 Real-Time GARCHEkaterina Smetanina Journal of Financial Econometrics, 2017, vol. 15, issue 4, 561-601 Abstract: Most GARCH-type models follow Engle’s (1982) original idea of modeling the volatility of asset returns as a function of only past information. We propose a new model, which retains the simple GARCH structure, but describes the volatility process as a mixture of past and current information. We show how the new model can be interpreted as the special case of a stochastic volatility (SV) model, which provides therefore a link between GARCH and SV models. We show that we are able to obtain better volatility forecasts than the standard GARCH-type models; improve the empirical fit to the data, especially in the tails of the distribution; and make the model faster in its adjustment to the new unconditional level of volatility. Further, we offer a much needed framework for specification testing as the new model nests the standard GARCH models. Keywords: forecasting; GARCH models; high-frequency data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:jfinec:v:15:y:2017:i:4:p:561-601. Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Financial Econometrics is currently edited by Allan Timmermann and Fabio Trojani More articles in Journal of Financial Econometrics from Oxford University Press Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC. |
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