GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation...

This paper examines the stochastic volatility model suggested by Heston (1993). We employ a time-series approach to estimate the model and we discuss the potential effects of time-varying skewness and kurtosis on the performance of the model. In particular, it is found that the model tends to...

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram-Charlier series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by...

We study the processes for the conditional mean and variance given a specification of the process for the observed time series. We derive general results for the conditional mean of univariate and vector linear processes, and then apply it to various models of interest. We also consider the...

We investigate several important inference issues for factor models with dynamic heteroskedasticity in the common factors. First, we show that such models are identified if we take into account the time-variation in the variances of the factors. Our results also apply to dynamic versions of the...

We analyze extensively the characteristics of the solution to an irreversibleinvestment decision when the only source of uncertainty comes from interest rates.They are assumed to be driven by the popular Cox-Ingersoll-Ross (CIR) stochasticprocess. Particular attention is paid to the impact that...

This paper deals with analysing and forecasting intradaily volatility in electricity spot prices. We analyse the hourly spot prices from the Argentine Electricity Market by grouping prices in three daily series (block bids). We estimate the VAR model for the conditional mean structure and...

The article proposes a new algorithm for adjusting correlation matrices and for comparison with Finger's algorithm, which is used to compute Value-at-Risk in RiskMetrics for stress test scenarios. The solution proposed by the new methodology is always better than Finger's approach in the sense...

In the context of time series regression, we extend the standard Tobitmodel to allow for the possibility of conditional heteroskedastic error processes of the GARCH type.We discuss the likelihood function of the Tobit model in the presence of conditionally heteroskedastic errors.Expressing the...

Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for estimating stochastic differential equations. They adjust for the bias (inconsistency) caused by discretization of the underlying stochastic process, which is in continuous time. The...