Results 171 to 180 of about 2,269 (199)

Asymmetric volatility in asset prices: An explanation with mental framing. [PDF]

Heliyon
Ormos M, Timotity D.
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Pricing of Weather Options for Berlin Quoted on the Chicago Mercantile Exchange [PDF]

Juliusz Pres, Piotr Fiszeder
core  

Volatility Dynamics of the Tokyo Stock Exchange: A Sectoral Analysis based on the Multivariate GARCH Approach [PDF]

Ka Cheng Tsui, Kin-Yip Ho
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Modelling Financial Processes with Long Memory in Mean and Variance [PDF]

Piotr Fiszeder
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Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach [PDF]

Journal of Economic Dynamics and Control, 2009
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Richard T. Baillie, Claudio Morana
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Modeling volatility with time-varying FIGARCH models

Economic Modelling, 2011
Abstract This paper puts the light on a new class of time-varying FIGARCH or TV-FIGARCH processes to model the volatility. This new model has the feature to account for the long memory and the structural change in the conditional variance process. The structural change is modeled by a logistic function allowing the intercept to vary over time.
Mustapha Belkhouja, Mohamed Boutahary
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A novel time-varying FIGARCH model for improving volatility predictions

Physica A: Statistical Mechanics and its Applications, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Xuehui, Zhu, Hongli, Zhang, Xinru, Zhao, Lutao   +3 more
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Volatility persistence in metal returns: A FIGARCH approach

Journal of Economics and Business, 2012
Abstract This study examines the returns and the long-memory properties of the return volatilities of four metals – copper, gold, platinum, and silver. Daily returns for the January 4, 1999 to March 10, 2009 period are used. Three key issues are addressed: (1) whether the volatility processes exhibit long-run temporal dependence; (2) whether the ...
Steven J. Cochran, Iqbal Mansur, Babatunde Odusami   +2 more
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Great Salt Lake Surface Level Forecasting Using FIGARCH Model

Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C, 2007
In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity ...
Qianru Li, Christophe Tricaud, Rongtao Sun, YangQuan Chen   +3 more
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Modeling and predicting stock returns using the ARFIMA-FIGARCH

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
Modeling of real world financial time series such as stock returns are very difficult, because of their inherent characteristics. ARIMA and GARCH models are frequently used in such cases. It is proven of late that, the traditional models may not produce the best results. Lot of recent literature says the successes of hybrid models.
P. Bagavathi Sivakumar, V. P. Mohandas
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