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Record W3047397683 · doi:10.5267/j.ac.2020.7.010

Pricing model for Indonesia government bond

2020· article· en· W3047397683 on OpenAlexvenueno aff
Randi Bayu Prathama, Gracia Shinta S. Ugut Sugiarto, Edison Hulu

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsnot available
Fundersnot available
KeywordsBondEconomicsYield curveGovernment bondForward rateInterest rateBond valuationEconometricsTreasuryYield (engineering)Financial economicsAsset (computer security)Monetary economicsFinanceComputer science

Abstract

fetched live from OpenAlex

The yield curve is the building block of fair value in pricing bonds. It has been used by market participants for their asset valuations, Central Bank and Government's Treasury for monetary, interest rate and borrowing decisions. The official yield curve construction in Indonesia government bond is based on Svensson model which is widely accepted and used by several countries. The objective is to find more accurate fair price from yield curve as the alternative of IBPA government bond curve as the baseline. This research takes observation on three alternative yield curve models in comparison with the baseline Svensson Model to price several series of benchmark and non-benchmark bonds. Fair price is further tested with One-Way ANOVA and Post Hoc in order to find the significance between models. The results show that alternative models are performing better in determining the fair value prices of the government bonds compared to the baseline model.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.218
Teacher spread0.180 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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