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Enregistrement W1841245191

Rethinking the Lender of Last Resort: Workshop Summary

2014· article· de· W1841245191 sur OpenAlexaboutno aff
Dietrich Domanski, Vladyslav Sushko

Notice bibliographique

RevueSSRN Electronic Journal · 2014
Typearticle
Languede
DomaineEconomics, Econometrics and Finance
ThématiqueGlobal Financial Crisis and Policies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLender of last resortMarket liquidityFinancial systemMoral hazardBusinessBailoutFinancial crisisLiquidity crisisEconomicsPosition (finance)Central bankMonetary policyFinanceMonetary economicsIncentiveMarket economyMacroeconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Lender of last resort (LOLR) is perhaps a central bank’s most controversial role. On the one hand, emergency liquidity assistance to financial institutions is a core responsibility of central banks. This is because of central banks’ unique ability to create liquid assets in the form of central bank reserves, their central position within the payment system and their macroeconomic stabilisation objective. On the other hand, central bank LOLR is seen as very risky; as it potentially creates moral hazard on a massive scale, exposes the central bank to large financial risks, and blurs the boundary with fiscal policy. Moreover, liquidity assistance to individual institutions is typically deeply unpopular, creating reputation risks.The financial crisis served as a reminder of the critical importance of the LOLR in restoring financial stability. But it also raised fundamental questions about the design of LOLR frameworks and the execution of LOLR policies. How to strike the right balance between limiting risks for central banks and ensuring that the LOLR function can be performed effectively when needed? Should central banks be ambiguous in public about the terms and conditions of liquidity support? Or is there a case for well-articulated LOLR policies, communicated ex ante as part of a broader financial stability framework?This BIS workshop explored these issues, with a view to providing input into the discussions among central banks, and the public debate more generally. While there was broad agreement that liquidity support during the crisis was key in stabilising the global financial system, the discussions highlighted a number of challenges regarding LOLR policies. These included effective ways of dealing with stigma, questions regarding the design of LOLR policies in a market-based financial system, how to contain moral hazard, and issues of governance of LOLR policies, particularly against the backdrop of evolving financial stability frameworks. Finally, the question of optimal mechanisms for liquidity assistance in foreign currency remains an open one.The workshop was organised in three sessions plus a working lunch. The first session, chaired by Hiroshi Nakaso (Bank of Japan), reviewed the experience of major central banks with LOLR measures during the financial crisis. Bill Nelson (Federal Reserve) opened the discussion with an assessment of the Fed’s actions during the crisis. Francesco Papadia (Bruegel) continued the panel with a discussion of how the European Central Bank (ECB) addressed interbank liquidity shortages and wider market dysfunction during the crisis. Andrew Hauser reviewed the Bank of England’s experience during the crisis. Jose Sidaoui (former Bank of Mexico) provided the perspective of a major emerging market economy (EME), where the foreign exchange market served as a key transmission mechanism of liquidity stress. Hiroshi Nakaso concluded the first session with a review of Bank of 2 BIS Papers No 79 Japan experiences during the 1990s banking crises and new aspects of LOLR action that emerged during the recent financial crisis.The second session, chaired by Claudio Borio (BIS), discussed how post-crisis changes in the financial system affected the central bank’s role as LOLR. Perry Mehrling (Columbia University) led off with a discussion of new demands on LOLR associated with a market-based credit system. Lex Hoogduin (University of Amsterdam) discussed the relationship between the LOLR and self-insurance against liquidity risk. Morten Bech (BIS) presented several practical proposals for incorporating liquidity insurance through the central bank into bank liquidity regulation. Tim Lane (Bank of Canada) concluded the panel with a short summary of recent and ongoing work on collateral markets in the Committee on the Global Financial System (CGFS).Sir Paul Tucker (Harvard University) delivered the keynote speech at the working lunch. The speech and the ensuing discussion focused on the issues of LOLR governance. The third session, chaired by Hyun Song Shin (BIS), focused on the international dimensions of LOLR regimes. Jean-Pierre Landau (Sciences Po) opened the panel discussion with a proposal for a multilateral foreign currency liquidity arrangement that would reduce inefficient accumulation of foreign exchange reserves as a means to provide self-insurance. Giovanni Dell’Ariccia (IMF) discussed the relative merits of self-insurance through foreign exchange reserves, bilateral central bank swap arrangements, multilateral arrangements and IMF credit lines. Michael Dooley (University of California) then discussed the constraints that EME central banks faced in obtaining foreign currency insurance. Finally, Steve Cecchetti (Brandeis University) concluded the panel with a discussion of the implications of the US dollar’s role as a reserve currency for the design of international liquidity support arrangements.Full publication: Re-Thinking the Lender of Last Resort

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,020
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,123

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,020
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,004
Communication savante0,0130,009
Science ouverte0,0040,010
Intégrité de la recherche0,0130,017
Charge utile insuffisante (le modèle a refusé de juger)0,0370,010

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,224
Écart entre enseignants0,206 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2014
Routes d'admission1
Résumé présentoui

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