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Record W270108993

Three Facets of Liquidity Illusion: Financial Innovation and the Credit Crunch

2008· article· en· W270108993 on OpenAlexaboutno aff
Anastasia Nesvetailova

Bibliographic record

VenueGerman policy studies/Politikfeldanalyse · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsCredit crunchFinancial crisisFinancial systemRecessionMarket liquidityCrunchEconomicsGreat DepressionGlobal recessionDefaultLiquidity crisisBusinessFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Introduction In summer of 2007, a contagious liquidity meltdown hit world markets. Sparked by sub-prime mortgage fiasco in USA, financial panic and tumbling asset values did not only destabilise American financial system, but have also shaken European and Asian markets. Over course of following eighteen months, a contagious financial crisis has been transformed into a recession that increasingly becomes world-wide. Some analysts evaluate losses related to global credit crunch at around $2 trillion (Roubini 2008). The casualty count from global credit crunch has included high-profile firms like Bear Sterns, Lehman Brothers and AIG insurance firms in US, Northern Rock and Lloyds (HBOS) in UK, several European banks, companies in real economy, entire banking system of Iceland and crucially, growing numbers of people who have lost or are on brink of losing their homes and jobs. The crisis prompted unprecedented emergency measures by public authorities in USA, Japan, EU, and later Canada, Australia, UK and emerging markets. The sheer scale of monetary injections by central banks over course of crisis is unprecedented in economic history, as are levels of interest rates that currently are at their historical lows. As financial meltdown approaches its second anniversary and as newly revealed bank losses and defaults in non-financial sectors prompt fears a global depression, sceptics warn that more strains are hidden in complex pyramids of credit around world. In turn, insiders of securitisation market--for many years largest source of funding for mortgages and consumer credit--have come to believe that industry may not recover its levels of trade until 2011-2012 (van Duyn 2009). Financial crises are always costly for those involved; they tend to expose errors of both policy-making and financial practice. In this, global credit crunch is not a unique event. It has unmasked American sub-prime mortgage industry as a scam; it has revealed that many high-ranking financial institutions have been entangled in complex chains of dubious debts and even Ponzi schemes; (2) and it has also shown that public authorities have lost track of real effects of financial deregulation. At same time, while investor herding, exuberance, speculation and gap between regulatory oversight and spiral of private financial innovation have been present in most outbreaks of financial volatility during past twenty years, global credit crunch has brought up two perplexing issues concerning nature of today's finance in particular. The first puzzle is apparent shock of event. In summer of 2007, falling market values seemed to have caught many market players and observers by surprise. For instance, a lawyer for Mr Cioffi, one of managers of crippled Bear Sterns fund has argued: the credit crisis took everyone by surprise, including Fed and Treasury. Dozens of largest financial institutions in world have lost over $300 billion to date on same investments. (3) While treating crisis as a 'surprise' and shock may well be a trick of a skilled lawyer defending two financiers against nine-count indictment with conspiracy, securities and wire fraud, treating crisis as a 'surprise' does not make much sense outside courtroom. Indeed, risks unleashed and accentuated by securitisation process, as well as fragility of US mortgage market and economy as a whole had been noted repeatedly by many commentators long before turmoil began in summer of 2007. For instance, as William White of BIS observed, the opacity and complexity of financial system today shrouds in secrecy who finally bears risks, and increases likelihood of operational problems. More broadly, reliance of banks in many countries on revenues from dealing with household sector, already heavily indebted, could in future prove a source of financial vulnerability . …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.305
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2008
Admission routes1
Has abstractyes

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