This package provides facilities for the simulation, estimation and forecasting of first order Beta-Skew-t-EGARCH models with leverage (one-component and two-component versions), see Harvey and Sucarrat (2014), and Sucarrat (2013).
Let y[t] denote a financial return at time t equal to
y[t] = sigma[t]*epsilon[t]
where sigma[t] > 0 is the scale or volatility (generally not equal to the conditional standard deviation), and where epsilon[t] is IID and t-distributed (possibly skewed) with df degrees of freedom. Then the first order log-volatility specifiction of the one-component Beta-Skew-t-EGARCH model can be parametrised as
sigma[t] = exp(lambda[t]),
lambda[t] = omega + lambdadagger,
lambdadagger[t] = phi1*lambdadagger[t-1] + kappa1*u[t-1] + kappastar*sign[-y]*(u[t-1]+1).
So the scale or volatility is given by sigma[t] = exp(lambda[t]). The omega is the unconditional or long-term log-volatility, phi1 is the GARCH parameter (|phi1| < 1 implies stability), kappa1 is the ARCH parameter, kappastar is the leverage or volatility-asymmetry parameter and u[t] is the conditional score or first derivative of the log-likelihood with respect to lambda. The score u[t] is zero-mean and IID, and (u[t]+1)/(df+1) is Beta distributed when there is no skew in the conditional density of epsilon[t]. The two-component specification is given by
sigma[t] = exp(lambda[t]),
lambda[t] = omega + lambda1dagger + lambda2dagger,
lambda1dagger[t] = phi1*lambdadagger[t-1] + kappa1*u[t-1],
lambda2dagger[t] = phi2*lambdadagger[t-1] + kappa2*u[t-1] + kappastar*sign[-y]*(u[t-1]+1).
The first component, lambda1dagger, is interpreted as the long-term component, whereas the second component, lambda2dagger, is interpreted as the short-term component.
The two main functions of the package are
tegarch. The first simulates a Beta-Skew-t-EGARCH models whereas the second estimates one. The second object returns an object (a list) of class 'tegarch', and a collection of methods can be applied to this class:
vcov.tegarch. In addition, the output produced by the
tegarchSim function and the
residuals.tegarch methods are of the Z's ordered observations (
zoo) class, which means a range of time-series methods are available for these objects.
Genaro Sucarrat, http://www.sucarrat.net/
Fernandez and Steel (1998), 'On Bayesian Modeling of Fat Tails and Skewness', Journal of the American Statistical Association 93, pp. 359-371.
Harvey and Sucarrat (2014), 'EGARCH models with fat tails, skewness and leverage'. Computational Statistics and Data Analysis 76, pp. 320-338.
Sucarrat (2013), 'betategarch: Simulation, Estimation and Forecasting of First-Order Beta-Skew-t-EGARCH models'. The R Journal (Volume 5/2), pp. 137-147.
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#simulate 500 observations from model with default parameter values: set.seed(123) y <- tegarchSim(500) #estimate and store as 'mymod': mymod <- tegarch(y) #print estimates and standard errors: print(mymod) #graph of fitted volatility (conditional standard deviation): plot(fitted(mymod)) #plot forecasts of volatility 1-step ahead up to 10-steps ahead: plot(predict(mymod, n.ahead=10))
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