Results 181 to 190 of about 13,792,513 (203)
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2001
This paper extends the FIGARCH long-memory volatility model to a multivariate framework. The proposed quasi maximum likelihood estimator for the parameters of the model is analyzed through Monte Carlo simulations and is found to perform satisfactorily.
Pafka, S, Mátyás, László
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This paper extends the FIGARCH long-memory volatility model to a multivariate framework. The proposed quasi maximum likelihood estimator for the parameters of the model is analyzed through Monte Carlo simulations and is found to perform satisfactorily.
Pafka, S, Mátyás, László
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Research on the Long-Term Memory of Commodity Housing Price Volatility Based on the FIGARCH Model
Advanced Materials Research, 2014The purpose of this paper is to test whether there exists a long-term memory volatility characteristics of housing price. The paper based on the data ranging of Zhengzhou from January 2004 to May 2014, by adopting the FIGARCH model, empirically studies and analysis this characteristics.
Feng Lan, Bao Hua Chen
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The Reserve Bank of Australia Intervention: Exchange Rate Volatility from FIGARCH Modelling
2003In this paper, we investigate the effect of the Reserve Bank of Australia on the $US/$A volatility in the period 1983-1995, which can be broken into four distinct phases. Equally, we investigate the changing effectiveness of daily intervention into various separate components. We test the existence of a long memory behaviour i.e.
Ahdi Noomen Ajmi +2 more
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Studies in Nonlinear Dynamics & Econometrics, 2007
This paper extends the Fractionally integrated GARCH (FIGARCH) model by incorporating Normal Inverse Gaussian Distribution (NIG). The proposed model is flexible and allows one to model time-variation, long memory, fat tails as well as asymmetry and skewness in the distribution of financial returns.
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This paper extends the Fractionally integrated GARCH (FIGARCH) model by incorporating Normal Inverse Gaussian Distribution (NIG). The proposed model is flexible and allows one to model time-variation, long memory, fat tails as well as asymmetry and skewness in the distribution of financial returns.
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Modeling Financial Volatility with Long Memory: An Empirical Comparison of GARCH and FIGARCH Models
This study examines the practical relevance of long-memory models in financial volatility by comparing GARCH(1,1) and FIGARCH(1, d, 1) specifications across major asset classes-equities (S&P 500, IBEX 35, Tesla), cryptocurrencies (Bitcoin), and emerging markets (BOVESPA).openaire +1 more source
Bivariate Error Correction FIGARCH and FIAPARCH Models on the Australian All Ordinaries Index and Its SPI Futures. [PDF]
, 2004 In this paper we extend the univariate FIGARCH and FIAPARCH models to a bivariate framework. We estimate bivariate error correction FIGARCH and FIAPARCH models between the All Ordinaries Index and its SPI futures using constant correlation and diagonal parameterisations.
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An Improved FIGARCH Model with The Difference Operator (1-Vl)D
2023Qunxing Pan, Peng Li, Xiuli Du
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Integrated ARCH, FIGARCH and AR models: Origins of long memory
2015Although the properties of the ARCH(∞) model are well investigated, the existence of long memory FIGARCH and IARCH solution was not established in the literature. These two popular ARCH type models which are widely used in applied literature, were causing theoretical controversy because of the suspicion that other solutions besides the trivial zero one,
Giraitis, Liudas +2 more
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Modeling high-frequency volatility with three-state FIGARCH models
Economic Modelling, 2015Yanlin Shi
exaly