Modelling long-run trends and cycles in financial time series data - Guglielmo Maria Caporale; Juncal Cunado; Luis A. Gil-Alana (Center for Economic Studies and ifo Institute (CESifo), 2008)
This paper proposes a very general time series framework to capture the long-run behaviour of financial series. The suggested model includes linear and non-linear time trends, and stationary and nonstationary processes based on integer and/or fractional degrees of differentiation. Moreover, the spectrum is allowed to contain more than a single pole or singularity, occurring at zero and non-zero (cyclical) frequencies. This model is used to analyse four annual time series with a long span, namely dividends, earnings, interest rates and long-term government bond yields. The results indicate that the four series exhibit fractional integration with one or two poles in the spectrum. A forecasting comparison shows that a model with a non-linear trend along with fractional integration outperforms alternative models over long horizons.
Keywords: C22; G1; 330; Fractional integration; financial time series data; trends; cycles; Finanzmarkt; Zeitreihenanalyse; Trend; Rendite; Prognoseverfahren; Theorie; USA