Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach
By Richard T. Baillie, Claudio Morana. International Computing Education Research Workshop, 2009
179 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/d45009a0015448ed9448e0b085558664c8c5fb2e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Duc Khuong Nguyen
- Rangan Gupta
- Shawkat Hammoudeh
- Amine Lahiani
- Mohamed Arouri
- Perry Sadorsky
- George Filis
- Cristina Amado
In Finance
- Duc Khuong Nguyen
- Cristina Amado
- Shawkat Hammoudeh
- Rangan Gupta
- Mohamed Arouri
- Amine Lahiani
- Timo Teräsvirta
- Yue‐Jun Zhang
Citations in the corpus
- Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model (2014, background): “…structural breaks), a body of research has suggested that both long memory and structural changes simultaneously characterize the structure of financial returns volatility (Lobato and Savin, 1998; Beine and Laurent, 2000; Morana and Beltratti, 2004; Martens et al., 2004; Baillie and Morana, 2009).”
- Modelling volatility by variance decomposition (2013, methodology): “This leads to the following de nition for gt: gt = rX l=1 0lGl(t ; l; cl): (8) It may be mentioned that Baillie and Morana (2009) recently proposed a GARCH model which also has a deterministically time-varying intercept.”
- Long memory and structural breaks in modeling the return and volatility dynamics of precious metals (2012, methodology): “Several attempts to discriminate between long memory and nonlinearity, we know of, include Van Dijk et al. (2002), Lahiani and Scaillet (2009), Baillie and Morana (2009), and Choi et al .”
- Modelling changes in the unconditional variance of long stock return series (2012, unknown): “Baillie and Morana (2009) recently proposed a generalization of the FIGARCH model in which the intercept changes deterministically according to the flexible functional form of Gallant (1984).”
- Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? (2019, methodology): “In order to forecast crude oil price volatility, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model proposed by Bollerslev (1986) is widely used due to its good performance in capturing the time-varying features of the data (Agnolucci, 2009; Hou & Suardi, 2012; Lux, Segnon, ”