Forecasting Stochastic Volatility Using the Kalman Filter: An Application to Canadian Interest Rates and Price-Earnings ratio
Bibliographic record
Abstract
In this paper, we aim at forecasting the stochastic volatility of key financial market\nvariables with the Kalman filter using stochastic models developed by Taylor (1986,\n1994) and Nelson (1990). First, we compare a stochastic volatility model relying on\nthe Kalman filter to the conditional volatility estimated with the GARCH model. We\napply our models to Canadian short-term interest rates. When comparing the profile\nof the interest rate stochastic volatility to the conditional one, we find that the omission\nof a constant term in the stochastic volatility model might have a perverse effect\nleading to a scaling problem, a problem often overlooked in the literature. Stochastic\nvolatility seems to be a better forecasting tool than GARCH(1,1) since it is less conditioned\nby autoregressive past information. Second, we filter the S&P500 price-earnings\n(P/E) ratio in order to forecast its value. To make this forecast, we postulate a\nrational expectations process but our method may accommodate other data generating\nprocesses. We find that our forecast is close to a GARCH(1,1) profile
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".