A new parametric minimum distance time-domain estimator for ARFIMA processes is introduced in this paper. The proposed estimator minimizes the sum of squared correlations of residuals obtained after filtering a series through ARFIMA parameters. The estimator is easy to compute and is consistent and asymptotically normally distributed for fractionally integrated (FI) processes with an integratio...

The purpose of this paper is to consider how to forecast implied volatility for a selection of UK companies with traded options on their stocks. We consider a range of GARCH and logARFIMA based models as well as some simple forecasting rules. Overall, we find that a logARFIMA model forecasts best over short and long horizons. Key-words : Implied Volatility, Forecasting, ARFIMA, GARCH, log-ARFIM...

This paper introduces a family of “generalized long-memory time series models”, in which observations have a specified conditional distribution, given a latent Gaussian fractionally integrated autoregressive moving average (ARFIMA) process. The observations may have discrete or continuous distributions (or a mixture of both). The family includes existing models such as ARFIMA models themselves,...

Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify estimate long parameter partially integrated series. One common models used represent that a ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are fractional number called parameter. To analyze determine model, fractal must be...

The standard approaches to estimating minimum variance hedge ratios (MVHRs) are mis-specified when futures prices are subject to price limits. This paper proposes a bivariate tobit-FIGARCH model with maturity effects to estimate dynamic MVHRs using single and multiple period approaches. Simulations and an application to a commodity futures hedge support the proposed approach and highlight the i...

In this work we focus on the effects of aggregation on parameters estimation and Value-at-Risk computation if the data generator follow a FIGARCH model. We present a Montecarlo experiment which shows that the memory structure is affected. In a second simulation study we compare Value-at-Risk estimates obtained by high frequency and aggregated data. We verify that aggregated data have a better p...

In a recent publication Stadnitski (2012) presented an overview of methods to estimate fractal scaling in time series, outlined as an accessible tutorial1. The publication was set-up as a comparison between monofractal and ARFIMA methods, and promotes ARFIMA to distinguish between spurious and genuine 1/f noise, shedding light on “the problem that the log–log power spectrum of short-memory ARMA...

The peaks-over-threshold (POT) method has a long tradition in modelling extremes environmental variables. However, it originally been introduced under the assumption of independently and identically distributed (iid) data. Since data often exhibits time series structure, this is likely to be violated due short- long-term dependencies practical settings, leading clustering high-threshold exceeda...