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Record W1841245191

Rethinking the Lender of Last Resort: Workshop Summary

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

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languagede
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsLender of last resortMarket liquidityFinancial systemMoral hazardBusinessBailoutFinancial crisisLiquidity crisisEconomicsPosition (finance)Central bankMonetary policyFinanceMonetary economicsIncentiveMarket economyMacroeconomics
DOInot available

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0130.009
Open science0.0040.010
Research integrity0.0130.017
Insufficient payload (model declined to judge)0.0370.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.224
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2014
Admission routes1
Has abstractyes

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