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Estimation of flexible fuzzy GARCH models for conditional density estimation

Rui Jorge Almeida, Nalan Baştürk, Uzay Kaymak () and João Miguel Costa Sousa

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: In this work we introduce a new flexible fuzzy GARCH model for conditional density estimation. The model combines two different types of uncertainty, namely fuzziness or linguistic vagueness, and probabilistic uncertainty. The probabilistic uncertainty is modeled through a GARCH model while the fuzziness or linguistic vagueness is present in the antecedent and combination of the rule base system. The fuzzy GARCH model under study allows for a linguistic interpretation of the gradual changes in the output density, providing a simple understanding of the process. Such a system can capture different properties of data, such as fat tails, skewness and multimodality in one single model. This type of models can be useful in many fields such as macroeconomic analysis, quantitative finance and risk management. The relation to existing similar models is discussed, while the properties, interpretation and estimation of the proposed model are provided. The model performance is illustrated in simulated time series data exhibiting complex behavior and a real data application of volatility forecasting for the S&P 500 daily returns series.

Keywords: Linguistic descriptions; Volatility forecasting; Conditional density estimation; Fuzzy GARCH models (search for similar items in EconPapers)
JEL-codes: C14 C22 G32 (search for similar items in EconPapers)
Date: 2013-07-31
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (1)

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