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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.

This article examines the incentives for banks to hold various assets on their balance sheets for use as collateral when the opportunity cost of doing so can be high. Focusing on the five-year period (2002-07) that preceded the financial crisis, it examines the choices made by financial institutions among the assets that are pledged as collateral in Canada's Large Value Transfer System. This serves as a baseline for collateral-management practices during relatively normal times. The results of this study are important for policy-makers, especially the Bank of Canada, which is concerned both about the efficient functioning of fixed-income markets and about the credit risk it ultimately bears in insuring LVTS settlement. The results suggest that relative market liquidity and market-making capacity are important factors in the choice of securities pledged as collateral in the LVTS.

Are summers getting hotter? Do daily temperatures change more than they used to? Using daily Canadian temperature data from 1960 to 2020 and modern econometric methods, we provide economists and policy-makers evidence on the important climate change issue of evolving temperatures.

This paper studies the relationship between bank holding company affiliation and the individual and systemic risk of banks. Using the 2005 hurricane season in the US as an exogenous shock to bank balance sheets, we show that banks that are part of a holding parent company are more resilient than independent banks.

We develop statistical inferences for a non-probability two-phase survey sample when relevant auxiliary information is available from a probability survey sample. The proposed method is assessed by simulation studies and used to analyze a non-probability two phase payment survey.

We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does.

This paper shows point identification in first-price auction models with risk aversion and unobserved auction heterogeneity by exploiting multiple bids from each auction and variation in the number of bidders. The required exclusion restriction is shown to be consistent with a large class of entry models.