Topic
Derivatives & Volatility
Option pricing, volatility models and forecasting, hedging and implied surfaces.
- Papers featured
- 917
- Last 12 months
- 79
- 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 202355cites
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
- 20 Jun 202423cites
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
- 19 Jul 202321cites
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
- 16 Jan 202620cites
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
- 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
- 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.
arXivIn Mathematical FinanceFeatured 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
- 17 Jan 202417cites
Deep Signature Algorithm for Multidimensional Path-Dependent Options
The research expands the backward scheme for state-dependent FBSDEs with reflections to path-dependent FBSDEs, demonstrating the convergence of the numerical algorithm and providing examples of its use.
arXivIn SIAM J. Financial Math.
Latest
- 6 Nov 20263fanfare
A Rough Instantaneous Squared VIX Model for Term Structure and Option Pricing
A rough-memory VIX model with Mittag-Leffler decay improves term-structure fit and out-of-sample option pricing, with larger gains during elevated volatility and shorter maturities.
arXiv
- 6 Nov 20262fanfare
Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting
Learning forecast-supervised weights for intraday volatility aggregation reduces forecasting error while preserving the structure of classical realized measures like raw realized variance.
arXiv
- 6 Nov 20262fanfare
Neural Score-Driven Volatility and Risk Forecasting
A score-driven volatility model using neural networks to represent innovation density flexibly outperforms Gaussian and Student-t benchmarks, especially during high-volatility periods like COVID-19.
arXiv
- 6 Nov 20262fanfare
Nonparametric GARCH: An Iterated Neural Estimator
An iterated neural estimator for GARCH processes alternates between learning the recursion and updating conditional variances, achieving minimax-optimal rates for in-sample and forecasting tasks.
arXiv
- 6 Nov 20262fanfare
How market volatility responds to a preceding price trend: a lag-resolved measurement in cryptocurrencies and equities
Measuring minute-by-minute volatility response to preceding trends, the research finds asymmetry and evidence of one-sided temporal response in cryptocurrencies but not equities, decomposed via time-reversal symmetry.
arXiv
- 6 Nov 20262fanfare
Security and Derivative Pricing with Fast Regime Switches
The paper solves regime-switching models for affine and exponential-Lévy securities by reducing to one linear system, expanding the solution in mean holding time and pricing bonds, options, and credit instruments.
arXiv
- 6 Nov 20262fanfare
Quantifying Repair Risk in Crypto Option Surfaces
Option-surface repairs that satisfy no-arbitrage constraints admit multiple admissible solutions, and finite-difference amplification causes downstream Greeks and risk measures to diverge substantially.
arXiv
- 6 Nov 20262fanfare
AP-PEN: A Hard-Constrained, Physics-Enforced Network for Convenience-Yield Inversion from Commodity Futures
A neural network that analytically enforces the Gibson-Schwartz PDE recovers commodity convenience yield from futures and avoided implausible estimates during the April 2020 WTI dislocation.
SSRN
- 6 Nov 20262fanfare
The Shape of a Public-Data Volatility Surface: Term-Structure Carry, and the Limits of Measuring Skew
Tests term-structure and skew trading signals in equity volatility from free public data, showing term-structure carry lifts Sharpe ratio to 2.94 but skew lacks sufficient historical data.
SSRN
- 30 Oct 20263fanfare
Weekend Crypto Volatility and Monday Market Risk
Weekend cryptocurrency volatility predicts Monday variance in equities, commodities and bonds 11-22% better post-2024, following US Bitcoin ETF approval in late 2023.
arXiv
- 30 Oct 20262fanfare
A Deep Learning-Driven Operator Splitting Scheme for Free Boundary Problems: An Application to American Option Pricing in Incomplete Markets
Operator splitting scheme with physics-informed neural networks prices five-dimensional American options under transaction costs and stochastic liquidity with validated boundary accuracy.
arXiv
- 30 Oct 20262fanfare
Smooth Threshold GARCH: Quasi Maximum Likelihood Estimation and Empirical Evidence
A GARCH variant with an estimated threshold captures volatility responding asymmetrically to positive versus negative returns, allowing positive returns to have no volatility impact.
arXiv
- 30 Oct 20262fanfare
On the continuity of conditional distributions and stratification for derivative pricing
The research establishes continuity results for conditional expectations in stratified Monte Carlo, enabling neural networks to construct admissible stratification factors for path-dependent claims.
arXiv
- 30 Oct 20263fanfare
Upsilon: A Joint Spot-Volatility-Time Sensitivity for Zero-Days-to-Expiration Index Options
The study defines Upsilon, an effective sensitivity for zero-days-to-expiration index options that captures the predictable, asymmetric joint dynamics of spot, implied volatility and time decay over their six-hour life.
