MODEL BLACK-SCHOLES OPSI CALL DAN OPSI PUT TIPE EROPA DENGAN DIVIDEN PADA KEADAAN CONSTANT MARKET
Bibliographic record
Abstract
Opsi tipe Eropa adalah suatu bentuk perjanjian berupa kontrak yang memberikan pemegang opsi suatu hak tetapi bukan suatu kewajiban untuk membeli atau menjual aset tertentu dengan harga tertentu pada waktu jatuh tempo. Opsi call memberikan hak kepada pemegang opsi untuk membeli saham pada waktu jatuh tempo. Sementara opsi put memberikan hak untuk menjual saham. Metode Black-Scholes merupakan salah satu metode untuk menentukan harga opsi. Asumsi yang digunakan pada model ini adalah adanya pembagian dividen. Dividen dibayarkan pada keadaan constant market. Harga saham yang berubah secara acak menurut waktu diasumsikan sebagai proses stokastik. Prediksi harga saham diasumsikan hanya dipengaruhi oleh harga saham saat ini dan tidak dipengaruhi oleh harga saham di masa lampau. Perhitungan harga opsi saham chevron corporation pada tanggal 16 November 2016 dengan mengaplikasikan model Black-Scholes. Hasil yang diperoleh menunjukkan bahwa pada keadaan constant market sebaiknya investor membeli opsi put di pasar saham dengan harga opsi yang lebih kecil dari harga opsi model Black-Scholes yaitu pada harga pelaksanaan 101, 102, 104, 105, 106, 107 dan 108, sedangkan untuk opsi call sebaiknya investor membeli opsi call di pasar saham untuk harga pelaksanaan 100 dan 101.Kata Kunci: Opsi tipe Eropa, Opsi call, Opsi put, Proses Stokastik, Dividen, Constant Market, Model Black-Scholes
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".