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Hi I want to implement Egarch (1,1) with t distribution model using RExcel and VBA. May I know the syntax. Following is the code that I 'm using. rinterface.RRun "spec=ugarchspec(variance.model=list(model=(eGARCH),garchOrder=c(1,1)), mean.model=list(armaOrder=c(1,1), arfima=FALSE), distribution.model=(std))" rinterface.RRun "fit = ugarchfit(Data = b, spec = spec)" rinterface.RRun "output=sigma(fit)" Please let me know the error and it's solution. Thanks Dheeraj [[alternative HTML ver...

Hi I am using the ugarchspec function from the rgarch package to fit a mean variance model jointly. Following is the code I'm using: > spec = ugarchspec(variance.model = list(model="eGARCH", garchOrder=c(1,1)), mean.model = list(armaOrder=c(1,1))) On doing this, I get the following error: Error in ugarchspec(variance.model = list(model = "eGARCH", garchOrder = c(1, : could not find function ".DistributionBounds" Any help on why this is happening? any way to solve this?...

Let me start with the code: library(quantmod) library(rugarch) getSymbols("SPY", from="1900-01-01") rets=na.trim(diff(log(Cl(SPY)))) tt = tail(rets["/2004-10-29"], 1000) spec = ugarchspec(variance.model=list(garchOrder=c(1,1)), mean.model=list(armaOrder=c(2,5)), distribution.model="sged") for(ii in 1:10) { ttFit = ugarchfit( spec=spec, data=as.vector(tt), out.sample=0, solver.control=list(trace=F) ) ttFore = ugarchforecast( ttFit, n.ahead=1, n.roll=0 ) print( as.array(ttFore)[,2,] ) } Produc...

Hi I was trying to implement Egarch (1,1) with Student t distribution using rugarch. But I was not getting any value. Following were the commands that I was using: library(rugarch) spec=ugarchspec(variance.model=list(model="eGARCH", garchOrder=c(1,1)), mean.model=list(armaOrder=c(1,1), arfima=FALSE), distribution.model="std") fit=ugarchfit(data=b,spec=spec) sigma(fit) May I know the error that I'm making in implementing the model? Any help with the syntax/commands or any useful content will be appreciated? Dheeraj [[a...

...pha + lambda*sqrt(h_t) + sqrt(h_t)*epsilon_t h_t = alpha0 + alpha1*epsilon_(t-1)^2 + beta1 * h_(t-1). Alpha refers to a risk-free return, lambda to the risk-premium. I've implemented it like this: #specification of the model spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1), submodel = NULL, external.regressors = NULL, variance.targeting = FALSE), mean.model = list( armaOrder = c(0,0), include.mean = TRUE, archm = TRUE, archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE), distribution.model = "jsu", start.pars = list(), fixed....

My code: spec<-ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1), submodel = "Null", external.regressors = NULL, variance.targeting = FALSE), mean.model = list(armaOrder=c(0,0),include.mean =FALSE, archm = FALSE, archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE), distribution.model = "norm", start.pars = lis...

...882 3 2017-05-16 17:55:00 -0.572 0.0000 4 2017-05-16 17:56:00 -0.736 1254.2695 5 2017-05-16 17:57:00 0.000 0.0000 6 2017-05-16 17:58:00 0.000 0.0000 > garch11.spec = ugarchspec(mean.model = list(armaOrder = c(1,1)), variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model = "norm") > dcc.garch11.spec = dccspec(uspec = multispec( replicate(2, garch11.spec) ), dccOrder = c(1,1), distribution = "mvnorm") > fit.a = dccfit(dcc....

...882 3 2017-05-16 17:55:00 -0.572 0.0000 4 2017-05-16 17:56:00 -0.736 1254.2695 5 2017-05-16 17:57:00 0.000 0.0000 6 2017-05-16 17:58:00 0.000 0.0000 > garch11.spec = ugarchspec(mean.model = list(armaOrder = c(1,1)), variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model = "norm") > dcc.garch11.spec = dccspec(uspec = multispec( replicate(2, garch11.spec) ), dccOrder = c(1,1), distribution = "mvnorm") > fit.a = dccfit(dcc....

...es what I intend to do. Therefore, I would like to have your helpful suggestion regarding the use of dccsim function in this purpose. The simulation code what I describe above is as follows: # first specify univariate egarch model uspec<-ugarchspec(variance.model=list(model="eGARCH",garchOrder=c(1,1)), distribution.model="norm",mean.model=list(armaOrder=c(0,0),include.mean=F)) model=multispec(replicate(2,uspec)) # replicate for two series # specify ADCC model mvspec=dccspec(model,dccOrder = c(1,1),model = "aDCC",distribution = "mvnorm") # cluster...