In this paper we propose a new bootstrap, or Monte-Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages,...

The analysis of diffusion processes in financial models is crucially dependent on the form of the drift and diffusion coefficient functions. A methodology is proposed for estimating and testing coefficient functions for ergodic diffusions that are not directly observable. It is based on...

A major application of rescaled adjusted range analysis (RS analysis) is the study of price fluctuations in financial markets. There, the value of the Hurst constant, H, in a time series may be interpreted as an indicator of the irregularity of the price of a commodity, currency or similar...

Newly developed and advanced methods for nonlinear time series analysis are in general not available in standard software packages. Moreover, their implementation requires substantial time, computing power as well as programming skills. The recent results on lag and bandwidth selection methods...

The testing of a computing model for a stationary time series is a standard task in statistics. When a parametric approach is used to model the time series, the question of goodness-of-fit arises. In this paper, we employ the empirical likelihood for an a-mixing process and formulate a statistic...