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Hi all I am looking for an add-in. I am currently working on something and I use daily data of closing stock prices. As not all companies are traded daily (e.g. on monday, then on thursday etc) at the stock exchange, there is satistically a problem. There are some papers which explain the approach to handle infrequent trading of a stock or non synchronous data and beta estimation (Dimson, 1979;

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hi I have a problem on how to proceed with further steps in my analysis. I did a linear OLS regression (ri,t=alpha*beta*rm,t+et) with my daily data of stock and index returns. There is now the problem of arch in my error terms. Thus I used the following r command: /garch(resid_desn, order=c(0,2)) ## This ARCH(2) process seems to fit the best after trial and error. Consequently, I get there

Hi all ARCH modelling I have a problem now on how to proceed with further steps in my analysis. I did a linear OLS regression with my daily data of stock and index returns. There is now the problem of arch in my error terms. Thus I used the following r command: garch(resid_desn, order=c(0,2)) ## This ARCH(2) process seems to fit the best after trial and error. Consequently, I get there three