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Expanding on the answer by @ir7, here is some pykalman code/psuedocode to help get you started. This can be adjusted in many ways but I have left in some parameters to give you an idea. I left a documentation link at the bottom as well. The functions will setup Kalman Filters that are applied to your data and subsequently that data is fed to a regression ...


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One resource that has Kalman Filter and Smoother, and Expectation-Maximization algorithms for a Linear Gaussian Model is pykalman module. You can check out statsmodels module too.


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There is not one package that does of all this, but you can look at the QuantLib bindings for R and Python for the Quant stuff. All three languages have excellent plotting libraries. Information about this can be found all over the internet, i.e. on Google, GitHub and here. I don't believe there can be one blog that does a deep dive in all of these topics ...


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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 ...


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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 ...


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One huge problem with GARCH models is that sometimes extremely changes in parameter values can lead to absurdities such as conditional variance paths exploding or plummeting below zero. One way to quickly solve that problem is to force the conditional variance process to be bound within an interval. When you filter out the conditional variance process, you ...


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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 ...


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