Calibrating Macroprudential Policies for the Canadian Mortgage Market
Notice bibliographique
Résumé
Macroprudential regulation has been on the rise since the 2007–09 global financial crisis. In Canada, the primary policy tools that have been employed in this regard are related to the residential housing market – namely, changes in mortgage loan-to-value ratios and loan maturity requirements. In this Commentary, we use an analytical model to forecast the probability of a state of low financial stability in the Canadian economy and recommend when policy action might be taken in light of its costs and benefits. We project a low probability of low financial stability in Canada that rises gradually through year-end 2020, but remains low. This might seem odd given recent events around COVID-19. However, there are two things for readers to keep in mind. First, COVID-19 is a black swan event occurring in the real economy, one that does not originate in financial markets, making it difficult for financial regulators and policymakers to anticipate and model in advance. This is critical, as the goal of our paper is to provide a modeling tool to do just that. Second, once we have entered a downturn, financial regulators will not tighten a policy to head off financial instability. They will, in fact, do the opposite, by loosening policy rules to try and stimulate the economy. Canada is an interesting case with respect to financial stability concerns and policies. Although the Canadian economy was able to stave off many of the negative effects of the last financial crisis, it continues to have growing levels of household debt. As a result, after loosening housing-related macroprudential policies in the lead-up to the crisis, policymakers have spent much of the past decade tightening these same policies. Despite work analyzing the effects of housing-related macroprudential policies, there has been very little focus on advising policymakers about when to implement them. Any such advice naturally begins with identifying occasions when financial stability concerns are prominent and likely to remain so, which we refer to as “low financial stability states.” The model identifies three such episodes in Canada between 1990 and the middle of 2019: the early 1990s recession, the mid-1990s government budget rebalancing and the 2008 financial crisis. The four Financial Stability Indicators (FSIs) in our model specifications are the house-priceto-rent ratio, the price-to-income ratio, the debt-servicing ratio and the household-credit-to-GDP ratio. We then use the model to forecast the probability of entering another such episode over a two-year policy horizon. The model provides an answer to the question of whether the probability of entering and staying in a low financial stability state is high enough to go ahead with the policy, given the cost of implementation. Our analysis suggests that, as of the second quarter of 2019, and abstracting from the black swan COVID-19 event, the probability of a lengthy period of low financial stability is low, extending to late 2020.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».