C7 - Game Theory and Bargaining Theory - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-29T06:11:20+00:00Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning
https://www.bankofcanada.ca/2022/06/staff-working-paper-2022-29/
Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.2022-06-28T15:47:31+00:00enQuantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning2022-06-28Business fluctuations and cyclesCentral bank researchEconometric and statistical methodsEconomic modelsFinancial stabilityStaff Working Paper 2022-29https://www.bankofcanada.ca/wp-content/uploads/2022/06/swp2022-29.pdfStaff Working Paper 2022-29Vladimir SkavyshSofia PriazhkinaDiego GualaThomas BromleyJune 2022CC1C15C6C61C63C68C7EE1E13GG1G17G2G21Vertical Bargaining and Obfuscation
https://www.bankofcanada.ca/2022/03/staff-working-paper-2022-13/
Is obscuring prices always bad for consumers? The answer depends on the market structure and on the negotiating power between manufacturers and retailers.2022-03-21T07:40:05+00:00enVertical Bargaining and Obfuscation2022-03-21Economic modelsMarket structure and pricingStaff Working Paper 2022-13https://www.bankofcanada.ca/wp-content/uploads/2022/03/swp2022-13.pdfVertical Bargaining and ObfuscationEdona ReshidiMarch 2022CC7C70LL1L13L4L42Stressed but not Helpless: Strategic Behaviour of Banks Under Adverse Market Conditions
https://www.bankofcanada.ca/2021/07/staff-working-paper-2021-35/
Our stress-testing tool considers banks under stress that can strategically manage their balance sheets. Using confidential Canadian supervisory data, we assess whether bank behaviour to maximize shareholder value can amplify a hypothetical stress scenario.2021-07-20T09:01:22+00:00enStressed but not Helpless: Strategic Behaviour of Banks Under Adverse Market Conditions2021-07-20Central bank researchEconomic modelsFinancial institutionsFinancial stabilityFinancial system regulation and policiesStaff Working Paper 2021-35https://www.bankofcanada.ca/wp-content/uploads/2021/07/swp2021-35.pdfStaff Working Paper 2021-35Grzegorz HalajSofia PriazhkinaJuly 2021CC6C63C7C72GG2G21Estimating Policy Functions in Payments Systems Using Reinforcement Learning
https://www.bankofcanada.ca/2021/02/staff-working-paper-2021-7/
We demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information.2021-02-01T09:44:50+00:00enEstimating Policy Functions in Payments Systems Using Reinforcement Learning2021-02-01Digital currencies and fintechFinancial institutionsFinancial system regulation and policiesPayment clearing and settlement systemsStaff Working Paper 2021-7https://www.bankofcanada.ca/wp-content/uploads/2021/02/swp2021-7.pdfEstimating Policy Functions in Payments Systems Using Reinforcement LearningPablo S. CastroAjit DesaiHan DuRodney J. GarrattFrancisco RivadeneyraFebruary 2021AA1A12CC7DD8D83EE4E42E5E58Interbank Asset-Liability Networks with Fire Sale Management
https://www.bankofcanada.ca/2020/09/staff-working-paper-2020-41/
Raising liquidity when funding is stressed creates pressure on the financial market. Liquidating large quantities of assets depresses their prices and may amplify funding shocks. How do banks weathering a funding crisis contribute to contagion risk?2020-09-28T14:30:46+00:00enInterbank Asset-Liability Networks with Fire Sale Management2020-09-28Financial stabilityFinancial system regulation and policiesPayment clearing and settlement systemsStaff Working Paper 2020-41https://www.bankofcanada.ca/wp-content/uploads/2020/09/swp2020-41.pdfStaff Working Paper 2020-41Zachary FeinsteinGrzegorz HalajSeptember 2020CC6C62C63C7C72GG0G01G1G11Monetary Payoff and Utility Function in Adaptive Learning Models
https://www.bankofcanada.ca/2019/12/staff-working-paper-2019-50/
