We discuss computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.

In this paper, we examine the stochastic volatility behaviour in the Spanish stock market returns over the time period 2 January 2001 - 12 May 2006. We use a long memory model that takes into account the existence of an endogenous structural break. When no breaks are taken into account the...

The functioning of electricity markets has experienced increasing complexity as a result of deregulation in recent years. Consequently this affects the multilateral price behaviour across regions with physical exchange of power. It has been documented elsewhere that features such aslong memory...

The micro evidence indicates that small firms grow faster than big firms. I argue that this relationship between the expected growth rate of a firm and its size may provide a micro foundation for the well-known high degree of persistence of shocks to aggregate output. The logic goes as follows....

By design a wavelet's strength rests in its ability to simultaneously localize a process in time-scale space. The wavelet's ability to localize a time series in time-scale space directly leads to the computational efficiency of the wavelet representation of a N X N matrix operator by allowing...

A common finding in the empirical literature is that financial volatility exhibits high persistence, or slow mean reversion of the order of months. We present evidence that financial volatility data contains more than a single time scale. After showing that the expectation of the sum of the...

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these...

This paper discusses various ways of measuring the persistence or Long Memory (LM) of financial market risk in both its time and frequency domains. For the measurement of the risk, irregularity or 'randomness' of these series, we can compute a set of critical Lipschitz - Hölder exponents, in...

This paper graphically demonstrates the significant impact of the observed financial market persistence, i.e., long term memory or dependence, on European option valuation. Many empirical researchers have observed non-Fickian degrees of persistence or long memory in the financial markets...

This article uses the DCC–FIAPARCH model to examine the time-varying properties of conditional return and volatility of crude oil and US stock markets as well as their dynamic correlations over the period 1988–2013. Our results indicate that both the long memory and asymmetric behavior...