Outliers in GARCH Processes

Hotta, Luiz Koodi; Tsay, Ruey S.

doi:10.1201/b11823-20

Cited by 28 publications

(16 citation statements)

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“…For instance we refer the tests proposed by Tsay (1986Tsay ( , 1988, Chang et al (1988) and Chen and Liu (1993b) based on ARMA models, while the recent literature focuses on detection and correction of outliers within the GARCH framework proposed by Franses and Ghijsels (1999), Charles and Darné (2005), Doornik and Ooms (2005), Zhang and King (2005), Bali and Guirguis (2007), Ané et al (2008), Grané and Veiga(2010) and Hotta and Tsay (2012). Along these lines, this study focuses on the detection and correction of outliers to emphasize the existence of large shocks that may affect the stock markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Breaks and outliers when modelling the volatility of the U.S. stock market

Chatzikonstanti

2017

Applied Economics

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This study analyses volatility persistence of the US stock market, after taking into account the role of breaks and outliers. By employing a wavelet-based algorithm, it identifies several outliers which are comfortably associated with major events such as the 'Black Monday' and the Asian crisis. There is also evidence of clustering of breaks and a substantial variation in the properties of the identified segments.

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Section: Literature Reviewmentioning

confidence: 99%

Breaks and outliers when modelling the volatility of the U.S. stock market

Chatzikonstanti

2017

Applied Economics

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“…1 This specification corresponds to Level Outlier defined by Hotta and Tsay (1998) who also define Volatility Outliers.…”

Section: Effects Of Outliers On ML Volatility Estimatesmentioning

confidence: 99%

Estimating GARCH volatility in the presence of outliers

2012

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“…In the process of detecting additive outliers, it is important to distinguish between level outliers and volatility outliers. The additive level outliers affect the level of the series and the additive volatility outliers affect the future conditional variances, meaning that outliers in the series affect underlying conditional variances (see Sakata and White, 1998;Hotta and Tsay, 1998 …”

Section: Outlier Detectionmentioning

confidence: 99%

The role of outliers and oil price shocks on volatility of metal prices

Behmiri

Manera

2015

Resources Policy

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SummaryThis study investigates the price volatility of metals, using the GARCH and GJR models. First we examine the persistence of volatility and the leverage effect across metal markets taking into account the presence of outliers, and second we estimate the effects of oil price shocks on the price volatility of metals, allowing for the asymmetric responses. We use daily spot prices for the selected metals, including aluminum, copper, lead, nickel, tin, zinc, gold, silver, palladium and platinum.The main findings indicate that, returns show a high degree of volatility persistence before and after correcting outliers, outliers bias the parameters estimation of the GARCH-type models, and removing outliers improves the performance of models in capturing volatility. However in a comparison, Student-t distribution outperforms the approach of correcting outliers in capturing volatility. Moreover, we find the existence of inverse leverage effect for seven metals, the leverage effect for copper and no leverage effect for nickel and palladium. Finally, price volatility of metals differently reacts to oil price shocks and there is an asymmetric reaction of volatility to oil price shocks for seven metals.Keywords: Metals, Commodities, Volatility, Oil Price, Outliers. JEL Classification: G13, Q4, C1Acknowledgments: We thank participants to the FEEM seminars. Moreover, the authors acknowledge financial support from the "Energy: Resources and Markets" research program of Fondazione Eni Enrico Mattei (FEEM).

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Outliers in GARCH Processes

Cited by 28 publications

References 8 publications

Breaks and outliers when modelling the volatility of the U.S. stock market

Breaks and outliers when modelling the volatility of the U.S. stock market

Estimating GARCH volatility in the presence of outliers

The role of outliers and oil price shocks on volatility of metal prices

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