This note considers a new class of nonparametric estimators for nonlinear time-series models based on kernel smoothers. Various new results are given for two popular nonlinear time-series models and compared with the results of Thavaneswaran and Peiris (Statist. Probab. Lett. 28 (1996) 227).

Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonparametric estimation problems for nonlinear time-series models with time-varying parameter [alpha](t). Examples are considered from the usual classes of nonlinear time-series models. The goal of...

Following the general approach for constructing test statistics for stochastic models based on optimal estimating functions by Thavaneswaran (1991), a new test statistic via martingale estimating function is proposed. Applications to some time-series models such as random coefficient...

This paper features an analysis of the e_ectiveness of a range of portfolio diversification strategies as applied to a set of 17 years of monthly hedge fund index returns on a set of ten market indices representing 13 major hedge fund categories, as compiled by the EDHEC Risk Institute. The...

This paper features an analysis of the effectiveness of a range of portfolio diversification strategies as applied to a set of 17 years of monthly hedge fund index returns on a set of ten market indices representing 13 major hedge fund categories, as compiled by the EDHEC Risk Institute. The...

This paper features an analysis of the effectiveness of a range of portfolio diversication strategies as applied to a set of 17 years of monthly hedge fund index returns on a set of ten market indices representing 13 major hedge fund categories, as compiled by the EDHEC Risk Institute. The...

In this paper a number of alternative ACD models are compared using a sample of data for three major companies traded on the Australian Stock Exchange. The comparison is performed by employing the methodology for evaluating density and interval forecasts, developed by Diebold, Gunther and Tay...

We consider a new class of time series models (introduced by Engle & Russell 1998) used in statistical applications in finance. These models treat the time between events (durations) as a stochastic process and the corresponding durations are modelled using a theory similar to that of...

In this paper a number of alternative autoregressive conditional duration (ACD) models are compared using a sample of data for three major companies traded on the Australian Stock Exchange. The comparison is performed by employing the methodology for evaluating density and interval forecasts,...

This paper considers a new class of time series models called autoregressive conditional duration (ACD) models. These models have been developed and applied to investigate the price discovery process in the context of financial markets. The various statistical properties of this class of ACD...