search for: sstdfit

...$est[2]), -.15, .15, add = TRUE, col=2) t_fit <- stdFit(logr) curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add = TRUE, col=6) snorm_fit <- snormFit(logr) curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]), -.25, .15, add = TRUE, col=4) st_fit <- sstdFit(logr) curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3], st_fit$est[4]), -.25, .15, add = TRUE, col=3) library(fSeries) #normal innovation garch_norm <- garchFit(series = logr, include.mean = F) #t inovation garch_t <- garchFit(series = logr, cond.dist = 'dstd', include.me...

...f the R language itself; I've been searching for a good couple of hours but couldn't find the answers. Hope you can help me out. >From the descriptive statistics of my series, I had determined that my GARCH error term should follow a student's t distribution, preferrably skewed; the sstdFit function returns nu=2.002 and xi=1.012. I know that for this distribution 2 degrees of freedom means the second, third and fourth moment are not defined; however, the QQ plot looks good -had to use the skwt package though, because rsstd didn't work- so I decided to give it a try. 1) That didn&...

...$est[2]), -.15, .15, add = TRUE, col=2) t_fit <- stdFit(logr) curve(dstd(x, t_fit$est[1], t_fit$est[2], t_fit$est[3]), -.15, .15, add = TRUE, col=6) snorm_fit <- snormFit(logr) curve(dsnorm(x, snorm_fit$est[1], snorm_fit$est[2], snorm_fit$est[3]), -.25, .15, add = TRUE, col=4) st_fit <- sstdFit(logr) curve(dsstd(x, st_fit$est[1], st_fit$est[2], st_fit$est[3], st_fit$est[4]), -.25, .15, add = TRUE, col=3) library(fSeries) #normal innovation garch_norm <- garchFit(series = logr, include.mean = F) #t inovation garch_t <- garchFit(series = logr, cond.dist = 'dstd', include.me...