---
title: Model Specification for Volatility Forecasting
url: https://www.ml-quant.com/papers/repec/eee-finana-v-97-y-2025-i-c-s1057521924007828/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:finana:v:97:y:2025:i:c:s1057521924007828
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1057521924007828%3Bh%3Drepec%3Aeee%3Afinana%3Av%3A97%3Ay%3A2025%3Ai%3Ac%3As1057521924007828
featured: 2025-01-23
citations: unknown
topic: Derivatives & Volatility
---


# Model Specification for Volatility Forecasting

The best model for forecasting asset price volatility should use the natural logarithmic form of the original volatility measure for efficient regression estimators.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1057521924007828%3Bh%3Drepec%3Aeee%3Afinana%3Av%3A97%3Ay%3A2025%3Ai%3Ac%3As1057521924007828
- Identifier: RePEc:eee:finana:v:97:y:2025:i:c:s1057521924007828
- Released: 2025-01-23
- First featured: Quant Letter No. 83 (2025-01-23): https://www.ml-quant.com/issues/2025-01-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Forecasting realized volatility in the stock market: a path-dependent perspective](https://www.ml-quant.com/papers/arxiv/2503.00851/): A new volatility forecasting model, combining the heterogeneous autoregressive model with path-dependent volatility models, shows improved forecasting accuracy in the Chinese stock market.
- [A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm](https://www.ml-quant.com/papers/arxiv/2412.07223/): The article presents a unique AI model for predicting future volatility in emerging stock markets, showing high accuracy and low error rates.
- [Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2504.09380/): The paper proposes a new GARCH-GRU model for financial volatility forecasting, showing better computational efficiency and forecasting accuracy than other models.
- [Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index](https://www.ml-quant.com/papers/arxiv/2504.18958/): The research uses the Financial Chaos Index to study stock market volatility, identifying three market types and using sentiment predictors for volatility forecasting.
- [Foundation Time-Series AI Model for Realized Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2505.11163/): The study finds that the TimesFM model, with incremental fine-tuning, is effective for volatility forecasting in financial risk management, outperforming traditional models.
- [Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2512.12250/): A combined model of Stochastic Volatility and Long Short Term Memory networks offers better volatility predictions for the S&P 500, outperforming traditional models for improved risk assessment.
