A hybrid GARCH, convolutional neural network and long short term memory methods for volatility prediction in stock market
Bibliographic record
Abstract
Forecasting the volatility of the stock market price is indispensable for managing the risks associated with market dynamics and provides valuable insights for financial decision in trading strategies. This study aims to enhance the accuracy of volatility prediction for stock market price using hybrid models combining econometric and deep learning approaches. Specifically, it introduces a novel GARCH-CNN-LSTM hybrid model for more precise volatility forecasting of stock market price. The GARCH model is efficient at capturing volatility clusters and kurtosis features, while the CNN excels in extracting spatial patterns from time series data, and LSTM effectively preserves essential information over extended periods. GARCH(1,1) model is selected based on AIC, maximum log-likelihood, and parameter significance. Subsequently, CNN and LSTM models are chosen for their complementary capabilities in volatility prediction. We evaluated the forecasting performance of the hybrid models from out-sample test data, employing Mean Square Error, Root Mean Square Error and Mean Absolute Error. The result indicates that the new model outperforms the existing models with an improvement of 8% to 13% accuracy. Furthermore, we conduct the Diebold-Mariano test to confirm significant differences in performance.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".