--- title: Derivatives & Volatility url: https://www.ml-quant.com/topics/derivatives-volatility/ 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 --- # Derivatives & Volatility Option pricing, volatility models and forecasting, hedging and implied surfaces. 868 papers featured; 38 in the last 12 months. Papers featured per quarter: 2023 Q2 34, 2023 Q3 119, 2023 Q4 99, 2024 Q1 130, 2024 Q2 100, 2024 Q3 101, 2024 Q4 47, 2025 Q1 81, 2025 Q2 92, 2025 Q3 27, 2025 Q4 25, 2026 Q1 1, 2026 Q2 2, 2026 Q3 10 ## Most cited - [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): 88 citations. The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility. - [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): 52 citations. Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise. - [A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm](https://www.ml-quant.com/papers/arxiv/2412.07223/): 25 citations. The article presents a unique AI model for predicting future volatility in emerging stock markets, showing high accuracy and low error rates. - [Physics-informed convolutional transformer for predicting volatility surface](https://www.ml-quant.com/papers/arxiv/2209.10771/): 23 citations. The paper presents a new architecture using physics-informed neural networks and convolutional transformers for better predicting financial market volatility. - [Rough PDEs for Local Stochastic Volatility Models](https://www.ml-quant.com/papers/arxiv/2307.09216/): 20 citations. The article presents a new way to set prices in local stochastic volatility models, using rough path theory to understand conditional dynamics and price European options. - [Robust Risk-Aware Option Hedging](https://www.ml-quant.com/papers/arxiv/2303.15216/): 19 citations. The study highlights the effectiveness of robust risk-aware reinforcement learning in managing risks related to path-dependent financial derivatives, especially in hedging barrier options, proving robust strategies are superior. - [A path-dependent PDE solver based on signature kernels](https://www.ml-quant.com/papers/arxiv/2403.11738/): 18 citations. 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. - [Realised Volatility Forecasting: Machine Learning via Financial Word Embedding](https://www.ml-quant.com/papers/arxiv/2108.00480/): 17 citations. A new NLP framework shows that adding news text can improve stock volatility forecasting, particularly during volatile times, when combined with standard models. - [The Self-Organized Criticality Paradigm in Economics&Finance](https://www.ml-quant.com/papers/arxiv/2407.10284/): 17 citations. The article proposes Self-Organised Criticality as a reason for extreme volatility in financial markets and large business cycle fluctuations, calling for specific policy considerations. - [Operator Deep Smoothing for Implied Volatility](https://www.ml-quant.com/papers/arxiv/2406.11520/): 17 citations. A novel method for smoothing implied volatility using neural operators is presented, which maps data to smoothed surfaces, respects no-arbitrage rules, and is robust to input subsampling. - [First order Martingale model risk and semi-static hedging](https://www.ml-quant.com/papers/arxiv/2410.06906/): 16 citations. The study expands on previous research on model risk distributionally robust sensitivities, introducing the minimization of the distributionally robust problem in relation to semi-static hedging strategies and outlining the optimal strategies. - [Convergence of Heavy-Tailed Hawkes Processes and the Microstructure of Rough Volatility](https://www.ml-quant.com/papers/arxiv/2312.08784/): 16 citations. The research identifies the weak convergence of a nearly-unstable Hawkes process with a heavy-tailed kernel, useful for creating a scaling limit for a financial market model. ## Latest - [Universal Diffusion Models for Implied Volatility Surfaces: Learning Shared Dynamics Across Stocks](https://www.ml-quant.com/papers/arxiv/2609.22893/) (2026-09-25): A universal diffusion model trained on pooled data from 50 stocks learns to jointly generate implied volatility surface changes and stock returns, extrapolating well to unseen stocks. - [Surface-Driven Stochastic Volatility for Commodity Options: Identification of Stochastic Vol-of-Vol and Leverage from Smile Dynamics](https://www.ml-quant.com/papers/arxiv/2609.27138/) (2026-09-25): Develops a surface-driven stochastic