similar to: EMM: how to make forecast using EMM methods?

Dear All, I have loaded package(tseries), but when I run predict.garch(...) R tells me could not find function "predict.garch", however ?predict.garch shows me something. I am confused about this. How can I forecast garch volatility? I have tried: predict(...,n.ahead=...),give me fitted value predict(...,n),give me NA,NA

I am trying to forecast volatility 2 periods forward using a ARCH(1) model: predict(garch(fit2,order=c(0,1),n.ahead=2)) ***** ESTIMATION WITH ANALYTICAL GRADIENT ***** Error in qr(com.hess$hess, ...) : unused argument(s) (n.ahead ...) What did I do wrong? Thank you. Best regards, Peter Arnold, CFA President PRA Investment Counsel, Inc. 704-341-8193 www.prainvestment.com

Hi, I've fitted a GARCH(1,1) for the residuals of my time serie (X). X is an ARMA(1,1) process. Now I want to do a n-step forecast for X, knowing these processes. How can I do this? I know that there's a command: predict() for ARIMA processes and so on, but what about GARCH? I've got: arma=arima(x, order=c(1,0,1)) (...) garch11<-garch(residuals(x),order = c(1, 1))

Dear everyone, i`m a german economics student, writing my master´s thesis about "Multivariate Volatility Models". After having read about theoretical aspects of Multivariate GARCH ans Stochastic Volatility Models, I would like to compare DCC-GARCH and DC-SV with help of an empirical application. I figuered out that one has to use MCMC-simulation-methods for that. Some days ago I

Hi R-users Can the users let me know how to do MGARCH estimate (Bivariate GARCH) and volatility forecast for 2 variables in R. thanks and regards snvk

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

Dear R users, I'm currently trying to re-project a geotiff in another coordinate system. For instance, I have a tif image in UTM 19 zone which I would like to reproject into UTM 18. I was wondering if it was possible in R. Furthermore, I looked into 'rgdal' package, but I can't really find out if I'm doing the right thing. So far, here is what I'm doing: library(rgdal)

Dear R People: Is there a way to forecast with GARCH modeling as found in tseries, please? When I use the predict command, I get an output of length 100, regardless of what I put in the n.ahead steps. R Version 1.9.0 Thanks in advance. Sincerely, Laura mailto: lauraholt_983 at hotmail.com download! http://toolbar.msn.click-url.com/go/onm00200413ave/direct/01/

Hi everyone, I wonder if anyone knows the reason why the outputs of the same reprojection in r and arcgis are different?. The magnitude of the change can be up to 40 km in the poles. Basically, I have a database of points equally separated by one degree over the globe. In ARCGIS, I am projecting the data in GCS-WGS-1984 and then reprojected it to Berhmann to ensure equal area

hello, In my time series data, I was able to successfully fit its ARIMA model (Box-Jenkins) and its GARCH model and estimate their parameters. I was also able to forecast future values of the time series based on my fitted ARIMA model using the predict() function call. However, I'm not sure what is the correct function command to call in order to forecast future values of my time series

hello, i was trying to predict values for a garch, so i did: predict(fitgarch,n.ahead = 20) but this doesn't work. Someone can tell me how to get the 20 values ahead of a garch model. thanks in advance _________________________________________________________________ O Windows Live ajuda-o a manter-se em contacto com todos os seus amigos, num só local.

Hello R group, I'm using the tseries package to forecast variance and I would like to do the same with correlation. I can't find any way to do that with the function garch(). for example, I have a matrix of time series Date CACIndex SPXIndex DAXIndex NKYIndex 1 05/01/1998 3072.84 977.07 4384.81 14896.1 2 06/01/1998 3037.73 966.58 4352.63 14896.40 3

Instead of emm-dashes I get struck-out text surrounded with single hyphens. RedCloth 3.0.4: >> d = RedCloth.new "-- hyphens to the left of me, hyphens to the right, all should be emm dashes --" => "-- hyphens to the left of me, hyphens to the right, all should be emm dashes --" >> d.to_html => "<p><del>- hyphens to the left of me, hyphens

Hello, My problem is, that I have to build hundreds of GARCH models to obtain volatility forecasts. I would like to run a loop, that would build those forecasts for me. There is no problem, with writing only the results of the forecasts, but I'd like to have stored results of the models in some file, that I could check later, what are the models like, to be able to compare if I should use

Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

Hi all, Could anybody point me to some overview/survey papers about using particle filters and sequential monte carlo methods to estimate stochastic volatilities? I couldn't find any such articles giving a big-picture view of the literature. How do these estimation methods compare to EMM and other Bayesian methods for estimating stochastic volatilities? Also, I am looking for some

Saludos, estoy intentando usar la función reprojectHDF() ( http://r-gis.net/?q=ModisDownload). #### source('ModisDownload.R') library(raster) library(RCurl) path<-"~/R/MODIS/Data/Test" input<-list.files(path,"*.hdf",all.files=T,recursive=T,full.names=T) input outname<-paste(substr(input[1],40,45),".tif",sep='') outname

In the manual page for prcomp(), it says that sdev is "the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)." ?However, this is not what I'm finding. ?The values appear to be the standard deviations of a reprojection of

Hi, i am working in the forecast of the daily price crude . The last prices of this data are the following: 100.60 101.47 100.20 100.06 98.68 101.28 101.05 102.13 101.70 98.27 101.00 100.50 100.03 102.23 102.68 103.32 102.67 102.23 102.14 101.25 101.11 99.90 98.53 96.76 96.12 96.54 96.30 95.92 95.92 93.45 93.71 96.42 93.99 93.76 95.24 95.63 95.95 95.83 95.65

Hi all, Does anybody have the source code of stochastic volatility models in R or Matlab, for example, the Bayesian based or the simulation based SV estimations as described by Prof Eric Zivot in the following discussion? https://stat.ethz.ch/pipermail/r-sig-finance/2005q4/000501.html -------------- I am wondering what is the current status of estimating stochastic vol models and what's