Staff working papers

We analyze how network effects affect competition in the nascent cryptocurrency market. We do so by examining the changes over time in exchange rate data among cryptocurrencies.

Guided by a macroeconomic model in which non-energy commodity prices are endogenously determined, we apply a new factor-based identification strategy to decompose the historical sources of changes in commodity prices and global economic activity.

This paper assesses the effectiveness and associated externalities that arise when macroprudential policies (MPPs) are used to manage international capital flows. Using a sample of up to 139 countries, we examine the impact of eight different MPP measures on cross-border bank flows over the period 1999-2009.

We propose a parsimonious model of information choice in a global coordination game of regime change that is used to analyze debt crises, bank runs or currency attacks. A change in the publicly available information alters the uncertainty about the behavior of other investors.

A major policy challenge posed by derivatives clearinghouses is that their collateral requirements can rise sharply in times of stress, reducing market liquidity and further exacerbating downturns.

This paper studies the formation of consumers’ inflation expectations using micro-level data from the Michigan Survey. It shows that beyond the well-established socio-economic determinants of inflation expectations such as gender, income or education, other characteristics such as the households’ financial situation and their purchasing attitudes also matter.

We exploit the panel dimension of the Canadian Financial Monitor (CFM) data to estimate the impact of retail payment innovations on cash usage. We estimate a semiparametric panel data model that accounts for unobserved heterogeneity and allows for general forms of non-random attrition.

The network pattern of financial linkages is important in many areas of banking and finance. Yet bilateral linkages are often unobserved, and maximum entropy serves as the leading method for estimating counterparty exposures.

Information on the allocation and pricing of over-the-counter (OTC) markets is scarce. Furfine (1999) pioneered an algorithm that provides transaction-level data on the OTC interbank lending market.

Estimation of the quantile model, especially with a large data set, can be computationally burdensome. This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model.