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Record W2107856774 · doi:10.5539/ijef.v4n2p217

The Forecasting Power of the Volatility Index in Emerging Markets: Evidence from the Taiwan Stock Market

2012· article· en· W2107856774 on OpenAlexvenueno aff
Ming Jing Yang, Meng-Yi Liu

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)Volatility swapVolatility smileEconomicsFinancial economicsPredictive powerStock marketImplied volatilityVolatility risk premiumStock market indexForward volatilityStock (firearms)Econometrics

Abstract

fetched live from OpenAlex

This paper explores the predictive power of the volatility index (VIX) in emerging markets from December 2006 to March 2010. The results of the study show that the models including both the volatility indicator and the option market information have a stronger predictive power. The predictive power of the models is improved by 88% in explaining the future volatility of stock markets, much better than that of other models merely considering the volatility indicator. With respect to the trading information from different types of investors in option markets, the trading information from the foreign institutional investors in option markets demonstrates a significantly positive relationship with the stock market volatility. In addition, the results of this paper also reveal that the volatility index (TVIX) of Taiwan stock index options is a strong indicator of future stock market volatility. The TVIX outperforms the historical volatility and the GARCH volatility forecast in assessing the activities of Taiwan’s stock market.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.232
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.239
Teacher spread0.207 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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