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7

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

This book goes through exactly this problem in quite detail (with C++ codes included). I've worked through it in the past, but can't sum it up off the top of my head.


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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 ...


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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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We use KFilter. here is a link to their documentation page for you to peruse. If you share a bit more about how you want to use the filter then it may help us. However please note that suggest me a library questions are typically not on topic on any of the stack exchange sites To update the answer to include the function the user wanted.... Here is the ...


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Q1- for AR(1) only one 1 lag, ie burn in, should be sufficient. However, you could do 50 to feel comfortable. Q2- Matching the theoretical one is not a possibility Q3. (update) AIC/BIC tests on the simulated series can help select the best one. You can get the logL values from KF or estimate functions in Matlab.


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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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