In this paper, we consider the problem of testing for parameter changes in time series models based on a cusum test. Although the test procedure is well established for the mean and variance in time series models, a general parameter case has not been discussed in the literature. Therefore, here we develop a cusum test for parameter change in a more general framework. As an example, we consider...

Using the spectral regression and Gaussian semiparametric methods of estimating the long-memory parameter, we test for fractional dynamic behavior in a number of important Japanese financial time series: spot exchange rates, forward exchange rates, stock prices, currency forward premia, Euroyen deposit rates, and the Euroyen term premium. Stochastic long memory is established as a feature of th...

The traditional base state with amendments model usually use the time sequence when choosing the base state to manipulate and it is usually use the extension of time point to represent the time-slot retrieval, which ignores the adjacent feature of the time point in a certain time-slot. The concept of operated base state and operation times is introduce in this paper, in which a base state with ...

This paper reviews the main estimation and prediction results derived in the context of functional time series, when Hilbert and Banach spaces are considered, specially, in the context of autoregressive processes of order one (ARH(1) and ARB(1) processes, for H and B being a Hilbert and Banach space, respectively). Particularly, we pay attention to the estimation and prediction results, and sta...

We present a new approach to generalised autoregressive conditional heteroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nan-cial time series, for example, asymmet...

This paper introduces the class of seasonal specific structural time series models, according to which each season follows specific dynamics, but is also tied to the others by a common random effects. This results in a dynamic variance components model that can account for some kind of periodic behaviour, such as periodic heteroscedasticity, and is tailored to deal with situations when one or a...