This paper contains a survey of univariate models of conditional heteroskedasticity. The classical ARCH model is mentioned, and various extensions of the standard GARCH model are highlighted. This includes the Exponential GARCH model. Stochastic volatility models remain outside this review.

Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity....

This article proposes a multivariate model of inflation with conditionally heteroskedastic common and country-specific components. The model is estimated in one-step via Quasi-Maximum Likelihood for the G7 countries for the period Q1-1960 to Q4-2009. It is found that various model specifications...

Exchange market pressure (EMP) measures the pressure on a currencyto depreciate. It adds to the actual depreciation a weightedcombination of policy instruments used to ward off depreciation,such as interest rates and foreign exchange interventions, where theweights are their effectiveness. The...

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...

A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...

In this paper, we propose two parametric alternatives to the standard GARCH model. They allow the conditional variance to have a smooth time-varying structure of either additive or multiplicative type. The suggested parameterizations describe both nonlinearity and structural change in the...

This paper proposes a semiparametric approach by introducing a smooth scale function into the standard GARCH model so that conditional heteroskedasticity and scale change in a financial time series can be modelled simultaneously. An estimation procedure combining kernel estimation of the scale...

Various versions of the wild bootstrap are studied as applied to regression models with heteroskedastic errors. We develop formal Edgeworth expansions for the error in the rejection probability (ERP) of wild bootstrap tests based on asymptotic t statistics computed with a heteroskedasticity...