C0 - General - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-29T15:46:01+00:00Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models
https://www.bankofcanada.ca/2022/09/staff-discussion-paper-2022-19/
This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. Computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models.2022-09-20T11:00:03+00:00enCalculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models2022-09-20Econometric and statistical methodsStaff Discussion Paper 2022-19https://www.bankofcanada.ca/wp-content/uploads/2022/09/sdp2022-19.pdfCalculating Effective Degrees of Freedom for Forecast Combinations and Ensemble ModelsJames YounkerSeptember 2022CC0C01C02C1C13C5C50C51C52C53Covariates Hiding in the Tails
https://www.bankofcanada.ca/2021/09/staff-working-paper-2021-45/
We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.2021-09-29T14:11:22+00:00enCovariates Hiding in the Tails2021-09-29Econometric and statistical methodsStaff Working Paper 2021-45https://www.bankofcanada.ca/wp-content/uploads/2021/09/swp2021-45.pdfStaff Working Paper 2021-45Milian BachemLerby ErgunCasper G. de VriesSeptember 2021CC0C01C1C14C5C58Classical Decomposition of Markowitz Portfolio Selection
https://www.bankofcanada.ca/2020/06/staff-working-paper-2020-21/
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.2020-06-05T13:51:15+00:00enClassical Decomposition of Markowitz Portfolio Selection2020-06-05Central bank researchStaff Working Paper 2020-21https://www.bankofcanada.ca/wp-content/uploads/2020/06/swp2020-21.pdfClassical Decomposition of Markowitz Portfolio SelectionChristopher DemoneOlivia Di MatteoBarbara CollignonJune 2020CC0C02Tail Index Estimation: Quantile-Driven Threshold Selection
https://www.bankofcanada.ca/2019/08/staff-working-paper-2019-28/
The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small.2019-08-02T10:52:22+00:00enTail Index Estimation: Quantile-Driven Threshold Selection2019-08-02Econometric and statistical methodsFinancial stabilityStaff Working Paper 2019-28https://www.bankofcanada.ca/wp-content/uploads/2019/08/swp2019-28.pdfTail Index Estimation: Quantile-Driven Threshold SelectionJon DanielssonLerby ErgunCasper G. de VriesLaurens de HaanAugust 2019CC0C01C1C14C5C58Characterizing the Canadian Financial Cycle with Frequency Filtering Approaches
https://www.bankofcanada.ca/2018/10/staff-analytical-note-2018-34/
In this note, I use two multivariate frequency filtering approaches to characterize the Canadian financial cycle by capturing fluctuations in the underlying variables with respect to a long-term trend. The first approach is a dynamically weighted composite, and the second is a stochastic cycle model.2018-10-26T12:28:48+00:00enCharacterizing the Canadian Financial Cycle with Frequency Filtering Approaches2018-10-26Challenges in Implementing Worst-Case Analysis
https://www.bankofcanada.ca/2018/09/staff-working-paper-2018-47/
Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.2018-09-14T08:50:24+00:00enChallenges in Implementing Worst-Case Analysis2018-09-14Financial stabilityStaff Working Paper 2018-47https://www.bankofcanada.ca/wp-content/uploads/2018/09/swp2018-47.pdfChallenges in Implementing Worst-Case AnalysisJon DanielssonLerby ErgunCasper G. de VriesSeptember 2018CC0C01C1C14C5C58Asymmetric Risks to the Economic Outlook Arising from Financial System Vulnerabilities
https://www.bankofcanada.ca/2018/03/staff-analytical-note-2018-6/
When financial system vulnerabilities are elevated, they can give rise to asymmetric risks to the economic outlook. To illustrate this, I consider the economic outlook presented in the Bank of Canada’s October 2017 Monetary Policy Report in the context of two key financial system vulnerabilities: high levels of household indebtedness and housing market imbalances.2018-03-14T10:15:59+00:00enAsymmetric Risks to the Economic Outlook Arising from Financial System Vulnerabilities2018-03-14Household Risk Assessment Model
https://www.bankofcanada.ca/2016/09/technical-report-106/
Household debt can be an important source of vulnerability to the financial system. This technical report describes the Household Risk Assessment Model (HRAM) that has been developed at the Bank of Canada to stress test household balance sheets at the individual level.2016-09-12T14:12:27+00:00frHousehold Risk Assessment Model2016-09-12Financial stabilityHousingSectoral balance sheetTechnical Report 106https://www.bankofcanada.ca/wp-content/uploads/2016/09/tr106.pdfHousehold Risk Assessment ModelBrian PetersonTom RobertsSeptember 2016CC0C6C63C65DD0D1D14The Canadian Debt-Strategy Model: An Overview of the Principal Elements
https://www.bankofcanada.ca/2011/05/discussion-paper-2011-3/
The Canadian Debt Strategy Model helps debt managers determine their optimal financing strategy. The model’s code and documentation are available to the public.2011-05-13T13:51:30+00:00enThe Canadian Debt-Strategy Model: An Overview of the Principal Elements2011-05-13Debt managementEconometric and statistical methodsFinancial marketsFiscal policyDiscussion Paper 2011-3https://www.bankofcanada.ca/wp-content/uploads/2011/05/dp11-3.pdfThe Canadian Debt-Strategy Model: An Overview of the Principal ElementsDavid BolderSimon DeeleyMay 2011CC0GG1G11G17HH6H63Financial Stress, Monetary Policy, and Economic Activity
https://www.bankofcanada.ca/2010/05/working-paper-2010-12/
This paper examines empirically the impact of financial stress on the transmission of monetary policy shocks in Canada. The model used is a threshold vector autoregression in which a regime change occurs if financial stress conditions cross a critical threshold.2010-05-21T13:24:59+00:00enFinancial Stress, Monetary Policy, and Economic Activity2010-05-21Financial stabilityMonetary policy and uncertaintyWorking Paper 2010-12https://www.bankofcanada.ca/wp-content/uploads/2010/05/wp10-12.pdfFinancial Stress, Monetary Policy, and Economic ActivityFuchun LiPierre St-AmantMay 2010CC0C01EE5E50GG0G01