Browse research

I study a model of competing data intermediaries (e.g., online platforms and data brokers) that collect personal data from consumers and sell it to downstream firms.

Can Bitcoin survive? Some say it will become vulnerable to attacks as the rewards for processing Bitcoin transactions continue to decline. The economics of fixed costs suggest the specialized hardware used to mine Bitcoin may be key to its survival.

Canada plans to adopt a retail payment system to allow Canadians to pay in real time (or near real time) 24 hours a day, 7 days a week. However, the traditional model for setting the overnight interest rate does not operate 24/7.

We propose a new strength measure of the global financial cycle by estimating a regime-switching factor model on cross-border equity flows for 61 countries. We then assess how the strength of the global financial cycle affects monetary policy independence, which is defined as the response of central banks' policy interest rates to exogenous changes in inflation.

Consumers, businesses and banks make millions of payments each day using a variety of instruments, such as debit cards, cheques and wires. Canada is currently developing three new systems to process these transactions: Lynx, Settlement Optimization Engine (SOE) and Real-Time Rail (RTR).

In times of high uncertainty, governments often implement interventions such as bailouts to financial institutions. To use public resources efficiently and to avoid major spillovers to the rest of the economy, policy-makers try to identify which institutions should receive assistance.

Repeated interactions between borrowers and lenders create the possibility of dynamic pricing: lenders compete aggressively with low prices to attract new borrowers and then raise their prices once borrowers have made a commitment. We find such pricing patterns in the Canadian mortgage market.

In this study, we enhance Markowitz portfolio selection with graph theory for the analysis of two portfolios composed of either EU or US assets. Using a threshold-based decomposition of their respective covariance matrices, we perturb the level of risk in each portfolio and build the corresponding sets of graphs.

Investors who trade based on good research are said to be the backbone of stock markets: They conduct research to discover the value of stocks and, through their trading, guide financial prices to reflect true value. What can make their job difficult is that high-speed, short-term traders could use machine learning and other technologies to infer when informed investors are trading.

We investigate the uncertainty around stock returns at different investment horizons. Since a return is either a loss or a gain, we categorize return uncertainty into two components—loss uncertainty and gain uncertainty. We then use these components to evaluate investment.