Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Movements in the India VIX are an important gauge of how the market’s risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period of study is from April 2009 to March 2021. To achieve the stated task, classifiers were trained and validated with 90% of the given sample, considering two-fold time-series cross-validation for hyper-tuning. Optimised models were then predicted on an unseen test dataset, representing 10% of the given sample. The results showed that optimal models performed well, and their accuracy scores were similar, with minor variations ranging from 63.33% to 67.67%. The stacking classifier achieved the highest accuracy. Furthermore, CatBoost, Light Gradient Boosted Machine (LightGBM), Extreme Gradient Boosting (XGBoost), voting, stacking, bagging and Random Forest classifiers are the best models with statistically similar performances. Among them, CatBoost, LightGBM, XGBoost and Random Forest classifiers can be recommended for forecasting day-to-day movements of the India VIX because of their inherently optimised structure. This finding is very useful for anticipating risk in the Indian 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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it