Topic
Derivatives & Volatility
Option pricing, volatility models and forecasting, hedging and implied surfaces.
- Papers featured
- 883
- Last 12 months
- 53
- Cited 100+
- 0
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- 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 202354cites
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 202324cites
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
- 16 Jan 202618cites
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
- 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
- 20 Dec 202318cites
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×
- 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
Latest
- 9 Oct 20263fanfare
Beneath the VIX: Probability, Severity, and Uncertainty Shocks
Separating risk-neutral shortfall probability from conditional severity in option prices shows that severity accounts for 91.5 percent of recession log premium movements, supplying joint empirical restrictions on uncertainty and economic activity.
arXiv
- 9 Oct 20263fanfare
Adversarial Training for Deep Hedging in Nonstationary Markets
WRAP combines Wasserstein reweighting and optimal-transport perturbations in a distributionally robust framework to make deep hedging policies robust to nonstationarity and distributional drift in market conditions.
arXiv
- 9 Oct 20262fanfare
HAN-Mamba: Hierarchical Selective State Space Networks for Multi-Scale Financial Volatility Forecasting
Replaces transformers with selective state space encoders in a hierarchical architecture for realized volatility forecasting, reducing error to 0.1927 on the Optiver benchmark with 33 percent fewer parameters than the attention variant.
arXiv
- 9 Oct 20263fanfare
The Credit Spread at Long Horizons
The study shows that Baa-Aaa spreads at 27-year maturity test pricing kernels better than shorter maturities, with a structural refinancing model matching observed spreads while prior kernels overpredict by 27 to 68 basis points.
SSRN
- 9 Oct 20263fanfare
Delta-Guided Soft Actor-Critic for Derivative Hedgingunder Model Misspecification and Transaction Costs
A reinforcement learning method embedding Black-Scholes delta guidance cuts hedging error by 19-38% across simulated and real options data with robust statistical significance.
SSRN
- 9 Oct 20263fanfare
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.
RePEc
- 2 Oct 20260cites
Negative Oil & Nickel Squeeze: A Feedback Model for Extreme Commodity Futures Prices
A feedback model explains extreme commodity futures prices like negative oil by connecting delivery constraints to roll options and long-short position imbalances.
arXiv
- 2 Oct 20260cites
When Hedging Changes the Payoff: Option Replication with Price Impact and Execution Costs
The study shows how hedging a derivative via trading the underlying changes its payoff when execution generates price impact and costs, deriving pricing equations and replication conditions.
arXiv
- 2 Oct 20263fanfare
Credit When it's Due: Corporate Bond Factors on a Schedule
The research finds that the first five trading days of each month account for 73% of individual bond credit returns and 83% of the market credit premium, revealing a concentrated timing pattern in fixed-income compensation.
SSRN
- 2 Oct 20263fanfare
Market-Implied Policy-Outcome Dispersion and Long-Run Equity Volatility: Meeting-Level Evidence from Kalshi FOMC Contracts
Using Kalshi FOMC contract probabilities, the paper shows that cross-outcome variance in Federal Reserve policy expectations contains significant information about long-run stock market volatility.
SSRN
- 2 Oct 20262fanfare
Tail Risk Hedging in Equity Portfolios: Evaluating the Cost-Effectiveness of Out-of-the-Money Put Options versus Dynamic Tail-Risk Mitigation Strategies During High-Volatility Regimes
Static out-of-the-money put options provide crash protection but drag long-term returns, while dynamic tail-risk strategies adapt to market regimes and deliver superior risk-adjusted returns across cycles.
SSRN
- 2 Oct 20263fanfare
Testing Whether Volatility Model Gains Persist: A Prespecified Holdout in Tail Risk Forecasting
Testing eight volatility models on equity indices with prespecified holdout periods, the research finds that gains from more complex models often do not persist across markets or time.
RePEc
- 2 Oct 20262fanfare
The implied volatility surface (also) is path-dependent
The research shows that past asset price trajectories predict implied volatility movements up to two years forward, with a parsimonious SSVI model capturing this path-dependent behavior.
RePEc
- 2 Oct 20262fanfare
Diagnosing and Stabilizing Dynamic Correlations in Multivariate Stochastic Volatility Models
Decomposing forecasting losses into correlation versus scale components, the paper shows how to diagnose and stabilize dynamic-correlation volatility models using realized-volatility inputs.
RePEc
- 2 Oct 20262fanfare
Learn the measure, estimate the moment: machine-learned drivers in dynamic conditional correlation models
Combining machine-learned forecasts of realized measures with dynamic conditional correlation models improves correlation matrix forecasts, producing valid predictions and beating realized-driver baselines across multiple horizons.
RePEc
- 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 202618cites
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×