---
title: Improved Volatility Forecasting
url: https://www.ml-quant.com/papers/ssrn/4765262/
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: SSRN 4765262
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4765262
featured: 2024-03-20
citations: unknown
topic: Derivatives & Volatility
---


# Improved Volatility Forecasting

Enhancing the Heterogeneous Autoregressive Regression model with new methods for deriving volatility estimators from option price data improves daily stock volatility forecasts.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4765262
- Identifier: SSRN 4765262
- Released: 2024-03-19
- First featured: Quant Letter No. 41 (2024-03-20): https://www.ml-quant.com/issues/2024-03-20/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [A path-dependent PDE solver based on signature kernels](https://www.ml-quant.com/papers/arxiv/2403.11738/): The article introduces a new, verifiably effective kernel-based solver for path-dependent partial differential equations (PPDEs). This provides a practical alternative to Monte Carlo methods, especially for option pricing under rough volatility.
- [A time-stepping deep gradient flow method for option pricing in (rough) diffusion models](https://www.ml-quant.com/papers/arxiv/2403.00746/): The study introduces a new deep learning method for pricing European options in diffusion models, transforming the option pricing equation into an energy minimization problem and using deep artificial neural networks.
- [Generalized measure Black–Scholes equation: towards option self-similar pricing](https://www.ml-quant.com/papers/arxiv/2404.05214/): The research presents a generalized version of the Black-Scholes model, considering option price dynamics to depend on a measure representing investors' uncertainty.
- [A monotone piecewise constant control integration approach for the two-factor uncertain volatility model](https://www.ml-quant.com/papers/arxiv/2402.06840/): The study introduces a new solution to the Hamilton-Jacobi-Bellman equation in option pricing, proving its stability, consistency, and effectiveness against traditional methods.
- [Option pricing for Barndorff–Nielsen and Shephard model by supervised deep learning](https://www.ml-quant.com/papers/arxiv/2402.00445/): A supervised deep-learning scheme has been developed to accurately compute call option prices for the Barndorff-Nielsen and Shephard model, using an additional input variable from the Black-Scholes formula.
- [TSMixer and Realized Volatility Prediction](https://www.ml-quant.com/papers/ssrn/4713756/): Stock Volatility Forecasting with Neural Networks: The TSMixer neural network model has proven to be more effective than traditional models in predicting stock market volatility, indicating a possible shift towards simpler models in the future.