nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒05‒04
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Functional limit theorems for generalized variations of the fractional Brownian sheet By Mikko S. Pakkanen; Anthony Réveillac
  2. Modelling Return and Volatility of Oil Price using Dual Long Memory Models By Heni BOUBAKER; Nadia SGHAIER
  3. Wavelet based Estimation of Time- Varying Long Memory Model with Nonlinear Fractional Integration Parameter By Heni Boubaker; Nadia Sghaier
  4. Penalized Splines as Frequency Selective Filters - Reducing the Excess Variability at the Margins By Blöchl, Andreas

  1. By: Mikko S. Pakkanen (CREATES and Department of Economics and Business, Aarhus University); Anthony Réveillac (CEREMADE, Université Paris-Dauphine)
    Abstract: We prove functional central and non-central limit theorems for generalized variations of the anisotropic d-parameter fractional Brownian sheet (fBs) for any natural number d. Whether the central or the non-central limit theorem applies depends on the Hermite rank of the variation functional and on the smallest component of the Hurst parameter vector of the fBs. The limiting process in the former result is another fBs, independent of the original fBs, whereas the limit given by the latter result is an Hermite sheet, which is driven by the same white noise as the original fBs. As an application, we derive functional limit theorems for power variations of the fBs and discuss what is a proper way to interpolate them to ensure functional convergence.
    Keywords: Fractional Brownian sheet, central limit theorem, non-central limit theorem, Hermite sheet, power variation, Malliavin calculus
    JEL: C10 C14
    Date: 2014–04–23
    URL: http://d.repec.org/n?u=RePEc:aah:create:2014-14&r=ets
  2. By: Heni BOUBAKER; Nadia SGHAIER
    Abstract: This paper investigates the dynamic properties of both return and volatility of the oil price. The analysis is carried out using a set of double long memory specifications incorporating several features such as long range dependence, asymmetry in conditional variances and time varying correlations. The in-sample diagnostic tests as well as the out-of-sample forecasting results show the performance of the ARFIMA-FIAPARCH model.
    Keywords: Oil price, return, volatility, dual long memory.
    Date: 2014–04–29
    URL: http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-283&r=ets
  3. By: Heni Boubaker; Nadia Sghaier
    Abstract: In this paper, we propose a time-varying long memory model where the fractional integration parameter varies nonlinearly according to Smooth Transition Regressive (STR) model. To estimate the fractional integration parameter, we suggest a new estimation method based on wavelet approach. In particular, we consider the instan- taneous least squares estimator (ILSE). We conduct some simulation experiments and provide an empirical application to modeling the dynamics of volatilities of some fi- nancial time series. The obtained results show that the model proposed offers an interesting framework to describe time-varying long range dependence of volatilities and provide evidence of regime change in persistence to shocks.
    Date: 2014–04–29
    URL: http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-284&r=ets
  4. By: Blöchl, Andreas
    Abstract: Penalized splines have become a popular tool to model the trend component in economic time series. The outcome of the spline predominantly depends on the choice of a penalization parameter that controls the smoothness of the trend. This paper derives the penalization of splines by frequency domain aspects and points out their link to rational square wave filters. As a novel contribution this paper focuses on the so called excess variability at the margins that describes the undesired increasing variability of the trend estimation to the ends of the series. It will be shown that the too high volatility at the margins can be reduced considerably by a time varying penalization, which yields more reliable estimations for the most recent periods.
    Keywords: excess variability; penalized splines; spectral analysis; time varying penalization; trends
    Date: 2014–04
    URL: http://d.repec.org/n?u=RePEc:lmu:muenec:20687&r=ets

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