---
title: Volatility Disagreement in the Options Market
url: https://www.ml-quant.com/papers/repec/nbr-nberwo-35500/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-09
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:nbr:nberwo:35500
source_url: https://econpapers.repec.org/RePEc:nbr:nberwo:35500
featured: 2026-10-09
citations: unknown
topic: Derivatives & Volatility
---


# Volatility Disagreement in the Options Market

Cross-sectional dispersion in volatility forecasts predicts delta-hedged straddle losses of 5.14% per month, consistent with mispricing rather than risk compensation.

- Source: https://econpapers.repec.org/RePEc:nbr:nberwo:35500
- Identifier: RePEc:nbr:nberwo:35500
- Released: 2026-10-07
- First featured: Quant Letter No. 134 (2026-10-09): https://www.ml-quant.com/issues/2026-10-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility
- Authors: Turan G. Bali, Bryan T. Kelly, Mathis Mörke

## Related

- [Realised Volatility Forecasting: Machine Learning via Financial Word Embedding](https://www.ml-quant.com/papers/arxiv/2108.00480/): A new NLP framework shows that adding news text can improve stock volatility forecasting, particularly during volatile times, when combined with standard 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.
- [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.
- [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.
- [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.
- [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.