volatility framework for commodity options using daily volatility surface factors, recovering vol-of-vol and leverage parameters from smile dynamics. - [Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes](https://www.ml-quant.com/papers/arxiv/2609.27764/) (2026-09-25): Tree-based machine learning outperforms neural networks at detecting systematic option-pricing deviations from Black-Scholes using 2.6 million real contracts, with domain-expert features crucial. - [Prices or implied volatilities? Choosing the loss function in machine learning option pricing](https://www.ml-quant.com/papers/ssrn/7498639/) (2026-09-25): The paper compares machine learning option pricing trained on pricing errors versus implied-volatility errors using 8.67 million S&P 500 index-option observations from 1997 through 2025. - [Hedge Fund Trading and Sovereign Bond Yield Sensitivity](https://www.ml-quant.com/papers/ssrn/7506360/) (2026-09-25): Leveraged hedge fund positions amplify sovereign bond yield sensitivity to monetary shocks by over a quarter through directional rebalancing, with effects scaling to position intensity. - [Tail-Risk Forecasting with General Cubic Distributions](https://www.ml-quant.com/papers/ssrn/7504480/) (2026-09-25): A cubic quantile framework forecasts Value-at-Risk and Expected Shortfall more reliably than GARCH benchmarks across eight equity indices without requiring a parametric density. - [MartingaleONet: Physics-Constrained Operator Learning for Real-Time Option Pricing and Volatility Calibration](https://www.ml-quant.com/papers/ssrn/7498326/) (2026-09-25): A deep operator network maps volatility surfaces to option prices under the Heston model 15,000 times faster than finite-difference methods while reducing dynamic hedging variance by over 59% under transaction costs. - [Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting](https://www.ml-quant.com/papers/repec/fip-fedgfe-103519/) (2026-09-25): Tree-based models partition the option surface by moneyness and maturity to forecast volatility, reducing one-month-ahead errors by 13 percent versus benchmark models. - [How Economic News Drives Implied Volatility in Agricultural Commodity Markets](https://www.ml-quant.com/papers/repec/ags-asea26-404810/) (2026-09-25): Financial and macroeconomic news topics systematically predict implied volatility in corn and soybean markets, with program trading and 2008 crisis topics most robust at short horizons. - [Adaptive LASSO-MGARCH for Multivariate Volatility Forecasting](https://www.ml-quant.com/papers/repec/cdf-wpaper-2026-4/) (2026-09-25): Introducing coefficient-specific penalization into multivariate GARCH equations reduces complexity and improves out-of-sample covariance forecasts across bonds, equities, and commodities. - [Valuation of variable annuities under the Volterra mortality and rough Heston models](https://www.ml-quant.com/papers/arxiv/2604.00472/) (2026-04-03): The paper discusses how to value variable annuity contracts that offer early surrender options. It uses advanced models and deep learning to find the best strategies for surrendering the contracts, while also providing protection against losses through minimum benefits. - [Numerical valuation of European options under two-asset infinite-activity exponential Lévy models](https://www.ml-quant.com/papers/arxiv/2511.02700/) (2026-04-03): The article introduces a better method for pricing European-style options using two-asset exponential Lévy models. It focuses on faster calculations by employing fast Fourier transforms and a semi-Lagrangian approach, surpassing older techniques. - [Realised Volatility Forecasting: Machine Learning via Financial Word Embedding](https://www.ml-quant.com/papers/arxiv/2108.00480/) (2026-01-16): A new NLP framework shows that adding news text can improve stock volatility forecasting, particularly during volatile times, when combined with standard models. - [Analysis of Fundamental and Technical Financial Ford Motor Company with The Arrangements of Implication Black Volatility](https://www.ml-quant.com/papers/ssrn/4482880/) (2025-12-28): The study shows that Ford Motor Company had its smallest earnings per share payout gap in 2020 compared to previous years. - [Global Liquidity and Volatility](https://www.ml-quant.com/papers/ssrn/4482265/) (2025-12-28): Global liquidity from banks impacts responses to crises and eases funding