Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach

Baillie, Richard T.; Morana, Claudio

doi:10.1016/j.jedc.2009.02.009

Cited by 191 publications

(116 citation statements)

References 52 publications

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“…6 Baillie and Morana (2007) introduce a new long-memory volatility process, denoted by Adaptive FIGARCH which is designed to account for both long-memory and structural change in the conditional variance process. One could provide an enrichment of the bivariate dual long-memory model by allowing the intercepts of the two means and variances to follow a slowly varying function as in Baillie and Morana (2007). This is undoubtedly a challenging yet worthwhile task.…”

Section: Dual Long-memorymentioning

confidence: 99%

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

Karanasos

Kartsaklas

2009

Journal of Empirical Finance

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Section: Dual Long-memorymentioning

confidence: 99%

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

Karanasos

Kartsaklas

2009

Journal of Empirical Finance

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“…Besides all of this, some authors demonstrated the relationship of long memory and structural breaks in time series: Micosch and Starica [39], Diebold and Inoue [40], Balcilar [41] and Smith [28]. In one of these studies, Baillie and Morana [42] introduced their adaptive-FIGARCH model that is quite robust against structural breaks. In this model, the authors examined the long memory features in conditional changing variance by considering structural breaks, which is accomplished by letting the constant term follow a slowly changing function.…”

Section: Literature Reviewsmentioning

confidence: 99%

Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Günay

2016

IJFS

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Abstract:In this study, the performance of the Multifractal Model of Asset Returns (MMAR) was examined for stock index returns of four emerging markets. The MMAR, which takes into account stylized facts of financial time series, such as long memory, fat tails and trading time, was developed as an alternative to the ARCH family models. Empirical analysis of the study consists of two sections. In the first section, we estimated the parameters of GARCH, EGARCH, FIGARCH, MRS-GARCH and MMAR for the stock index returns of Croatia, Greece, Poland and Turkey. In the second section, 1000 paths were obtained for each model using Monte Carlo simulations. We then compared the scaling function values of simulated and original time series for different q orders (1-5). According to the obtained results, the MMAR is mostly superior to other models and presents the best replica of the original time series. Another important finding is the achievement of the MRS-GARCH. We found that for lower levels of persistency (long memory) of return series, the performance of the MRS-GARCH excels, and for H = 0.5, it narrowly outperforms the MMAR.

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“…The FIGARCH model extends the conditional variance equation of the standard GARCH model by adding fractional differences in order to allow for long memory property of the GARCH volatility process (Baillie et al 1996;Baillie and Morana 2009). Following Baillie et al (2000), we implement an ARMA(1,1)-FIGARCH(1,d,1) model given bỹ r t;n ¼ l þ cr t;nÀ1 þ e t;n þ he t;nÀ1 ; e t;n jX t;nÀ1 $ D v ð0; h t;n Þ…”

Section: Figarch Modelmentioning

confidence: 99%

Volatility forecasting in the Chinese commodity futures market with intraday data

Jiang

Ahmed

Liu

2016

Rev Quant Finan Acc

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Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.

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Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach

Cited by 191 publications

References 52 publications

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

Dual long-memory, structural breaks and the link between turnover and the range-based volatility

Performance of the Multifractal Model of Asset Returns (MMAR): Evidence from Emerging Stock Markets

Volatility forecasting in the Chinese commodity futures market with intraday data

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