametric tv-GARCH model. Using a number of non-parametric techniques, we estimate slowly time-varying curves for the parameters and combine it with a recursive system based on the Kalman filter as a methodological framework to obtain forecasts from the model. Our results based on Monte Carlo simulations and an application to the Selective Stock Price Index of the Chilean stock market indicate that the non-parametric tv-GARCH model shows the best fit and provides the best forecasting performance, compared with the stationary GARCH model and other techniques, such as tv-GARCH with time-varying parameters determined by deterministic functions. These results have relevant implications for risk management, portfolio diversification, and asset allocation.
期刊介绍：
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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