Pierre Guérin

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Staff Analytical Notes

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Staff Working Papers

What Drives Interbank Loans? Evidence from Canada

Staff Working Paper 2018-5 Narayan Bulusu, Pierre Guérin
We identify the drivers of unsecured and collateralized loan volumes, rates and haircuts in Canada using the Bayesian model averaging approach to deal with model uncertainty. Our results suggest that the key friction driving behaviour in this market is the collateral reallocation cost faced by borrowers.
Content Type(s): Staff Research, Staff Working Papers Topic(s): Financial markets, Wholesale funding JEL Code(s): C, C5, C55, E, E4, E43, G, G2, G23

Markov‐Switching Three‐Pass Regression Filter

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes.
Content Type(s): Staff Research, Staff Working Papers Topic(s): Econometric and statistical methods JEL Code(s): C, C2, C22, C23, C5, C53

What Are the Macroeconomic Effects of High-Frequency Uncertainty Shocks

Staff Working Paper 2016-25 Laurent Ferrara, Pierre Guérin
This paper evaluates the effects of high-frequency uncertainty shocks on a set of low-frequency macroeconomic variables that are representative of the U.S. economy. Rather than estimating models at the same common low-frequency, we use recently developed econometric models, which allows us to deal with data of different sampling frequencies.

The Dynamics of Capital Flow Episodes

Staff Working Paper 2016-9 Christian Friedrich, Pierre Guérin
This paper proposes a novel methodology for identifying episodes of strong capital flows based on a regime-switching model. In comparison with the existing literature, a key advantage of our methodology is to estimate capital flow regimes without the need for context- and sample-specific assumptions.

Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data

Staff Working Paper 2015-24 Pierre Guérin, Danilo Leiva-Leon
This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging – so as to explicitly reflect the objective of forecasting a discrete outcome.

Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work

Staff Working Paper 2014-11 Christiane Baumeister, Pierre Guérin, Lutz Kilian
The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets.

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