When players repeatedly face an identical or similar game (e.g., coordination game, technology adoption game, or product choice game), they may learn through experience to perform better in the future. This learning behaviour has important economic implications.2019-12-20T15:53:25+00:00enMonetary Payoff and Utility Function in Adaptive Learning Models2019-12-20Econometric and statistical methodsEconomic modelsStaff Working Paper 2019-50https://www.bankofcanada.ca/wp-content/uploads/2019/12/swp2019-50.pdfMonetary Payoff and Utility Function in Adaptive Learning ModelsErhao XieDecember 2019CC5C57C7C72C9C92An Application of Shapley Value Cost Allocation to Liquidity Savings Mechanisms
https://www.bankofcanada.ca/2019/07/staff-working-paper-2019-26/
Liquidity demands in real-time gross settlement payment systems can be enormous. To reduce the liquidity requirement, central banks around the world have implemented liquidity savings mechanisms (LSMs).2019-07-29T16:51:09+00:00enAn Application of Shapley Value Cost Allocation to Liquidity Savings Mechanisms2019-07-29Payment clearing and settlement systemsStaff Working Paper 2019-26https://www.bankofcanada.ca/wp-content/uploads/2019/07/swp2019-26.pdfAn Application of Shapley Value Cost Allocation to Liquidity Savings MechanismsRodney J. GarrattJuly 2019CC7C72EE5E58The Framework for Risk Identification and Assessment
https://www.bankofcanada.ca/2018/11/technical-report-113/
Risk assessment models are an important component of the Bank’s analytical tool kit for assessing the resilience of the financial system. We describe the Framework for Risk Identification and Assessment (FRIDA), a suite of models developed at the Bank of Canada to quantify the impact of financial stability risks to the broader economy and a range of financial system participants (households, businesses and banks).2018-11-13T11:51:05+00:00enThe Framework for Risk Identification and Assessment2018-11-13Economic modelsFinancial institutionsFinancial stabilityHousingTechnical Report 113https://www.bankofcanada.ca/wp-content/uploads/2018/11/tr113.pdfThe Framework for Risk Identification and AssessmentCameron MacDonaldVirginie TracletNovember 2018CC3C5C6C7DD1EE0E00E2E27E3E37E4E47GG0G2G21The MacroFinancial Risk Assessment Framework (MFRAF), Version 2.0
https://www.bankofcanada.ca/2017/09/technical-report-111/
This report provides a detailed technical description of the updated MacroFinancial Risk Assessment Framework (MFRAF), which replaces the version described in Gauthier, Souissi and Liu (2014) as the Bank of Canada’s stress-testing model for banks with a focus on domestic systemically important banks (D-SIBs).2017-09-14T11:59:57+00:00enThe MacroFinancial Risk Assessment Framework (MFRAF), Version 2.02017-09-14Financial stabilityFinancial system regulation and policiesTechnical Report No. 111https://www.bankofcanada.ca/wp-content/uploads/2017/09/tr111.pdfThe MacroFinancial Risk Assessment Framework (MFRAF), Version 2.0Jose FiqueSeptember 2017CC7C72EE5E58GG0G01G2G21G28Stability and Efficiency in Decentralized Two‐Sided Markets with Weak Preferences
https://www.bankofcanada.ca/2017/02/staff-working-paper-2017-4/
Many decentralized markets are able to attain a stable outcome despite the absence of a central authority (Roth and Vande Vate, 1990). A stable matching, however, need not be efficient if preferences are weak. This raises the question whether a decentralized market with weak preferences can attain Pareto efficiency in the absence of a central matchmaker.2017-02-15T12:57:17+00:00enStability and Efficiency in Decentralized Two‐Sided Markets with Weak Preferences2017-02-15Economic modelsStaff Working Paper 2017-4https://www.bankofcanada.ca/wp-content/uploads/2017/02/swp2017-4.pdfStability and Efficiency in Decentralized Two‐Sided Markets with Weak PreferencesRadoslav RaykovFebruary 2017CC7C78DD6D61