Rethinking Monetary Policy Transmission : Nonbank Finance, Central Bank Digital Currencies, and the Role of Bank Market Power
Notice bibliographique
Résumé
Essay I I study the transmission of monetary policy through banks and nonbank financial intermediaries (NBFIs) in the United States. First, I construct a dataset on nonbank financial intermediation that accounts for the various linkages between financial intermediaries. I empirically demonstrate that following monetary policy tightenings, U.S. households substitute bank deposits with nonbank-created liquidity. Bank lending contracts while nonbank intermediation expands. Second, to explain these empirical findings, I develop a New-Keynesian model incorporating both banks and nonbanks. Banks face a liquidity constraint that limits their ability to issue debt, a restriction that nonbanks do not encounter. As the policy rate rises, banks keep their deposit rates relatively low while nonbanks increase the returns on their liabilities in tandem with the policy rate. This generates the household portfolio rebalancing and the shift toward nonbank finance observed in the data. Moreover, I show that in the absence of nonbank financial intermediation, the economic contraction following a monetary tightening is deeper. These findings suggest that the presence of NBFIs weakens the effect of monetary policy. Essay II (with Matthias Hänsel and Hiep Nguyen): Interest rates on new central bank digital currencies (CBDCs) can be expected to enter the monetary policy toolkit soon. Using an extended Sidrauski (1967) model featuring an oligopsonistic banking sector, we study the complex transmission of interest rates on CBDC, which generally involve both direct and indirecteffects. This is because a CBDC rate cut does not only affect the rate on the CBDC itself, but also induces the non-competitive deposit providers to adjust their spreads, as the new substitute for their products becomes relatively less attractive. A calibration exercise suggests that the indirect effects depend strongly on the sources of deposit market power: If driven by high concentration, they substantially amplify the aggregate effects of the CBDC policy rate, both in response to transitory shocks as well as regarding its long-run welfare effects. This contrasts them with policies directed at the banking sector which are weakened by a less competitive deposit market. Essay III (with Maria Elena Filippin): We examine the risks to bank intermediation following the introduction of a central bank digital currency (CBDC). In our framework, CBDC competes with commercial bank deposits as a household liquidity source, and commercial banks can secure central bank funding by posting collateral. First, we revisit the equivalence results of Brunnermeier and Niepelt (2019) and Niepelt (2022). We show that the central bank can, even in the presence of a collateral constraint, ensure the same equilibrium allocation and price system following the introduction of a CBDC by offering loans to banks. However, to access the loans, banks must hold collateral at the expense of extending credit to firms. Thus, while the government can ensure that the introduction of CBDC has no real effects on the economy, it does not guarantee "full neutrality" as the portfolio and policy changes affect banks' business models. Second, we study the dynamic responses of the economy to a near-permanent increase in CBDC, without imposing equivalence. In this case, we find that the introduction of CBDC need not disintermediate banks, but could in fact expand the banks' credit to firms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».