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Many derivatives markets use collateral requirements calculated with industry-standard but dated methods that are not designed with systemic risk in mind. This paper explores whether the conservative nature of conventional collateral requirements outweighs their lack of consideration of systemic risk.

The objective of this paper is twofold. First, we assess whether financial education might be a suitable tool to promote the financial inclusion opportunities that big techs provide. Second, we study how this potential financial inclusion could impact financial stability.

We propose alternative single-equation semi-structural models for forecasting inflation in Canada, whereby structural New Keynesian models are combined with time-series features in the data. Several marginal cost measures are used, including one that in addition to unit labour cost also integrates relative price shocks known to play an important role in open-economies.

We examine the ability of the simple linear-quadratic model under rational expectations to explain dynamic behaviour of aggregate Canadian imports. In contrast to authors of previous studies who examine dynamic behaviour using the LQ model, we estimate the structural parameters using the Euler equation in a limited information framework that does not require an explicit solution for the model's control variables in terms of the exogenous forcing variables.

The existence of downward nominal wage rigidity (DNWR) has often been used to justify a positive inflation target. It is traditionally assumed that positive inflation could “grease the wheels” of the labour market by putting downward pressure on real wages, easing labour market adjustments during a recession.

Using a nonlinear New Keynesian model with a financial accelerator, we show that financial frictions generate large state-dependent amplification effects. Shocks propagate more strongly in periods of financial stress. We propose an endogenous regime-switching DSGE framework for efficient estimation and improved model fit.

This paper measures how both geographical and cultural proximity of bank branches affect household credit choice and pricing. For credit products that require high levels of ex-ante screening, we find that both proximities can complement each other in reducing the cost of providing soft information, thereby increasing credit access.

Inflation can affect both the dispersion of commodity-specific price levels across locations (relative price variability, RPV) and the dispersion of inflation rates (relative inflation variability, RIV). Some menu-cost models and models of consumer search suggest that the RIV-inflation relationship could differ from the RPV-inflation relationship.

We construct new indicators of demand and supply for the Canadian economy by using natural language processing techniques to analyze earnings calls of publicly listed firms. Our results indicate that the new indicators could help central banks identify inflationary pressures in real time.

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated.