G17 - Financial Forecasting and Simulation

• Finding the balance—measuring risks to inflation and to GDP growth

  Staff Analytical Note 2023-18 Bruno Feunou, James Kyeong

  Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17

• Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency

  Staff Discussion Paper 2023-19 Chinara Azizova, Bruno Feunou, James Kyeong

  This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17

• Forecasting Banks’ Corporate Loan Losses Under Stress: A New Corporate Default Model

  Technical Report No. 122 Gabriel Bruneau, Thibaut Duprey, Ruben Hipp

  We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

  Content Type(s): Staff research, Technical reports Topic(s): Economic models, Financial institutions, Financial stability, Financial system regulation and policies JEL Code(s): C, C2, C22, C5, C52, C53, G, G1, G17, G2, G21, G28

• Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning

  Staff Working Paper 2022-29 Vladimir Skavysh, Sofia Priazhkina, Diego Guala, Thomas Bromley

  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.

  Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Central bank research, Econometric and statistical methods, Economic models, Financial stability JEL Code(s): C, C1, C15, C6, C61, C63, C68, C7, E, E1, E13, G, G1, G17, G2, G21

• The potential effect of a central bank digital currency on deposit funding in Canada

  Staff Analytical Note 2020-15 Alejandro García, Bena Lands, Xuezhi Liu, Joshua Slive

  A retail central bank digital currency denominated in Canadian dollars could, in theory, create competition for bank deposit funding.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Digital currencies and fintech, Financial institutions, Financial stability JEL Code(s): E, E4, E41, E44, E5, G, G1, G10, G17, G2, G21, G3, G32, O

• A Counterfactual Valuation of the Stock Index as a Predictor of Crashes

  Staff Working Paper 2017-38 Tom Roberts

  Stock market fundamentals would not seem to meaningfully predict returns over a shorter-term horizon—instead, I shift focus to severe downside risk (i.e., crashes).

  Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Financial stability JEL Code(s): G, G0, G01, G1, G12, G17, G19

• Early Warning of Financial Stress Events: A Credit-Regime-Switching Approach

  Staff Working Paper 2016-21 Fuchun Li, Hongyu Xiao

  We propose an early warning model for predicting the likelihood of a financial stress event for a given future time, and examine whether credit plays an important role in the model as a non-linear propagator of shocks.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability JEL Code(s): C, C1, C12, C14, G, G0, G01, G1, G17

• Predicting Financial Stress Events: A Signal Extraction Approach

  Staff Working Paper 2014-37 Ian Christensen, Fuchun Li

  The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky, Lizondo and Reinhart (1998) is used to monitor the evolution of a number of economic indicators that tend to exhibit an unusual behaviour in the periods preceding a financial stress event.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability JEL Code(s): C, C1, C14, C4, E, E3, E37, E4, E47, F, F3, F36, F37, G, G0, G01, G1, G17

• A Semiparametric Early Warning Model of Financial Stress Events

  Staff Working Paper 2013-13 Ian Christensen, Fuchun Li

  The authors use the Financial Stress Index created by the International Monetary Fund to predict the likelihood of financial stress events for five developed countries: Canada, France, Germany, the United Kingdom and the United States.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability JEL Code(s): C, C1, C12, C14, G, G0, G01, G1, G17

• Jump-Diffusion Long-Run Risks Models, Variance Risk Premium and Volatility Dynamics

  Staff Working Paper 2013-12 Jianjian Jin

  This paper calibrates a class of jump-diffusion long-run risks (LRR) models to quantify how well they can jointly explain the equity risk premium and the variance risk premium in the U.S. financial markets, and whether they can generate realistic dynamics of risk-neutral and realized volatilities.

  Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Economic models JEL Code(s): G, G1, G12, G17