ARCH modeling in finance

Bollerslev, Tim; Chou, Ray Y.; Kroner, Kenneth F.

doi:10.1016/0304-4076(92)90064-x

Cited by 3,400 publications

(514 citation statements)

References 171 publications

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“…GARCH (1,1) is also the preferred model within the class of GARCH(P, Q) models, as the likelihood ratios of GARCH(1,1) versus GARCH(2,1) and GARCH(1,2) are 1.12 and 0.00, respectively, for the pound, 0.92 and 0.00 for the mark, and 1.90 and 0.00 for the yen, which are all insignificant. This is in accordance with Bollerslev et al (1992), who state that in most applications P = Q = 1 is sufficient.…”

Section: Single-regime Garchsupporting

confidence: 92%

“…To capture this, many authors use autoregressive conditional heteroskedasticity (ARCH) models, as introduced by Engle (1982) and extended to generalized ARCH (GARCH) in Bollerslev (1986); see Bollerslev, Chou and Kroner (1992) for an overview of the GARCH literature. Such models usually improve the fit a lot compared with a constant variance model and, as Andersen and Bollerslev (1998) claim, GARCH models provide good volatility forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…The use of a t-distribution instead of a normal one is quite popular in the standard, single-regime GARCH literature (see Bollerslev, Chou and Kroner (1992)). For regimeswitching models, a t-distribution can be extra useful.…”

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confidence: 99%

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Improving GARCH volatility forecasts with regime-switching GARCH

Klaassen

2002

Advances in Markov-Switching Models

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Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with different volatility levels; GARCH effects are allowed within each regime. The resulting Markov regime-switching GARCH model improves on existing variants, for instance by making multi-periodahead volatility forecasting a convenient recursive procedure. The empirical analysis demonstrates that the model resolves the problem with the high single-regime GARCH forecasts and that it yields significantly better outof-sample volatility forecasts.Key words: GARCH, Markov-switching, variance, forecasting, exchange rates. JEL classification: C52, C53, F31. * Department of Economics, University of Amsterdam, Roetersstraat 11, 1018 WB Amsterdam, the Netherlands; tel: +31-20-5254191; fax: +31-20-5254254; E-mail: klaassen@fee.uva.nl. I thank Harry Huizinga, Frank de Jong, Michael McAleer, Bertrand Melenberg, Theo Nijman, Arthur van Soest, Kenneth West, the editors of this issue, James Hamilton and Baldev Raj, and two referees for very useful and constructive comments.

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Section: Single-regime Garchsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Improving GARCH volatility forecasts with regime-switching GARCH

Klaassen

2002

Advances in Markov-Switching Models

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“…A large body of this literature has been devoted to the univariate models; see [7] and [13]. While the economic integration of industries within a market or across international markets becomes obvious, the relations between the volatilities and co-volatilities of different stock indices or markets have become the centre of attention.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

Chowdhury¹,

Arefin²

2017

JFE

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This paper tries to investigate the financial shock transmission dynamics using daily return data under different sectors traded in Dhaka Stock Exchange (DSE). Bayesian VAR model was used as conditional mean in GJR-GARCH, scalar-diagonal VECH and BEKK GARCH models to test return and volatility spillover effects. Lagged squared residuals and lagged conditional variances were used as variance regressors in conditional variance of GJR-GARCH to test the spillover effects. Finding reveals a highly significant memory effect on all sector returns except one. Asymmetric versions of GARCH, VECH and BEKK identified significant effect of news about volatility, past memory and differential effect of bad news on conditional volatility of almost all series. GJR-GARCH results identified pharmaceutical sector spillover free yet all other pairs were found to be unidirectional or in some cases bidirectional. Co-volatility among almost all sectors is also observed from VECH and BEKK output.

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“…But as Shiller (1989, p. 346-8) Fama (1965) and Black (1976) Aiyagari-Gertler (1991) Weil (1989) and Aiyagari-Gertler. See, e.g., Bollerslev et al (1992), Engle et al (1990) and Ghysels et al (1996) ' 'Cochrane (1991'Cochrane ( , 1996 presents a production-based asset pricing model, which demonstrates that the empirical implications stemming from the production side can be richer and fit the data better than the predictions coming out of standard consumption-based models. These models are quite different from ours in that they are not based on liquidity shortages, but instead assume complete markets.…”

Section: Introductionmentioning

confidence: 99%