The Huntington's disease candidate region exhibits many different haplotypes.

Analysis of 78 Huntington's disease (HD) chromosomes with multi-allele markers revealed 26 different haplotypes, suggesting a variety of independent HD mutations. The most frequent haplotype, accounting for about one third of disease chromosomes, suggests that the disease gene is between D4S182 and D4S180. However, the paucity of an expected class of chromosomes that can be related to this major haplotype by assuming single crossovers may reflect the operation of other mechanisms in creating haplotype diversity. Some of these mechanisms sustain alternative scenarios that do not require a multiple mutational origin for HD and/or its positioning between D4S182 and D4S180.     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.