The Impact of the U.S. Macroeconomic Variables on the CBOE VIX Index
Bibliographic record
Abstract
The purpose of this study is to find the influence of various macroeconomic factors on the volatility index, as macroeconomic factors affect stock market volatility, resulting in an impact on the VIX Index, representing the risk in the stock market. To estimate the significance and importance of the U.S. macroeconomic variables on stock market volatility and risk, classification problems from machine learning are constructed to predict the daily and weekly trends of the VIX Index. Data from May 2007 to December 2021 is considered for analysis. The selected models are trained with twenty-four daily features and twenty-four plus nine weekly features. The outcomes suggest that the decisions made by the Light GBM and XG Boost on ranking features can be significantly accepted over logistic regression. It is found from the results that economic policy uncertainty indices, gold price, the USD Index, and crude oil are signified as strong predictors. The Financial Stress Index, initial claims, M2, TED spread, Fed rate, and credit spread are also strong predictors, while various yields on fixed income securities make a little less impact on the VIX Index. The TED spread, Financial Stress Index, and Equity Market Volatility (Infectious Disease Tracker) are positively associated with the VIX.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".