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Record W3033700513 · doi:10.1016/j.heliyon.2020.e03980

A common risk factor in global credit and equity markets: An exploratory analysis of the subprime and the sovereign-debt crises

2020· article· en· W3033700513 on OpenAlexaboutno aff
Javier Márquez, Laura Gismera Tierno, Sara Lumbreras

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
FundersTulane University
KeywordsFinancial systemEquity (law)Credit riskBusinessSovereign debtSovereigntyCredit derivativeDebtFinancial economicsEconomicsCredit historyFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

This paper investigates the existence of a common risk factor across asset classes and geographical areas, focusing on the crises and post-crisis periods. This factor has important implications for diversification in investor's portfolios. We assess a worldwide sample of assets: Equity, Corporate CDS and Sovereign CDS from fourteen countries across Europe, US and Asia, and focus the analysis to a time window where diversification was crucial: the crises and post-crisis periods. To identify the factors that underlie asset movements and their composition, a Principal Component Analysis (PCA) is applied. We find that there is supporting evidence for the existence of a common risk factor that underlies 86 percent of our sample' assets movements and reflects a global non-diversifiable risk that permeates the financial system. The uncovered risk factor is robust across periods, and it is evenly distributed across assets and countries, with the noticeable exception of Japan, which follows a divergent risk pattern. This is also true, to a lesser extent, for the US, Canada and China. Within the Eurozone financial assets a higher commonality is uncovered. In addition, we confirm that the common risk factor becomes more important in times of crisis. The existence of a common risk factor limits the possibilities of diversification, in particular during turmoil periods when correlations among assets' movements rise. However, the fact that some geographies display a lower commonality can be used to improve the risk profile of diversified portfolios.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.045
GPT teacher head0.262
Teacher spread0.216 · 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 designObservational
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

Citations6
Published2020
Admission routes1
Has abstractyes

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