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A simple google search should get your started: I like this one the best because it compares different packages: http://stat-www.berkeley.edu/~brill/Stat248/kalmanfiltering.pdf and here couple more: http://www.r-bloggers.com/the-kalman-filter-for-financial-time-series/ http://cran.r-project.org/web/packages/dlm/index.html ...


2

A great example of kalman filtering is in the Kyle Model. I have attached a presentation on the application of R to the kalman filter in the Kyle Model. http://www.rinfinance.com/RinFinance2009/presentations/microstructure-tutorial.pdf Basically in the Kyle Model, a market maker finds the likelihood an asset is ending up at a certain price given that a ...


2

Estimation of the initial states of R and particularly Q is indeed more of an art than science. The task at hand is to estimate the covariances. You have basically two main choices: Live with the fact that you will never be able to exactly pinpoint the covariance of noise in financial time series. The most often used approach is to pose the coveriance ...


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Usually $var(e_x), var(e_y)$ variances are calibrated by maximum likelihood from data similar as you want to calibrate your parameters $\theta$. Ratio $var(e_x)/var(e_y)$ tells you what are changes in your time-series $var(e_x)/var(e_y)$ is small: changes in time-series of observations are just noise and underlying state doesn't change much; ...


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This is definitely not a Kalman filter's issue: if you replace this line of code args <- eapply(env = env, FUN = function(x){ClCl(x)}) with this one args <- eapply(env = env, FUN = function(x){ClCl(x)})[Symbols] eapply() will keep the order of the original Yahoo query from quantmod. You can check and you will see each $\beta_{t}$ matches about ...



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