Topic
Derivatives & Volatility
Option pricing, volatility models and forecasting, hedging and implied surfaces.
- Papers featured
- 868
- Last 12 months
- 38
- Cited 100+
- 0
- Top venue
- SSRN
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Most cited
Featured papers in this topic with the most citations today.
- 15 May 202488cites
Risk Revisited
The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
SSRN
- 12 Jul 202352cites
Rough Volatility: Fact or Artefact?
Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
arXivIn Sankhya B
- 12 Dec 202425cites
A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm
The article presents a unique AI model for predicting future volatility in emerging stock markets, showing high accuracy and low error rates.
arXivIn 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)Featured 2×
- 6 Dec 202323cites
Physics-informed convolutional transformer for predicting volatility surface
The paper presents a new architecture using physics-informed neural networks and convolutional transformers for better predicting financial market volatility.
arXivIn Quantitative Finance
- 19 Jul 202320cites
Rough PDEs for Local Stochastic Volatility Models
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.
arXivIn Mathematical Finance
- 3 Jan 202419cites
Robust Risk-Aware Option Hedging
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.
arXivIn Applied Mathematical Finance
- 20 Mar 202418cites
A path-dependent PDE solver based on signature kernels
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.
arXivIn Mathematics of Computation
- 16 Jan 202617cites
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
A new NLP framework shows that adding news text can improve stock volatility forecasting, particularly during volatile times, when combined with standard models.
arXiv
- 17 Jul 202417cites
The Self-Organized Criticality Paradigm in Economics&Finance
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.
arXiv
- 20 Jun 202417cites
Operator Deep Smoothing for Implied Volatility
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.
arXivIn International Conference on Learning Representations
- 23 Oct 202416cites
First order Martingale model risk and semi-static hedging
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.
arXiv
- 20 Dec 202316cites
Convergence of Heavy-Tailed Hawkes Processes and the Microstructure of Rough Volatility
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.
arXivFeatured 3×
Latest
- 25 Sep 20260cites
Universal Diffusion Models for Implied Volatility Surfaces: Learning Shared Dynamics Across Stocks
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.
arXiv
- 25 Sep 20260cites
Surface-Driven Stochastic Volatility for Commodity Options: Identification of Stochastic Vol-of-Vol and Leverage from Smile Dynamics
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.
arXiv
- 25 Sep 20260cites
Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes
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.
arXiv
- 25 Sep 20263fanfare
Prices or implied volatilities? Choosing the loss function in machine learning option pricing
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.
SSRN
- 25 Sep 20263fanfare
Hedge Fund Trading and Sovereign Bond Yield Sensitivity
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.
SSRN
- 25 Sep 20263fanfare
Tail-Risk Forecasting with General Cubic Distributions
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.
SSRN
- 25 Sep 20262fanfare
MartingaleONet: Physics-Constrained Operator Learning for Real-Time Option Pricing and Volatility Calibration
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.
SSRN
- 25 Sep 20263fanfare
Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting
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.
RePEc
- 25 Sep 20262fanfare
How Economic News Drives Implied Volatility in Agricultural Commodity Markets
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.
RePEc
- 25 Sep 20262fanfare
Adaptive LASSO-MGARCH for Multivariate Volatility Forecasting
Introducing coefficient-specific penalization into multivariate GARCH equations reduces complexity and improves out-of-sample covariance forecasts across bonds, equities, and commodities.
RePEc
- 3 Apr 20260cites
Valuation of variable annuities under the Volterra mortality and rough Heston models
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.
arXiv
- 3 Apr 20260cites
Numerical valuation of European options under two-asset infinite-activity exponential Lévy models
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.
arXivIn Applied Mathematical Finance
- 16 Jan 202617cites
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
A new NLP framework shows that adding news text can improve stock volatility forecasting, particularly during volatile times, when combined with standard models.
arXiv
- 28 Dec 20251cites
Analysis of Fundamental and Technical Financial Ford Motor Company with The Arrangements of Implication Black Volatility
The study shows that Ford Motor Company had its smallest earnings per share payout gap in 2020 compared to previous years.
SSRNFeatured 2×
- 28 Dec 202577shares
Global Liquidity and Volatility
Global liquidity from banks impacts responses to crises and eases funding strains internationally.
SSRNFeatured 2×
- 28 Dec 2025456shares
Robert C. Merton's Contributions
Robert C. Merton is a significant finance scholar known for his work on derivatives pricing and finance theories.
SSRNFeatured 2×
- 28 Dec 20250cites
Low Volatility Asset Valuation in Brazilian Stock Market: Lower Risk with Higher Returns
Lower volatility Brazilian stocks have consistently outperformed high-volatility stocks in annual returns from 2003 to 2021.
SSRNFeatured 2×
- 28 Dec 2025881shares
Global Dollar Holdings Trends
Foreign institutional investors significantly increased their USD security holdings, influenced by varying currency hedging demands.
SSRNFeatured 2×
- 19 Dec 20252cites
Asymptotic Expansions for High-Frequency Option Data
A new method for analyzing financial data helps test for sudden volatility changes, with evidence from SP500 options indicating significant variation.
SSRNFeatured 2×
- 19 Dec 20250cites
Deep Hedging with Reinforcement Learning: A Practical Framework for Option Risk Management
The article describes a reinforcement-learning method for hedging equity index options that enhances risk-adjusted returns while managing turnover and costs.
arXiv
- 19 Dec 20250cites
An Efficient Machine Learning Framework for Option Pricing via Fourier Transform
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.
arXiv
- 19 Dec 20252cites
Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting
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.
arXiv
- 14 Dec 20250cites
Option-Implied Zero-Coupon Yields: Unifying Bond and Equity Markets
The paper presents a new approach to pricing zero-coupon bonds that aligns them with equity options for more accurate interest rate modeling.
arXivIn Journal of Risk and Financial Management
- 14 Dec 20250cites
DeepSVM: Learning Stochastic Volatility Models with Physics-Informed Deep Operator Networks
Physics-Informed Volatility: DeepSVM is a machine learning model that accurately calibrates stochastic volatility without labels, but needs better regularization for derivatives.
arXivFeatured 2×
- 1 Dec 20250cites
CREDIT DERIVATIVE -An Alternative Tool for Indian Commercial Banks to Transfer Credit Risk
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.
SSRNFeatured 2×
- 1 Dec 2025190shares
European Real Estate Volatility
This study shows that different European real estate markets have varying volatility and suggests using tactical asset allocation to improve investment performance.
SSRNFeatured 2×
- 1 Dec 20251cites
Constrained deep learning for pricing and hedging european options in incomplete markets
This article discusses a method using deep learning to price and hedge European options in incomplete markets, optimizing risk distribution while handling tricky payoffs.
arXiv
- 1 Dec 20258cites
Signature approach for pricing and hedging path-dependent options with frictions
This innovative approach simplifies the pricing and hedging of path-dependent options, improving strategies in markets with friction through numerical analysis.
arXiv
- 1 Dec 20250cites
Beta-Dependent Gamma Feedback and Endogenous Volatility Amplification in Option Markets
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.
arXiv
- 1 Dec 20255cites
Empirical examination of the stability of expectations -Augmented Phillips Curve for developing and developed countries
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.
arXiv