betategarch (version 2.0)

tegarch.fit: Compute the fitted values

Description

Computes the fitted values. Essentially a wrapper to the tegarch.recursion (one-component) and tegarch.recursion2 (two-component) functions.

Usage

tegarch.fit(y, result, lambda.initial = NULL)

Arguments

y

numeric vector, typically a financial return series.

result

a list with estimation result, for example as a result of result <- tegarch.est(y)

lambda.initial

NULL (default) or initial value(s) of the recursion for lambda. If NULL, then the values are chosen automatically.

Value

• A matrix with y and the fitted values of, amongst other, sigma, the log-scale (lambda), the conditional standard deviation (stdev), u, epsilon and the standardised residuals (residstd).

Details

Empty

References

Fernandez and Steel (1998), 'On Bayesian Modeling of Fat Tails and Skewness', Journal of the American Statistical Association 93, pp. 359-371. Harvey and Chakravarty (2008), 'Beta-t-(E)GARCH', Cambridge Working Papers in Economics 0840, Faculty of Economics, University of Cambridge. Nelson, Daniel B. (1991): 'Conditional Heteroskedasticity in Asset Returns: A New Approach', Econometrica 59, pp. 347-370. Harvey and Sucarrat (2012), ' EGARCH models with fat tails, skewness and leverage', Cambridge Working Papers in Economics 1236, Faculty of Economics, University of Cambridge.

See Also

tegarch.recursion, tegarch.recursion2

Examples

##simulate series with 500 observations:
set.seed(123)
y <- tegarch.sim(500, omega=0.01, phi1=0.9, kappa1=0.1, kappastar=0.05, df=10, skew=0.8)

##estimate a 1st. order Beta-skew-t-EGARCH model:
mymod <- tegarch.est(y)

##compute fitted values:
fitvals <- tegarch.fit(y, mymod)

##make plot of the conditional standard deviaiton:
plot(fitvals[,"stdev"])

##make histogram of the standardised residuals:
hist(fitvals[,"residstd"])