This paper studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time dependent conditional variances. This TVP model is flexible to accommodate a wide variety of timevariation patterns but requires effective shrinkage...

A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real time. The method conveniently deals with the path dependence...

The author evaluates the effect of the Bank of Canada's conditional commitment regarding the target overnight rate on longer-term market interest rates by taking into account the relationship between interest rates, inflation, and unemployment rates. By using vector autoregressive models of...

We offer a multi-period systemic risk assessment framework with which to assess recent liquidity and capital regulatory requirement proposals in a holistic way. Following Morris and Shin (2009), we introduce funding liquidity risk as an endogenous outcome of the interaction between market...

This paper proposes a class of parametric correlation models that apply a two-layer autoregressive-moving-average structure to the dynamics of correlation matrices. The proposed model contains the Dynamic Conditional Correlation model of Engle (2002) and the Varying Correlation model of Tse and...

The mixture innovation (MI) model places spike-and-slab mixture distributions on the innovations of time-varying regression coefficients and allows for a wide variety of time variation patterns. Despite its appeal, estimating the MI model can be computationally prohibitive when the number of...