An Application of the “Recursive Flexible Window” Methodology to Test for Financial Bubbles in a Major Stock Market
Bibliographic record
Abstract
Identifying and dating financial bubbles in real time is in the forefront of current empirical research. Their accuracy provides real time useful “warning alerts” to central bankers and fiscal regulators. The complexity of their nonlinear structure and the inherent sudden break mechanisms makes the econometric testing challenging. The new recursive flexible window methodology provided by Phillips, Shi, and Yu (2015) gives consistent results and delivers significant power gains when multiple bubbles occur. It successfully identifies well-known historical episodes of exuberance and collapse. In this paper we look at the Indian stock market indices, the SENSEX, and the NIFTY 50, to see if there is any evidence of a bubble there. We use monthly data for each series, with the Sensex data spanning April 1979 to October 2018 and NIFTY 50 data spanning July 1990 to October 2018. The existence of bubbles in this index will give us some indication of where bubbles are more likely to occur, and therefore provide evidence of potential economic (financial) crises.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".