Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures
Bibliographic record
Abstract
In this article, we examine whether incorporating complexity measures as features in deep learning (DL) algorithms enhances their accuracy in predicting forex market volatility. Our approach involved the gradual integration of complexity measures alongside traditional features to determine whether their inclusion would provide additional information that improved the model’s predictive accuracy. For our analyses, we employed recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) as DL model architectures, while using the Hurst exponent and fuzzy entropy as complexity measures. All analyses were conducted on intraday data from four highly liquid currency pairs, with volatility estimated using the Range-Based estimator. Our findings indicated that the inclusion of complexity measures as features significantly enhanced the accuracy of DL models in predicting volatility. In achieving this, we contribute to a relatively unexplored area of research, as this is the first instance of such an approach being applied to the prediction of forex market volatility. Additionally, we conducted a comparative analysis of the three models’ performance, revealing that the LSTM and GRU models consistently demonstrated a superior accuracy. Finally, our findings also have practical implications, as they may assist risk managers and policymakers in forecasting volatility in the forex market.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".