This paper shows how one can compute option prices from a Bayesian inference view point, using a GARCH model for the dynamics of the the volatility of the underlying asset. The proposed evaluation of an option is the predictive expectation of its payoff function. The predictive distribution of...

Associated with every popular nonlinear estimationmethod is at least ont "artificial" linear regression. We define an artificial regression in terms of three conditions that it must satisfy. Then we show how artificial regressions can be useful for numerical optimization, testing hypotheses, and...

The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classicla R/S...

In this paper we develop a general strategy for studying the effect on unbiased, nearest-neighbor walks of opening up or blocking a trap or neural site on a d-dimensional lattice.

This paper employs response surface regressions based on simulation experiments to calculate asymptotic distribution functions of the tests for cointegration proposed by Johansen. The paper provides accurate tables of asymptotic critical values. A program which can be used to calculate both...

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...