SSRN
- 30 Oct 20262fanfare
How Long is Five Minutes?
Validating against event-contract settlement data, the five-minute midquote captures only 68.7% of true price impact and significantly misprices thin markets, challenging the convention.
SSRN
- 30 Oct 20262fanfare
A Correlated Random-level Volatility Model: Exact One-dimensional Pricing with Skew
A six-parameter volatility model with one Gaussian factor prices European options exactly and captures implied-volatility skew better than SSVI or Heston, though short-maturity slopes remain too steep.
SSRN
- 30 Oct 20262fanfare
Rebuilding an Equity Volatility Surface from Public Data: Reliability and the Variance Risk Premium
A pipeline reconstructs equity volatility surfaces from free options data and assesses reliability; a short delta-hedged straddle harvests the variance risk premium, profitable in pre-registered tests but with realistic frictions limiting the edge.
SSRN
- 30 Oct 20262fanfare
AFNO: Universal Certifiably-Arbitrage-Free Operator Learning Framework for European Call-Price Surfaces
Proposes a neural-operator architecture that certifiably outputs arbitrage-free European call-price surfaces, addressing failure of standard neural operators to enforce no-arbitrage constraints.
SSRN
- 23 Oct 20260cites
Arbitrage-Free Implied Volatility Surface Reconstruction via Additive Martingales
AddMart reconstructs implied volatility surfaces from sparse quotes by mapping to martingale densities, ensuring no-arbitrage by construction and enabling crash probability estimation.
arXiv
- 23 Oct 20260cites
DSINC beyond Heston: the 3/2 and other non-affine stochastic-volatility models
The paper extends damped-sinc methods beyond Heston to general one-factor stochastic-volatility models including the 3/2 and SABR, achieving basis-point accuracy with comparable computational cost.
arXiv
- 23 Oct 20260cites
Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options
An arithmetic stochastic volatility model for crude oil provides semi-closed-form pricing for vanilla and calendar spread options, hedges daily risk effectively, and improves fit over geometric models.
arXiv
- 23 Oct 20260cites
Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Simulation-based inference learns posterior distributions for rough Heston parameters and propagates them through neural pricers to deliver calibrated uncertainty intervals for exotic options.
arXiv
- 23 Oct 20263fanfare
Learning Before Scheduled Events: Prediction Markets, State Prices, and Option Valuation
Prediction-market prices serve as risk-adjusted state prices to measure event probability and information arrival, improving option valuation and revealing market frictions in elections and Brexit.
SSRN
- 23 Oct 20263fanfare
Candlestick Words Improve Intraday Volatility Forecasts Beyond HAR and GARCH
Encoding intraday bars as descriptive words adds 1.7 to 3.7 points of out-of-sample R² to standard volatility models after spikes but shows no edge on daily bars.
SSRN
- 23 Oct 20263fanfare
What Is a Corporate Bond Spread Made of? Decomposition, Pricing and a Real-Time Backtest in Brazil’s Debenture Market
Decomposing debenture spreads into issuer, market, and contract components shows issuer risk explains 54 percent of variance, enabling out-of-sample pricing within 14 basis points.
SSRN
- 23 Oct 20262fanfare
From PDE Residuals to Hedging Risk: A Posteriori Diagnostics for Neural Option Pricing with Trading Costs and Volatility Misspecification
The research separates pricing error from hedging loss by decomposing delta-hedge profit and loss into terminal mismatch, residuals, volatility misspecification, rebalancing, and trading costs.
SSRN
- 23 Oct 20263fanfare
Urgency, Liquidity, and the Price of Convenience
A monetary asset pricing model estimates the convenience yield of liquidity at 5.1% annually, roughly ten times conventional measures, with only 24% attributable to urgency.
RePEc
- 16 Oct 20260cites
GROMM: Generative Risk-neutral Option Market Models
The research develops a statically and dynamically arbitrage-free model for European option prices with smooth interpolation, fitting market data within bid-ask spreads for over 95% of relevant options.
arXiv
- 16 Oct 20260cites
Three options are roughly enough - Variance-optimal semi-static hedging in the rough Heston model
The paper shows that combining dynamic trading in the underlying with static positions in three vanilla options reduces variance-swap hedging error by more than 95% across different market roughness regimes.
arXiv
- 16 Oct 20260cites
MLP-Initialized Reinforcement Learning for Exit Timing in SPX Short-Straddle Portfolios
An MLP-initialized reinforcement-learning framework optimizes exit timing for short-straddle options, achieving 37.54% cumulative return and a Sharpe ratio of 1.705 out-of-sample over 2024-2026.
arXiv