similar to: Need R-help

Dear r users, Few days ago I posted the same topic but unable to receive any suggestion. So I am asking this same question. I was trying to fit a garch(1,1) model to my dataset. But while executing I got a warning message "NaNs produced in: sqrt(pred$e)". And got the estimated sd's along with five "NA", but as per my best knowledge I should get only one

Dear all R-users, I wanted to fit a Garch(1,1) model to a dataset by: >garch1 = garch(na.omit(dat)) But I got a warning message while executing, which is: >Warning message: >NaNs produced in: sqrt(pred$e) The garch parameters that I got are: > garch1 Call: garch(x = na.omit(dat)) Coefficient(s): a0 a1 b1 1.212e-04 1.001e+00 1.111e-14 Can any one

Dear list members, I am trying to estimate parameters of the AR(1)-GARCH(1,1) model. I have one additional dummy variable for the AR(1) part. First I wanted to do it using garchFit function (everything would be then estimated in one step) however in the fGarch library I didn't find a way to include an additional variable. That would be the formula but, as said, I think it is impossible to add

Hello, i want to estimate a complex GARCH-model (see below). http://r.789695.n4.nabble.com/file/n4112396/GJR_Garch.png W stands for the Day of the Week Dummies. r stands for returns of stock market indices. I stands for the GJR-term. I need some help with three problems: 1.) implementation of the GJR-term in the variance equation 2.) compute robust covariance matrix

Hello Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey Tsay's Analysis of Financial Time Series), I try to write an R program to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for Intel's stock returns. For some random reason, I cannot decipher what is wrong with my R program. The R package fGarch already gives me the answer, but my customized function

Hi, there, First of all, I am not familar with the GARCH concept as well as R interface. I have three questions regarding GARCH (1,1). First, I got tseries package from CRAN. Where exactly am I supposed to install it? In my case, I copies it to C:\Program Files\R\rw2001\library. What do I need to do in order to actually install it? Second, I am not sure if I've installed it properly, but

I used garchFit function to fit 1600 observations of EURO/USD 2-day returns in GARCH(1,1) model. As part of the summary I got warning message: NaNs produced in: sqrt(diag(fit$cvar)) And didn't get any estimates for 3 params' std.error, t value or probability: Error Analysis: Estimate Std. Error t value Pr(>|t|) mu -0.004827 0.020141 -0.240 0.811 ar1 0.010311

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

Hello R-list-members, I'm trying to model ARMA(0,2) & GARCH(1,1) process using the code below, but according to my textbook, the estimated parameters are wrong. The MA-parameters should be negative. (I've got the same problem using garchFit()). Can anyone tell me what I'm doing wrong? And how can I calculate the hessian matrix? Many thanks, Desislava Kavrakova Code:

Hi, it would be nice if the VaR package could get expanded to work with more models. I wrote a function which calculates the VaR for a Garch(1,1) AR(2) model. Since I am fairly new to R I am sure it can be written much neater. If someone feels like having a go, here is what I have written library(fSeries) library(tseries) VaR.Garch <- function(data,stockId=1,p=0.01,dt=1) { r <-

Dear R guru, I am Saikat Sarkar working as a researcher of Economics in Tampere University, Finland. I am trying to estimate some Garch related tests with Bayesian analysis by R programme. I am not good in R but trying to survive. Anyway I have the coding but not working properly. I have tried to find the problem but failed. I am writing to all R gurus to help me out. Could you please look at

Hi all, I got the daily stock return data from 2005 - 2008, calculated from HF minute data. (Thanks to Jeff and Josh). Now, I set 05 - 07yr as the carlibration period for estimating the parameters of ARMA(1,1)-GARCH(1,1) model, aqnd leave 08 for backtesting. So I use the return data observations from 1:760 (yr 05-07) to estimate the volatility on 2nd-Jan-08 (the position 761), then use the

-Bugs and fixes reported to Diethelm Wuertz. -In the interim. To make the Ox functions part of the fSeries package work please follow the following steps. ------------------------------------------------- 1. Install R-project. 2. Install fSeries. 3. Download: http://www.core.ucl.ac.be/~laurent/G@RCH/site/xbdcons/garch42.zip (G@RCH package for Ox) 4. Download:

Hello, I have a rather complex problem... I will have to explain everything in detail because I cannot solve it by myself...i just ran out of ideas. So here is what I want to do: I take quotes of two indices - S&P500 and DJ. And my first aim is to estimate coefficients of the DCC-GARCH model for them. This is how I do it: library(tseries) p1 = get.hist.quote(instrument =

Dear All; Searching on the achieve, many questions on optim() have been asked, but I haven't seen the following. The question began with my original inquiry on "Optimization failed in fitting mixture 3-parameter Weibul l distribution using fitdistr()" which I posted on Jul. 28, Prof. Ripley kindly advised me to look into options of optim() for the answer. Following his advice and

Hi, I am trying to write the Realized GARCH model with order (1,1) The model can be describe bellow: r_t = sqrt( h_t) * z_t logh_t = w + b*logh_(t-1) + r*logx_(t-1) logx_t = c + q*logh_t + t1*z_t +t2*(z_t ^2 -1) + u_t and z follow N(0,1) , u follow N(0, sigma.u^2) But I'm troubled with the simulation check for my code. After I simulate data from the model and estimate the data, I

Hi, I have been using this site ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm) to help me with some ARIMA modelling in R. Unfortunately the methods mentioned do not appear to work with second order differencing; arima(*, 2, *). I have used some dummy data to illustrate my point. When I use the xreg=... method, the estimate of intercept is *way* off. This can be seen by the high s.e but I

Hey, I'm trying to implement a GARCH model with Johnson-Su innovations in order to simulate returns of financial asset. The model should look like this: r_t = alpha + 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

Hi I'm trying to fit a model in betareg and I'm getting errors, but have no idea what they mean or how to solve them. Does anyone have experience with this? > model <- betareg(ACT ~ ST*SoilT, data = actDL_F) Warning messages: 1: In sqrt(W) : NaNs produced 2: In sqrt(W) : NaNs produced 3: In sqrt(1 + phihat) : NaNs produced data summaries don't give any na's or problems I

Hi, we have a problem concerning the use and forecasting on the GARCH (1, 1) on financial data. We would like to make predictions on future returns in a loop which moves the window of estimation forward one unit for each iteration. Our code is as follows: > BJORN <- read.table("C:/Users/Osvald/Desktop/DATATILLUPPSATS.txt", + header=TRUE, sep="\t",