Xiangjin Shen - Latest - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T18:34:10+00:00Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models
https://www.bankofcanada.ca/2022/07/staff-working-paper-2022-31/
We use graphic processing unit computing to compare Bayesian and sample theory semiparametric binary response models. Our findings show that optimal bandwidth does not outperform regular bandwidth in binary semiparametric models.2022-07-04T11:20:56+00:00enComparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models2022-07-04Credit risk managementEconometric and statistical methodsStaff Working Paper 2022-31https://www.bankofcanada.ca/wp-content/uploads/2022/07/swp2022-31.pdfComparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response ModelsXiangjin ShenIskander KaribzhanovHiroki TsurumiShiliang LiJuly 2022CC1C14C3C35C5C51C6C63DD1How well can large banks in Canada withstand a severe economic downturn?
https://www.bankofcanada.ca/2022/05/staff-analytical-note-2022-6/
We examine the potential impacts of a severe economic shock on the resilience of major banks in Canada. We find these banks would suffer significant financial losses but nevertheless remain resilient. This underscores the role well-capitalized banks and sound underwriting practices play in supporting economic activity in a downturn.2022-05-24T15:00:17+00:00enHow well can large banks in Canada withstand a severe economic downturn?2022-05-24Assessing Climate-Related Financial Risk: Guide to Implementation of Methods
https://www.bankofcanada.ca/2022/01/technical-report-120/
A pilot project on climate transition scenarios by the Bank of Canada and the Office of the Superintendent of Financial Institutions assessed climate-related credit and market risks. This report describes the project’s methodologies and provides guidance on implementing them.2022-01-14T12:00:37+00:00enAssessing Climate-Related Financial Risk: Guide to Implementation of Methods2022-01-14Climate changeCredit and credit aggregatesEconometric and statistical methodsFinancial stabilityTechnical Report 2022-120https://www.bankofcanada.ca/wp-content/uploads/2021/11/tr120.pdfHossein HosseiniCraig JohnstonCraig LoganMiguel MolicoXiangjin ShenMarie-Christine TremblayJanuary 2022CC5C53C8C83GG1G3G32Assessing the Resilience of the Canadian Banking System
https://www.bankofcanada.ca/2019/05/staff-analytical-note-2019-16/
The stability of the Canadian financial system, as well as its ability to support the Canadian economy, depends on the ability of financial institutions to absorb and manage major shocks. This is especially true for large banks, which perform services essential to the Canadian economy.2019-05-16T06:00:33+00:00enAssessing the Resilience of the Canadian Banking System2019-05-16Modelling the Macrofinancial Effects of a House Price Correction in Canada
https://www.bankofcanada.ca/2018/11/staff-analytical-note-2018-36/
We use a suite of risk-assessment models to examine the possible impact of a hypothetical house price correction, centred in the Toronto and Vancouver areas. We also assume financial stress significantly amplifies the macroeconomic impact of the house price decline.2018-11-14T09:00:37+00:00enModelling the Macrofinancial Effects of a House Price Correction in Canada2018-11-14Financial System Resilience and House Price Corrections
https://www.bankofcanada.ca/2018/11/financial-system-resilience-and-house-price-corrections/
We use models to better understand and assess how risks could affect the financial system. In our hypothetical scenario, a house price correction and elevated financial stress weigh on the economy. An increased number of households and businesses have difficulty repaying loans. Nonetheless, the large banks remain resilient.2018-11-14T08:00:49+00:00enFinancial System Resilience and House Price Corrections2018-11-14Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement
https://www.bankofcanada.ca/2018/05/staff-working-paper-2018-21/
We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms.2018-05-14T14:30:19+00:00enAnalysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement2018-05-14Econometric and statistical methodsPayment clearing and settlement systemsStaff Working Paper 2018-21https://www.bankofcanada.ca/wp-content/uploads/2018/05/swp2018-21.pdfAnalysis of Asymmetric GARCH Volatility Models with Applications to Margin MeasurementElena GoldmanXiangjin ShenMay 2018CC5C58GG1G19G2G23G28