Accuracy and MCC were read at a fixed 0.5, which penalised a model whose
probabilities rank well but sit off-centre: the VAE+Transformer had the best
ROC-AUC (0.762) and one of the lowest accuracies. Every rung now takes the
cut-off with the best MCC on its own validation block (the last fifth of
training, never the test fold); the 0.5 figures are kept beside them.
Test predictions are pooled across folds and the VAE+Transformer's ROC-AUC is
compared with every other rung by a moving-block bootstrap (one-week blocks),
giving a 95% interval and p-value per comparison. Per-fold scores are saved.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Ablation ladder on the deployed regime target, with HAR and GARCH rungs
--target regime asks the live question (next 24h above the trailing 90-day
median), with persistence as the current regime and two econometric rungs on
the same purged folds: HAR (Corsi 2009) and GARCH(1,1) by maximum likelihood,
each turned into a probability by logistic calibration on the training block.
GARCH forecasts mean variance against a median threshold, so an uncalibrated
cut scored 43% where the calibrated one scores 61%.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
19 days ago
Complete the ablation ladder against the thesis methodology
Five rungs were missing. The ladder now covers every model named in
sections 3.1.5 and 3.1.9:
added CNN, LSTM baseline 4
added gradient boosting baseline 3, previously quoted from a
separate screen rather than measured
on these folds
added CNN+Transformer ablation (b)
added CNN+VAE+Transformer ablation (d), baseline 6
The last is the architecture Contribution 1 claims as novel. No rung in
this study contained a CNN at all, so the proposed model had never been
measured -- the thesis claimed an architecture the code does not implement
and the ablation could not speak to.
The three CNN rungs share one CNNEncoder, so they differ only in what
follows the convolution, which is what isolating the CNN's contribution
requires.
Gradient boosting skips with a note where scikit-learn is unavailable --
Windows Application Control blocks it…
Add the ablation, HAR benchmark, figures and three related-work comparisons
Fifteen studies in three groups -- volatility benchmarking, architecture
ablation, historic versus sentiment -- kept separate because a paper
benchmarking against HAR-RV is not evidence about whether a VAE earns its
place, and mixing them is how a related-work chapter ends up comparing
numbers that were never comparable.
run_har_benchmark.py regresses log realised volatility against HAR-RV on
identical folds and reports QLIKE as well as R2, which is what makes the
comparison like-for-like. Nine cells, three assets, three horizons: the
neural network wins two, both on BTC, and loses QLIKE in both. On ETH and
ADA ordinary least squares on the same 33 features wins both metrics at
every horizon. Improvement over HAR is 1.1-1.4x against the 5.4x claimed by
arXiv 2511.20105, whose HAR scores 0.128 where this one scores 0.32-0.36.
It also corrects an earlier figure in sota_volatility_comparison.md: the
2.1x reported there used a weak HAR specification taken from stored
metadata. Fitted pro…