strains internationally. - [Robert C. Merton's Contributions](https://www.ml-quant.com/papers/ssrn/4480625/) (2025-12-28): Robert C. Merton is a significant finance scholar known for his work on derivatives pricing and finance theories. - [Low Volatility Asset Valuation in Brazilian Stock Market: Lower Risk with Higher Returns](https://www.ml-quant.com/papers/ssrn/4480285/) (2025-12-28): Lower volatility Brazilian stocks have consistently outperformed high-volatility stocks in annual returns from 2003 to 2021. - [Global Dollar Holdings Trends](https://www.ml-quant.com/papers/ssrn/4478513/) (2025-12-28): Foreign institutional investors significantly increased their USD security holdings, influenced by varying currency hedging demands. - [Asymptotic Expansions for High-Frequency Option Data](https://www.ml-quant.com/papers/ssrn/4440168/) (2025-12-19): A new method for analyzing financial data helps test for sudden volatility changes, with evidence from SP500 options indicating significant variation. - [Deep Hedging with Reinforcement Learning: A Practical Framework for Option Risk Management](https://www.ml-quant.com/papers/arxiv/2512.12420/) (2025-12-19): The article describes a reinforcement-learning method for hedging equity index options that enhances risk-adjusted returns while managing turnover and costs. - [An Efficient Machine Learning Framework for Option Pricing via Fourier Transform](https://www.ml-quant.com/papers/arxiv/2512.16115/) (2025-12-19): A new algorithmic framework merges a smooth offset method with machine learning for quicker pricing of path-independent options, vastly speeding up evaluations compared to older techniques. - [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/) (2025-12-19): 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. - [Option-Implied Zero-Coupon Yields: Unifying Bond and Equity Markets](https://www.ml-quant.com/papers/arxiv/2512.10823/) (2025-12-14): The paper presents a new approach to pricing zero-coupon bonds that aligns them with equity options for more accurate interest rate modeling. - [DeepSVM: Learning Stochastic Volatility Models with Physics-Informed Deep Operator Networks](https://www.ml-quant.com/papers/arxiv/2512.07162/) (2025-12-14): Physics-Informed Volatility: DeepSVM is a machine learning model that accurately calibrates stochastic volatility without labels, but needs better regularization for derivatives. - [CREDIT DERIVATIVE -An Alternative Tool for Indian Commercial Banks to Transfer Credit Risk](https://www.ml-quant.com/papers/ssrn/4973692/) (2025-12-01): Poor credit risk management in Indian banks has led to rising Non-Performing Assets, highlighting the need for modern risk tools, such as credit derivatives, to improve future performance. - [European Real Estate Volatility](https://www.ml-quant.com/papers/ssrn/4964818/) (2025-12-01): This study shows that different European real estate markets have varying volatility and suggests using tactical asset allocation to improve investment performance. - [Constrained deep learning for pricing and hedging european options in incomplete markets](https://www.ml-quant.com/papers/arxiv/2511.20837/) (2025-12-01): This article discusses a method using deep learning to price and hedge European options in incomplete markets, optimizing risk distribution while handling tricky payoffs. - [Signature approach for pricing and hedging path-dependent options with frictions](https://www.ml-quant.com/papers/arxiv/2511.23295/) (2025-12-01): This innovative approach simplifies the pricing and hedging of path-dependent options, improving strategies in markets with friction through numerical analysis. - [Beta-Dependent Gamma Feedback and Endogenous Volatility Amplification in Option Markets](https://www.ml-quant.com/papers/arxiv/2511.22766/) (2025-12-01): The study connects individual option hedging to broader market volatility, revealing how market-maker actions during volatility spikes can increase fluctuations, especially in low-beta stocks. - [Empirical examination of the stability of expectations -Augmented Phillips Curve for developing and developed countries](https://www.ml-quant.com/papers/arxiv/2511.22786/) (2025-12-01): The research on the Phillips Curve from 1980 to 2016 reveals strong forward-looking inflation expectations in developed countries, while others face challenges in its application due to past volatility.