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I know that if the order of Arch(m) is over 3, we should use GARCH and GARCH(1,1) was proved to be the best. But was GARCH(1,1) proved to be available for any country's stock market? My result show that GARCH(1,1) is not statistically significant. However, the Garch(2,1) (3,1) (4,1) (5,1) (6,1) (7,1) (8,1) are statistically significant. Consequently, i ...


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I have many materials about GARCH model (Applied Time series econometrics,page198 ; Econometrics by example- Damodar Gujarati p.238; Introductory econometrics for finance - Chris Brooks p.379) to figure out the Order of Garch(m,s). ‚Äč -All indicate that if the order of ARCH is over 3, use GARCH. And as the order of ARCH increases to infinity, ARCH(m) is ...


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You can plot $\vert x_t \vert$ or $x^2$ just to see whether your data presents volatility clusters (periods in which volatility is high and other periods in which it is low). When you fit a GARCH model, you fit it simply on the $x_t$ series, not on the $\vert x_t \vert$ or $x^2$ series (as previously said in the comments): the likelihood algorithm will do ...


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In this context, unconditional variance refers to the stationary variance level predicted by your GARCH model. This quantity need not coincide with the sample variance of the data on which the latter model has been calibrated. That being said, in an effort to reduce the complexity of the GARCH parameters' estimation process (nasty non-linear optimisation ...


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Jacob, you conclude that "The main finding is that VaR is more suited for our index portfolio GSPC than for our stock JPM." This conclusion is not surprising. However I think that is not the VaR in itself "more suited," but the underlying GARCH(1,1) model. You introduce the standardized error in section 4.6. That is the right way. I did not study your pdf ...


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The reason is earnings and other idiosyncratic corporate actions like takeovers, major product releases, etc. There are three terms in garch(1,1), the constant, term proportional to previous day's volatility, and a term proportional to "stock noise". Earnings jump is much larger than previous "regular" volatility, and also much larger than "regular" noise. ...



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