nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒12‒14
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Temporal aggregaton of univariate linear time series models By Andrea, SILVESTRINI
  2. Phase Distribution and Phase Correlation of Financial Time Series By Ming-Chya Wu; Ming-Chang Huang; Hai-Chin Yu; Thomas Chiang
  3. Are Real Exchange Rates Nonlinear or Nonstationary? Evidence from a new Threshold Unit Root Test By Erdem Basci; Mehmet Caner

  1. By: Andrea, SILVESTRINI
    Abstract: In this paper we feature state-of-the-art econometric methodology of temporal aggregation for univariate linear time series, namely ARIMA-GARCH models. We present a unified overview of temporal aggregation techniques for this broad class of processes and we explain in detail, although intuitively, the technical machinery behind the results. An empirical application with Belgian public deficit data illustrates the main issues.
    Keywords: Temporal aggregation; ARIMA, GARCH, seasonality
    JEL: C10 C22 C43
    Date: 2005–08–15
  2. By: Ming-Chya Wu (Academic Sinica, Taiwan); Ming-Chang Huang (Chung Yuan University, Taiwan); Hai-Chin Yu (Chung Yuan University, Taiwan); Thomas Chiang (Drexel University, USA)
    Abstract: Scaling, phase distribution and phase correlation of financial time series are investigated based on the Dow Jones Industry Average (DJIA) and NASDAQ 10-minute intraday data for a period from Aug. 1 1997 to Dec. 31 2003. The returns of the two indices are shown to have nice scaling behaviors and belong to stable distributions according to the criterion of Levy's alpha stable distribution condition. A novel approach catching characteristic features of financial time series based on the concept of instantaneous phase is further proposed to study phase distribution and correlation. The analysis of phase distribution concludes return time series fall into a class which is different from other non-stationary time series. The correlation between returns of the two indices probed by the distribution of phase difference indicates there was a remarkable change of trading activities after the event of 911 attack, and this change persisted in later trading activities.
    Keywords: Phase Distribution, High Frequency Data, Scaling Analysis, Levy Distribution, Stock Market, Frequency Variant
    JEL: G
    Date: 2005–12–10
  3. By: Erdem Basci (Central Bank of Turkey); Mehmet Caner (University of Pittsburgh)
    JEL: F3 F4
    Date: 2005–12–09

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