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A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting
"... Forecasting stock market returns volatility is a challenging task that has attracted the atten-tion of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both ..."
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Forecasting stock market returns volatility is a challenging task that has attracted the atten-tion of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of fuzzy inference systems and GJR-GARCH mod-eling approach in order to consider the principles of time-varying volatility, leverage effects and volatility clustering, in which changes are cataloged by similarity. Moreover, a differ-ential evolution (DE) algorithm is suggested to solve the problem of Fuzzy GJR-GARCH parameters estimation. The results indicate that the proposed method offers significant im-provements in volatility forecasting performance in comparison with GARCH-type models and with a current Fuzzy-GARCH model reported in the literature. Furthermore, the DE-based algorithm aims to achieve an optimal solution with a rapid convergence rate.
Forecasting Financial Assets Volatility Using Integrated GARCH-Type Models: International Evidence
"... Abstract In this article we compare the forecasting ability of two symmetric integrated GARCH models (FIGARCH & HYGARCH) with an asymmetric model (FIAPARCH) based on a skewed Student distribution. Each model is used for forecasting the daily conditional variance of 10 financial assets, for a sa ..."
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Abstract In this article we compare the forecasting ability of two symmetric integrated GARCH models (FIGARCH & HYGARCH) with an asymmetric model (FIAPARCH) based on a skewed Student distribution. Each model is used for forecasting the daily conditional variance of 10 financial assets, for a sample period of about 18 years. This exercise is done for seven stock indexes (Dow Jones, NASDAQ, S&P500, DAX30, FTSE100, CAC40 and Nikkei 225) and three exchange rates vis-a-vis the US dollar (the GBP-USD, YEN-USD and Euro-USD). Results indicate that the skewed Student AR (1) FIAPARCH (1.d.1) relatively outperforms the other models in outof-sample forecasts for one, five and fifteen day forecast horizons. Results indicate also, no difference for the AR (1) FIGARCH (1.d.1) and AR (1) HYGARCH (1.d.1) models since they have the same forecasting ability.
Particle Filter-Based On-Line Estimation of Spot Volatility with Nonlinear Market Microstructure Noise Models
, 2010
"... Summary. A new technique for the on-line estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. We make a clear distinction between volatility ..."
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Summary. A new technique for the on-line estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. We make a clear distinction between volatility per time unit and volatility per transaction and provide estimators for both. A new nonlinear market microstructure noise model is proposed that reproduces the major stylized facts of high-frequency data. A computationally efficient particle filter is used that allows for the approximation of the unknown efficient prices and, in combination with a recursive EM algorithm, for the estimation of the volatility curves. In addition, the estimators are improved by an on-line bias correction. We neither assume that the transaction times are equidistant nor do we use interpolated prices.
MODELLING DECISIONS UNDER UNCERTAINTY IN A BEHAVIOURAL QUEUING SYSTEM
"... adaptive expectations, uncertainty. In this paper we use an agent-based modelling and simulation approach to model a queuing system with autonomous customers who routinely choose a facility for service. We propose a Cellular Automata model to represent the customers ’ interactions and study how cust ..."
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adaptive expectations, uncertainty. In this paper we use an agent-based modelling and simulation approach to model a queuing system with autonomous customers who routinely choose a facility for service. We propose a Cellular Automata model to represent the customers ’ interactions and study how customers use their own experience and that of their neighbours in order to update their memory and decide what facility to join the next period. We use exponential smoothing to update the customers ’ expected sojourn time. We incorporate uncertainty regarding these expectations into the customers ’ decision. We compare the resulting behaviour when customers take into account uncertainty to the case where they ignore uncertainty at both the individual and the system level.
Declaration
, 2014
"... Barriers to, and policy opportunities for, the growth of renewable energy technologies in South Africa: Rethinking the role of municipalities by ..."
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Barriers to, and policy opportunities for, the growth of renewable energy technologies in South Africa: Rethinking the role of municipalities by
Non-profit academic project, developed under the open access initiative Computing Conditional VaR Using
"... How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System ..."
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How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System
