Dynamics of Realized Volatility and Correlations: An Empirical Study Using Interest Rate Spread Options
Bibliographic record
Abstract
by Hydro-Québec. 3 We have benefited from the useful commentsof Jean-Hugues Lafleur. All remaining errors are our responsibility. This study empirically examines the competitiveness of different forecasting sets of realized volatilities and correlations using linear and nonlinear specifications of time series based on high frequency data. The linear specification uses lagged explanatory variables to explain fractionally integrated series of realized volatilities and correlations. The nonlinear specification consists of a two-step approach. In the first step, joint time series of realized volatilities and correlations are filtered using a multivariate singular system analysis approach. Based on the cleaned series, vectors of nearest neighbors are identified in space and casted into a local linear regression to generate forecasts in the second step. The empirical performance of those specifications is compared to a GARCH diagonal-BEKK model in the context of a trader who would simultaneously quote a call spread option price based on the forecasted parameters and delta-hedge her position with a replicating portfolio. More traditional loss functions based on the absolute forecasting error are also used. The forecasting methodologies based on time series of realized volatilities and correlations generally (but not unanimously) dominate the GARCH approach. Evidence of nonlinearity seems apparent for time series of volatilities irrespective of the return sampling frequency. General performance ranking for the approaches based on realized volatilities and correlations is not robust to the chosen loss function and the return sampling frequency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".