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Modelling Long-Run Trends and Cycles in Financial Time Series Data

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Author Info
Guglielmo Maria Caporale ()
Juncal Cunado ()
Luis A. Gil-Alana ()

Additional information is available for the following registered author(s):

Abstract

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.

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Publisher Info
Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number CESifo Working Paper No. 2330.

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Date of creation: 2008
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Handle: RePEc:ces:ceswps:_2330

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Related research
Keywords: fractional integration; financial time series data; trends; cycles;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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