Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models

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.Long memory, Bias, Modified profile likelihood, Restricted maximum likelihood estimator, Time-series regression model likelihood

Persistence and cycles in US hours worked

This paper analyses monthly hours worked in the US over the sample period 1939m1 – 2011m10 using a cyclical long memory model; this is based on Gegenbauer processes and characterised by autocorrelations decaying to zero cyclically and at a hyperbolic rate along with a spectral density that is unbounded at a non-zero frequency. The reason for choosing this specification is that the periodogram of the hours worked series has a peak at a frequency away from zero. The empirical results confirm that this model works extremely well for hours worked, and it is then employed to analyse their relationship with technology shocks. It is found that hours worked increase on impact in response to a technology shock (though the effect dies away rapidly), consistently with Real Business Cycle (RBC) models.This study is partly funded by the the Ministry of Education of Spain (ECO2011-2014 ECON Y FINANZAS, Spain) and from a Jeronimo de Ayanz project of the Government of Navarra

Long memory and volatility dynamics in the US Dollar exchange rate

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 forecasting given their importance for FOREX dealers. Compared with previous studies using a standard fractional integration framework such as Granger and Ding (1996), we estimate a more general model which allows for dependence not only at the zero but also at other frequencies. The results show differences in the behaviour of the two series: a long-memory cyclical model and a standard I(d) model seem to be the most appropriate for the US dollar rate vis-à-vis the Euro and the Japanese Yen respectively

Filtering Random Graph Processes Over Random Time-Varying Graphs

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochastic- ity in both the graph topology as well as the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response (FIR) and autoregressive moving average (ARMA) graph filters, when operating on random time- varying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that (i) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and (ii) there are meaningful upper bounds for the variance of the filter output. We conclude the paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm, and yield a (up to) four times complexity redution, with very little difference from the optimal solution

Long Memory and Volatility Dynamics in the US Dollar Exchange Rate

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 forecasting given their importance for FOREX dealers. Compared with previous studies using a standard fractional integration framework such as Granger and Ding (1996), we estimate a more general model which allows for dependence not only at the zero but also at other frequencies. The results show differences in the behaviour of the two series: a long-memory cyclical model and a standard I(d) model seem to be the most appropriate for the US dollar rate vis-à-vis the Euro and the Japanese Yen respectively.Fractional integration, Long memory, Exchange rates, Volatility

Dual estimation of the poles and zeros of an ARMA(p,q) process

"September 1985."Bibliography: p. 33-34.Army Research Office Contract DAAG-29-84-K-0005M. Isabel Ribeiro, Jose M.F. Moura

Nonlinear Features of Realized FX Volatility

This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to measure ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAX model. We explore nonlinear departures from these linear specifications using a doubly stochastic process under duration-dependent mixing. This process can capture large abrupt changes in the level of volatility, time varying persistence, and time-varying variance of volatility. The results have implications for forecast precision, hedging, and pricing of derivatives. Dans cet article, nous étudions les caractéristiques nonlinéaires de la dynamique de la volatilité des taux de change à l'aide d'estimations de la volatilité quotidienne basées sur la somme du carré des rendements intraquotidiens. Les erreurs de mesure commises en utilisant la volatilité réalisée pour mesurer la volatilité latente ex post font en sorte que les modèles standards de séries chronologiques de la variance conditionnelle deviennent des variantes d'un modèle ARMAX. Nous explorons des alternatives nonlinéaires à ces spécifications linéaires en utilisant un processus doublement stochastique, avec mixage dépendant de la durée. Ce processus peut capter des changements importants et abrupts dans le niveau de la volatilité, de même qu'une persistence et une variance de la volatilité variant dans le temps. Nos résultats influent sur la précision des prévisions, la couverture et l'évaluation des produits dérivés.High-frequency data, realized volatility, semi-Marko, Données à haute fréquence, volatilité réalisée, demi-Markov

Generating long streams of 1/falpha1/f^alpha noise

We review existing methods for generating long streams of 1/f^alpha noise ($0<\alpha\le 2$) focusing on the digital filtering of white noise. We detail the formalism to conceive an efficient random number generator (white outside some bounds) in order to generate very long streams of noise without an exhaustive computer memory load. For $\alpha=2$ it is shown why the process is equivalent to a random-walk and can be obtained simply by a first order filtering of white noise. As soon as $\alpha<2$ the problem becomes non linear and we show why the exact digital filtering method becomes inefficient. Instead, we work out the formalism of using several 1/f^2 filters spaced logarithmically, to approximate the spectrum at the percent level. Finally, from work on logistic maps, we give hints on how to design generators with $\alpha>2$. The software is available from http://planck.lal.in2p3.fr/article.php3?id\_article=8Comment: Last version (corrected web site

Recursive Parametric Frequency/Spectrum Estimation for Nonstationary Signals With Impulsive Components Using Variable Forgetting Factor

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