similar to: High Frequency Time Series

R-guRus , ARMA function in tseries, seems to be calculating the AR coeff 's as coef <- lm(xx[,1]~xx[,lag$ar+1])$coef [*snipped* from around line 77,] I'd like to modify this model with another term somewhat in these lines lm(xx[,1] ~xx[,lag$ar+1]+mvgsignal)$coef where mvgsignal is a moving average signal based on some indicators, the question is could i simply hack into

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

I hope that someone can help me with the following question: I would like to solve the Markowitz optimization problem WITH short-sale constraints. Maybe a procedure to solve a quadratic optimization problem with convex constraints and positive variables is already implemented in R? Thank you very much, edg -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

Anyone know if there's an R package somewhere that supports estimation of a linear regression model with GARCH error process? There's a garch command in the tseries package, but unless I'm missing something it is restricted to the univariate case, i.e. you can fit a GARCH model to a single time-series but not estimate a model with GARCH errors. -- Allin Cottrell Department of

Hi,I try to calcualte AIC or Loglik to GARCH model,But the Packege Tseries do not deal with them.How can I calculate AIC or Loglike to GARCH Model By Packege Tseries? Thanks. ____________________________________________________ Free Internet Access NOW! In Alexandria, Ismaileya, Suez, Portsaid, Hurgadha, Sharm Banha, Shebin El-Kom, Damietta, Tanta, Zagazig, Mansoura, Damanhour, Assyout, Qena

Dear all, Can garch function fit also t-innovations or only Gaussian innovations? -- With kind regards -- Lepo pozdravljeni -- Gr??e (Gr?ezi) -- Gorazd Brumen ------------------------------- Mail 1: gbrumen at student.ethz.ch Mail 2: gorazd.brumen at fmf.uni-lj.si Tel.: +41 (0)1 63 34906 Homepage: valjhun.fmf.uni-lj.si/~brumen

Hi there, Does anyone know if R has a function for testing whether a time series is stationary?? Thanks in advance, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James's Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St James's

I would like to know if there is a way to test no correlaction in time series ? cov(r_t, r_t-1)=0 And r_t are homoscedastik and independent. Thanks [[alternative HTML version deleted]]

Hello all, I would like how to modelized a time serie with AR-ARCH process. It can be used arma and garch functions in tseries package for build ar process or a garch process, but how can it be modelized a ar-garch model ? Thanks [[alternative HTML version deleted]]

Hi R specialists, The na.omit.ts() method fails when the time series contains internal NA's. How can these automatically be removed? > spectrum(ts.mNDII, na.action=na.omit) Error in na.omit.ts(as.ts(x)) : time series contains internal NAs How can the na.action be activated correctly? > acf(ts.Lin, type=c("correlation"), na.action=na.omit) Error in na.omit.ts(as.ts(x)) :

Dear R People: Is there a library for Multivariate time series, please? For some reason, I'm thinking that Dr. Paul Gilbert may have one? R Version 1.6.2 (i've updated!) for Windows Thanks so much! Sincerely, Erin Hodgess University of Houston - Downtown mailto: hodgess at uhddx01.dt.uh.edu

Follow the example in tseries, we can simulated a GARCH(0,2), n <- 1100 a <- c(0.1, 0.5, 0.2) # ARCH(2) coefficients e <- rnorm(n) x <- double(n) x[1:2] <- rnorm(2, sd = sqrt(a[1]/(1.0-a[2]-a[3]))) for(i in 3:n) # Generate ARCH(2) process { x[i] <- e[i]*sqrt(a[1]+a[2]*x[i-1]^2+a[3]*x[i-2]^2) } x <- ts(x[101:1100]) and x is a GARCH(0,2). But, I would like to know how

Dear all, Is there a test in R for the randomness of a sequence of observations (e.g. to test the random number generator)? Specifically I am looking for autocorrelations which are not necessarily linear in nature, which the acf function does not seem to be flexible enough to detect as it tests for linear autocorrelation. Thanks in advance, Paul.

Dear R People: Here is an interesting question(I think) Suppose I want to calculate Centered Moving Averages; i.e. x[1] <- ( sum(y[1:12]) )/12 x[2] <- ( sum(y[2:13]) )/12 and so on. Of course, this is easily done through loops. However, I have been trying to do this more elegantly, but have failed. I have tried things like j1 <- 1:109 j2 <- 12:120 x[1:109] <- ( sum( y[j1:j2])

Hi there, Does anyone know how to predict ARMA? It doesn?t have either predict or forecast methods. I found couple of packages called fbasic and fseries at http://www.itp.phys.ethz.ch/econophysics/R/, which has ?arma.predict? in it, but it doesn?t seem to be working. Any help in this regard would be appreciated. Thanks in advance. Regards Skanda Kallur "Prediction is very difficult,

I make quite a lot of use of irregular time-series, and had already spent a bit of time writing an 'its' class when the 'irts' class was released via the package 'tseries'. I have experimented with the 'irts' class, and have some practical issues with its use. In some applications of irregular time-series (in my case these are financial and econometric) there are

Can anyone please recommend a good site for crime related time series? Thanks! Erin mailto: hodgess at uhddx01.dt.uh.edu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

Dear R People: Here is yet another histogram question (yahq), please: When I use the hist() command with freq=F, I get density on the side. I would really like to have relative frequency; that is, Rf = density/(sum(density)) Is there a step that I'm leaving out, please? thanks! Sincerely, Erin hodgess at uhddx01.dt.uh.edu