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Papers by Bruno De Backer

Research paper thumbnail of Credit gaps in Belgium : identification, characteristics and lessons for macroprudential policy

Financial Stability Review, 2016

the 2007-2008 financial crisis revealed the importance of financial cycles, and more specifically... more the 2007-2008 financial crisis revealed the importance of financial cycles, and more specifically credit cycles, for financial stability and developments in the real economy. even though several (mostly country-specific) banking or financial crises had already shown the relevance of such credit cycles in the build-up of financial imbalances, it was the recent financial crisis that triggered renewed interest in the impact of credit and financial cycles on economic and financial stability. Since then, a growing literature has confirmed that systemic banking crises are often preceded by credit booms (e.g. Reinhart and Rogoff, 2009 ; Drehmann et al., 2011 ; Gourinchas and obstfeld, 2012 ; Behn et al., 2013 ; Laeven and valencia, 2013 ; Detken et al., 2014 and aikman et al., 2015) and that the subsequent credit crunch during the bust phase of the credit cycle tends to cause more severe recessions than those that do not coincide with a financial crisis (e.g. Hutchinson and noy, 2005 ; Rei...

Research paper thumbnail of Real and Financial Cycles in EU Countries - Stylised Facts and Modelling Implications

SSRN Electronic Journal, 2018

Research paper thumbnail of Macrofinancial information on the post-COVID-19 economic recovery: Will it be V, U or L-shaped?

Finance Research Letters, 2021

Research paper thumbnail of A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models

Journal of Empirical Finance, 2014

ABSTRACT We present an estimation and forecasting method, based on a differential evolution MCMC ... more ABSTRACT We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.

Research paper thumbnail of Estimating and forecasting structural breaks in financial time series

We present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in... more We present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in AR-GARCH models subject to an unknown number of structural breaks at unknown dates. Break dates are directly treated as parameters and the number of breaks is determined by the marginal likelihood criterion. We prove the convergence of the algorithm and we show how to compute marginal likelihoods. We allow for both pure change-point and recurrent regime specifications and we show how to forecast structural breaks. We illustrate the efficiency of the algorithm through simulations and we apply it to eight financial time series of daily returns over the period 1987-2011. We find at least three breaks in all series.

Research paper thumbnail of A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models

Journal of Empirical Finance, 2014

We present an estimation and forecasting method, based on a differential evolution MCMC method, f... more We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.

Research paper thumbnail of Credit gaps in Belgium : identification, characteristics and lessons for macroprudential policy

Financial Stability Review, 2016

the 2007-2008 financial crisis revealed the importance of financial cycles, and more specifically... more the 2007-2008 financial crisis revealed the importance of financial cycles, and more specifically credit cycles, for financial stability and developments in the real economy. even though several (mostly country-specific) banking or financial crises had already shown the relevance of such credit cycles in the build-up of financial imbalances, it was the recent financial crisis that triggered renewed interest in the impact of credit and financial cycles on economic and financial stability. Since then, a growing literature has confirmed that systemic banking crises are often preceded by credit booms (e.g. Reinhart and Rogoff, 2009 ; Drehmann et al., 2011 ; Gourinchas and obstfeld, 2012 ; Behn et al., 2013 ; Laeven and valencia, 2013 ; Detken et al., 2014 and aikman et al., 2015) and that the subsequent credit crunch during the bust phase of the credit cycle tends to cause more severe recessions than those that do not coincide with a financial crisis (e.g. Hutchinson and noy, 2005 ; Rei...

Research paper thumbnail of Real and Financial Cycles in EU Countries - Stylised Facts and Modelling Implications

SSRN Electronic Journal, 2018

Research paper thumbnail of Macrofinancial information on the post-COVID-19 economic recovery: Will it be V, U or L-shaped?

Finance Research Letters, 2021

Research paper thumbnail of A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models

Journal of Empirical Finance, 2014

ABSTRACT We present an estimation and forecasting method, based on a differential evolution MCMC ... more ABSTRACT We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.

Research paper thumbnail of Estimating and forecasting structural breaks in financial time series

We present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in... more We present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in AR-GARCH models subject to an unknown number of structural breaks at unknown dates. Break dates are directly treated as parameters and the number of breaks is determined by the marginal likelihood criterion. We prove the convergence of the algorithm and we show how to compute marginal likelihoods. We allow for both pure change-point and recurrent regime specifications and we show how to forecast structural breaks. We illustrate the efficiency of the algorithm through simulations and we apply it to eight financial time series of daily returns over the period 1987-2011. We find at least three breaks in all series.

Research paper thumbnail of A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models

Journal of Empirical Finance, 2014

We present an estimation and forecasting method, based on a differential evolution MCMC method, f... more We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.