New technologies and new players in the financial technology (fintech) sector could affect the financial system, and this impact could in turn affect how the Bank of Canada works to fulfill its core functions. For this reason, we are closely monitoring fintech developments. Bank staff are researching potential implications of fintech and the Bank is a founding member of the Blockchain Research Institute.
As the sole issuer of bank notes, the Bank of Canada conducts Methods-of-Payment (MOP) surveys to obtain a detailed and representative snapshot of Canadian payment choices, with a focus on cash usage. The 2017 MOP Survey is the third iteration. This paper finds that the overall cash volume and value shares are 33 per cent and 15 per cent, respectively.
Should a central bank take over the provision of e-money, a circulable electronic liability? We discuss how e-money technology changes the tradeoff between public and private provision, and the tradeoff between e-money and a central bank's existing liabilities like bank notes and reserves.
The use of bank notes in Canada for payments has declined consistently for some time, and similar trends are evident in other countries. This has led some observers to predict a cashless society in the future.
Can securities be settled on a blockchain and, if so, what are the gains relative to existing settlement systems? We consider a blockchain that ensures delivery versus payment by linking transfers of assets with payments and operates using a proof-of-work protocol. The main benefit of a blockchain is faster and more flexible settlement, whereas the challenge is to avoid settlement fails when participants fork the chain to get rid of trading losses.
A blockchain is a digital ledger that keeps track of a record of ownership without the need for a designated party to update and enforce changes to the record. The updating of the ledger is done directly by the users of the blockchain and is traditionally governed by a proof-of-work (PoW) protocol.
This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent common-factor structure.