This paper investigates persistence in high-frequency, intraday data (and also daily and monthly ones) in the case of the EuroStoxx 50 futures over the period from 2002 to 2018 (720 million trade records) using R/S analysis and the Hurst exponent as a measure of persistence. The results indicate...

In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. We find that the high-frequency EUA price dynamics are very well...

We provide a method for distinguishing long-range dependence from deterministic trends such as structural breaks. The method is based on the comparison of standard log-periodogram regression estimation of the memory parameter with its tapered counterpart. The difference of these estimators...

This note examines the stochastic properties of US term spreads with parametric and semi-parametric fractional integration techniques. Since the observed data (rather than the estimated residuals from a cointegrating regression) are used for the analysis, standard methods can be applied. The...

This paper analyses the long-memory properties of high frequency financial time series. It focuses on temporal aggregation and the influence that this might have on the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for...

This paper focuses on nominal exchange rates, specifically the US dollar rate vis-à-vis the Euro and the Japanese Yen at a daily frequency. We model both absolute values of returns and squared returns using long-memory techniques, being particularly interested in volatility modelling and...

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week...

The research used a long memory or Autoregressive Fractionally Integrated Moving Average model to study and forecast crude oil prices using weekly West Texas Intermediate and Brent series for the period 15/5/1987 to 20/12/2013. Fractional differencing Methods such as Local Whittle Estimator and...

In this study, the performance of the Multifractal Model of Asset Returns (MMAR) was examined for stock index returns of four emerging markets. The MMAR, which takes into account stylized facts of financial time series, such as long memory, fat tails and trading time, was developed as an...

Long memory and nonlinearity are two key features of some macroeconomic time series which are characterized by persistent shocks that seem to rise faster during recession than it falls during expansion. A variant of nonlinear time series model together with long memory are used to examine these...