The Volume of Issuance of Government Islamic Securities SR-007 Series, 2015–2018
Bibliographic record
Abstract
In Islamic finance, Sukuk instruments are similar to the bond market in conventional finance and are intended to increase long-term investment capital. However, according to Sharia, Sukuk means there is no uncertainty (Garar), interest (usury) and gambling (Maisir).This is also new in the financial market in Islamic Bank. The government uses Sukuk as an instrument for financing the state budget, and Sukuk financing has contributed to the development and financing of state projects. The amount of Sukuk issuance volume is influenced by external factors such as macroeconomic conditions and internal factors, namely prices and yields of Sukuk. The problem is whether inflation, exchange rates, Sukuk prices and returns affect the volume of Sukuk issuance. Therefore, this research aims to determine the effect of macroeconomic variables (inflation and exchange rates), Sukuk prices and Sukuk yields on the volume of Retail Sukuk issuance in Indonesia, using the Vector Error Correction Model (VECM) research model and the assistance of econometrics EViews 9. The data used in this study are time-series data from April 2015 to March 2018, with a research sample of Retail Sukuk with the latest maturity in March 2018, namely Retail Sukuk SR-007 series. The results of the study show that the inflation and price variables have a long-term significant influence on the volume of Retail Sukuk SR-007 issuance and only yield variables that do not influence both the long- and short-term, while the exchange rate variable has significant long- and short-term effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".