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  <title>EconStor Community: Bank of Canada, Ottawa</title>
  <link rel="alternate" href="https://hdl.handle.net/10419/53154" />
  <subtitle>Bank of Canada, Ottawa</subtitle>
  <id>https://hdl.handle.net/10419/53154</id>
  <updated>2026-04-28T14:09:20Z</updated>
  <dc:date>2026-04-28T14:09:20Z</dc:date>
  <entry>
    <title>Privacy-enhancing technologies for CBDC solutions</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/311792" />
    <author>
      <name>Arora, Rakesh</name>
    </author>
    <author>
      <name>Du, Han</name>
    </author>
    <author>
      <name>Kazmi, Raza Ali</name>
    </author>
    <author>
      <name>Le</name>
    </author>
    <author>
      <name>Duc-Phong</name>
    </author>
    <id>https://hdl.handle.net/10419/311792</id>
    <updated>2025-02-22T02:24:31Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Privacy-enhancing technologies for CBDC solutions
Authors: Arora, Rakesh; Du, Han; Kazmi, Raza Ali; Le; Duc-Phong
Abstract: With the rapid digitization of financial transactions, central banks have given considerable focus in recent years to the research and development of central bank digital currencies (CBDCs). While CBDCs could offer several advantages, there are concerns about end-user privacy. Traditional methods of protecting confidentiality in banking and financial systems have primarily relied on data encryption and access control techniques. However, these techniques alone are inadequate, especially in cases where data are shared across different entities because privacy in such situations is typically governed by legal frameworks. Privacy-enhancing technologies (PETs) can offer robust protection for data throughout their lifecycle, whether stored, in transit or during processing, and ensure privacy is maintained even when data are extensively shared or analyzed. This study explores the use of PETs in the design of CBDC systems, potentially paving the way for solutions that better safeguard end-user privacy and meet rigorous data protection standards. While PETs promise significant advancements in privacy protection, they present some challenges in implementation. They can introduce performance overheads and add complexity to systems, and their effectiveness and applicability are currently limited due to their early stage of development. As these technologies evolve, it is crucial for organizations to carefully consider these factors to fully leverage PET benefits while managing associated challenges. This paper provides a comprehensive overview of how PETs can transform privacy design in financial systems and the implications of their broader adoption.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Quantile VARs and macroeconomic risk forecasting</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/311806" />
    <author>
      <name>Surprenant, Stéphane</name>
    </author>
    <id>https://hdl.handle.net/10419/311806</id>
    <updated>2025-02-22T02:09:20Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Quantile VARs and macroeconomic risk forecasting
Authors: Surprenant, Stéphane
Abstract: Recent rises in macroeconomic volatility have prompted the introduction of quantile vector autoregression (QVAR) models to forecast macroeconomic risk. This paper provides an extensive evaluation of the predictive performance of QVAR models in a pseudo-out-of-sample experiment spanning 112 monthly US variables over 40 years, with horizons of 1 to 12 months. We compare QVAR with three parametric benchmarks: a Gaussian VAR, a generalized autoregressive conditional heteroskedasticity VAR and a VAR with stochastic volatility. QVAR frequently, significantly and quantitatively improves upon the benchmarks and almost never performs significantly worse. Forecasting improvements are concentrated in the labour market and interest and exchange rates. Augmenting the QVAR model with factors estimated by principal components or quantile factors significantly enhances macroeconomic risk forecasting in some cases, mostly in the labour market. Generally, QVAR and the augmented models perform equally well. We conclude that both are adequate tools for modeling macroeconomic risks.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Tech reluctance: Fostering empathy for Canadians facing challenges with digital systems</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/311794" />
    <author>
      <name>Hernandez, Sebastian</name>
    </author>
    <author>
      <name>Wang, Helena</name>
    </author>
    <author>
      <name>Omrane, Badr</name>
    </author>
    <author>
      <name>Roberts, Vera</name>
    </author>
    <author>
      <name>Pereyra, David</name>
    </author>
    <id>https://hdl.handle.net/10419/311794</id>
    <updated>2025-02-22T02:11:51Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Tech reluctance: Fostering empathy for Canadians facing challenges with digital systems
Authors: Hernandez, Sebastian; Wang, Helena; Omrane, Badr; Roberts, Vera; Pereyra, David
Abstract: Designing inclusive and user-friendly digital payment systems is crucial to eliminate barriers faced by users. This research focuses on fostering empathy for and identifying the needs of users who exhibit behaviours that indicate they encounter accessibility or usability barriers in digital systems. Specifically, we examine two types of users based on two common behaviours: users who rely on others to perform tasks and those who avoid interacting with technology. The Bank of Canada partnered with the Inclusive Design Research Centre at OCAD University to gain a deeper understanding of these groups. Co-design sessions with end users were used to identify scenarios when cooperative efforts are needed, system features that facilitate supported banking and pain points customers and their support people encounter. The findings show that individuals in the two groups avoid systems they expect lack usability. Addressing these issues through standard accessibility practices, live assistance and thoughtful interface design can enhance user interaction and trust. For accessibility issues that cannot realistically be eliminated, technology that enhances cooperative relationships and allows account owners to control information sharing is key.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Differentiable, filter free Bayesian estimation of DSGE models using mixture density networks</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/311805" />
    <author>
      <name>Naubert, Christopher</name>
    </author>
    <id>https://hdl.handle.net/10419/311805</id>
    <updated>2025-02-22T02:13:41Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: Differentiable, filter free Bayesian estimation of DSGE models using mixture density networks
Authors: Naubert, Christopher
Abstract: I develop a methodology for Bayesian estimation of globally solved, non-linear macroeconomic models. A novel feature of my method is the use of a mixture density network to approximate the distribution of initial states. I use the methodology to estimate a medium-scale, two-agent New Keynesian model with irreversible investment and a zero lower bound on nominal interest rates. Using simulated data, I show that the method is able to recover the "true" parameters when using the mixture density network approximation of the initial state distribution. This contrasts with the case when the initial states are set to their steady-state values.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
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