# Tag Info

2

In general, if you have a model of relation between $y$ and $x$ whereby the relation is not perfect but measured with errors: $$y_t = f(x_t) + \varepsilon_t,$$ where errors $\varepsilon$ are assumed to be additive but need not be, you are free to choose the distribution of these errors to better fit the reality. That is where GARCH enters as a great ...

1

I traced the error. It is a C language routine implemented in R that appears to have been functionally obsolesced, so it is called by other routines, but I don't think it is still implemented as its own routine. Some information on it is at ftp://cran.r-project.org/pub/R/doc/manuals/r-devel/R-exts.html Given the underlying math, there is one of three ...

1

TLDR: The jump frequency depends on how you specify the jump size distribution. If you want the $\lambda$ to actually represent the jump frequency under a certain jump-diffusion model, then you should jointly estimate all model parameters, e.g. using maximum likelihood estimation (MLE) or generalized method of moments (GMM). Example: Consider a general ...

Only top voted, non community-wiki answers of a minimum length are eligible