In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton-Raphson estimator and the local profile likelihoo...

This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity ~ARMAGARCH! model+ The conditions for the strict stationarity, the ergodicity, and the higher order moments of the model are established+ Consistency of the quasimaximum-likelihood estimator ~QMLE! is proved under only the second-order moment conditi...

This paper uses a multivariate vector error-correction generalized autoregressive conditional heteroscedasticity model to investigate the effect of British grain prices on their Irish equivalents. We find that in the long run the law of one price holds and in the short run the model captures the salient features of Irish grain prices. The model is used to compute rolling forecasts of the condit...

This master thesis describes how to price options by means of Genetic Programming. The underlying model is the Generalized Autoregressive Conditional Heteroskedastic (GARCH) asset return process. The goal of this master thesis is to find a closed-form solution for the price of European call options where the underlying securities follow a GARCH process. The data are simulated over a wide range ...

in this study, the relationship between inflation and economic growth of iran is conciliated with a perspective on uncertainty of inflation. we use a generalized autoregressive conditional heteroskedasticity (garch) model that make possible this advantage that conditional variance of error term changes along the time, also, vector error correction model (vecm) is stable. for surveying the co-in...

In this paper, we suggest and evaluate specification tests to test the validity of the conditional mean function implied by Autoregressive Conditional Duration (ACD) models. We propose Lagrange multiplier tests against sign bias alternatives, various types of conditional moment tests and integrated conditional moment tests which are consistent against all possible alternatives. In a Monte-Carlo...

In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The modeling framework incorporates diurnal and annual periodicity modeling by peri...