Modelling long-run trends and cycles in financial time series data

Copyright @ 2012 Wiley Publishing Ltd. This is the accepted version of the following article: "Modelling long-run trends and cycles in financial time series data", Journal of Time Series Analysis, 34(3), 405-421, 2013, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12010/abstract.This article proposes a general time series framework to capture the long-run behaviour of financial series. The suggested approach includes linear and segmented time trends, and stationary and non-stationary 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 both zero but non-zero (cyclical) frequencies. This framework is used to analyse five annual time series with a long span, namely dividends, earnings, interest rates, stock prices and long-term government bond yields. The results based on several likelihood criteria indicate that the five series exhibit fractional integration with one or two poles in the spectrum, and are quite stable over the sample period examined.Ministerio de Ciencia y Tecnologia and Jeronimo de Ayanz project of the Government of Navarra

Modelling Long Run Trends and Cycles in Financial Time Series Data

This paper proposes a general time series framework to capture the long-run behaviour of financial series. The suggested approach includes linear and segmented 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 both zero but non-zero (cyclical) frequencies. This framework is used to analyse five annual time series with a long span, namely dividends, earnings, interest rates, stock prices and long-term government bond yields. The results based on several likelihood criteria indicate that the five series exhibit fractional integration with one or two poles in the spectrum, and are quite stable over the sample period examined

Modelling long-run trends and cycles in financial time series data

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

US stock market volatility persistence: evidence before and after the burst of the IT bubble

Volatility, Bull market, Bear market, Long range dependence, Absolute returns, Squared returns, G10, G12, C32,