Statistical Evidence on the Mean Reversion of Interest Rates

End, Jan Willem van den; Willem, Jan

doi:10.2139/ssrn.1950596

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Economic theory predominantly assumes that interest rates are (in the long run) mean reverting. But statistical evidence is not so clear (van den End, 2011). The mainstream literature says that unit roots can not be rejected, which would imply that interest rates are not mean reverting (Campbell & Shiller, 1991;Rose, 1988;Siklos & Wohar, 1997;Stock & Watson, 1988).…”

Section: Datamentioning

confidence: 99%

A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction

Berninger

Stöcker

Rügamer

2021

Journal of Forecasting

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Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short-term and long-term prediction of time series that exhibit a temporary nonstationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short-term predictions with long-term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to that of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model.

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Section: Datamentioning

confidence: 99%

A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction

Berninger

Stöcker

Rügamer

2021

Journal of Forecasting

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“…Economic theory predominantly assumes that interest rates are (in the long run) mean reverting. But statistical evidence is not so clear [29]. The mainstream literature says that unit roots can not be rejected, which would imply that interest rates are not mean reverting [24,22,25,4].…”

Section: Datamentioning

confidence: 99%

A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction

Berninger¹,

Stöcker²,

Rügamer³

2020

Preprint

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Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC-based inference by deriving relevant full conditional distributions and employ a Metropolis-Hastings within Gibbs Sampler approach to sample from the posterior (predictive) distribution. In combining data-driven short term predictions with long term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to the one of a 2-Additive-Factor Gaussian model as well as to the predictions of a dynamic Nelson-Siegel model.

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“…This study uses a data set including 73 variables that could potentially be relevant to explaining Gibson's paradox in the Netherlands over the period 1800-2012. The series have been obtained from following sources: Zanden & Riel (2000), Zanden & Lindblad (1989), Smits, Horlings, & Van Zanden (2000), Hart, Jonker, & van Zanden (2010), van Bochove & Huitker (1987, Netherland (2010), (Netherland, 2012), CBS -Historical series -Publications (2013), International Institute of Social History (2013), Homer & Sylla (2011), Jacobs & Smits (2001), Barro & Ursua, (2008), Officer & Williamson (2013, Bos (2008), Groote, Albers, & De Jong (1996), Homepage Statistics -De Nederlandsche Bank (2013), van den End (2011).…”

Section: Data Sources and Descriptionmentioning

confidence: 99%

A Non-Linear Analysis of Gibson’s Paradox

Caporale

Škare

2018

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techniques are used. It is shown that changes in monetary policy regimes or volatility in the price of gold by themselves cannot account for the behaviour of government bond yields and prices over the last 200 years. However, the inclusion of changes in the real rate of return on capital, M1, primary credit rate, expected inflation, and money purchasing power enables a nonlinear model to account for a sizeable percentage of the total variance of Dutch bond yields. This is a novel study of the Gibson's paradox using a non-linear approach unravelling the multidimensional nature of the paradox. The results should assist policy makers in setting monetary policy and help to better understand the behaviour of financial institutions on the markets. Central banks, should however evaluate the implication of this study in setting inflation targeting goals. Results of this study are also expected to stimulate further research on the bank credit policies by managing interest rates.

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Statistical Evidence on the Mean Reversion of Interest Rates

Cited by 7 publications

References 40 publications

A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction

A Bayesian time‐varying autoregressive model for improved short‐term and long‐term prediction

A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction

A Non-Linear Analysis of Gibson’s Paradox

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