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Record W2883148935 · doi:10.17632/brggsjnn24.1

Data for: Volatility spillovers between foreign exchange and stock markets

2018· article· en· W2883148935 on OpenAlexaboutno aff
Simón Sosvilla‐Rivero

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)Foreign exchangeEconomicsMonetary economicsStock (firearms)Financial economicsStock exchangeEconometricsBusinessFinance

Abstract

fetched live from OpenAlex

The data consists of daily closing stock prices denominated in local currency for the US (Standard & Poor's 500 composite index, S&P500), the Euro area (Eurostoxx 50 Index), Japan (Nikkei 225 index), the UK (Financial Times Stock Exchange 100 Index, FTSE100), Australia (All Ordinaries Index, AOI), Switzerland (Swiss Market Index, SMI) and Canada (Toronto Stock Exchange Composite Index, TSX). The exchange rate series for each country is a trade-weighted exchange rate, to account for each country’s diverse investment positions in foreign equities. In particular, we examine the following effective exchange rates: US Dollar (USD), Euro (EUR), Australian dollar (AUD), Swiss franc (CHF), Canadian dollar (CAD), British pound (GBP) and Japanese yen (JPY). The stock price data has been extracted from Datastream. The exchange rate series are the Bank of England trade-weighted exchange rates.

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)0.001
metaresearch head score (Gemma)0.008
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.046
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0460.032

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 head0.070
GPT teacher head0.272
Teacher spread0.202 · 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
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

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