The study finds no evidence of stochastic arbitrage opportunities in S&P 500 index options.
problem Identifying arbitrage opportunities in S&P 500 index options.
method Developed linear and mixed-integer linear programs to compute the maximum option premium.
result No evidence of systematic stochastic arbitrage opportunities in S&P 500 index options.
The paper extends option pricing theory for markets with informed traders.
problem Discontinuity in option pricing for markets with informed traders.
method New models for option pricing in complete markets considering informed traders' information on stock price direction and return mean.
result The discontinuity puzzle in option pricing is resolved using continuous diffusion price processes.
Improved price bounds for multi-asset derivatives using market option data.
problem Creating robust price bounds for multi-asset derivatives under market-implied dependence.
method Extracting inter-asset dependence information from market option prices and applying modified martingale optimal transport.
result Improved price bounds for multi-asset derivatives, demonstrating relevance and tractability.
Bitcoin option prices reflect both market maker supply and trader demand, especially from those with insider information.
problem Understanding how market prices of bitcoin options are influenced by both market makers and informed traders.
method Analysis of Deribit options tick-level data to identify supply and demand effects.
result At-the-money option prices are driven by volatility traders, while out-of-the-money options are influenced by both volatility traders and those with insider information.
Paper presents a data-driven method for option pricing.
problem Option pricing accuracy under market volatility.
method Data-driven ensemble approach based on no-arbitrage theory.
result Model performance validated with real data.
Deep learning models predict option prices from 3D tensor data.
problem Predicting option prices for risk management and trading.
method 3D tensor representation of financial data, deep learning models (2D tensors in 3 channels).
result Proposed models outperform traditional methods like B-S model and vector-based LSTM.
Extends option pricing model to incorporate market factor dynamics.
problem Option pricing models need to account for market influencing factors.
method Extended Kim-Stoyanov-Rachev-Fabozzi model using invariance principles.
result New binomial model for complete markets with log-return dynamics.
Informer improves option pricing accuracy in volatile markets.
problem Challenges in accurate option pricing due to market volatility and traditional model limitations.
method Applying Informer, a Transformer-based neural network, for option pricing.
result Informer outperforms traditional models in option pricing accuracy.
Study the hedging of cryptocurrency options in a volatile market.
problem Hedging options in a volatile, non-stationary cryptocurrency market.
method Calibrated to SVI-implied volatility surfaces, Monte Carlo price paths generated using SVCJ, GARCH, and historical data. Delta, Delta-Gamma, Delta-Vega, and Minimum Variance strategies applied. Wide range of market models tested.
result Calibration results indicate stochastic volatility, low jump frequency, and infinite activity. Short-dated options less sensitive to volatility or Gamma hedges; longer-dated options benefit from multiple-instrument hedges.
Study finds option volume imbalance predicts equity market returns.
problem Predicting equity market returns using option volume imbalance.
method Nonlinear analysis of option volumes decomposed into five market participant classes.
result Strong signals of predictability of excess market returns from Market-Maker volumes.
Machine learning models outperform traditional option pricing models.
problem Improving option pricing accuracy using complex models.
method Evaluation of machine learning (NN, RF, CatBoost) and traditional models (Black-Scholes, Heston) on synthetic and real data.
result Machine learning models outperform traditional models in predicting option prices.
Neural-SDE models improve option hedging with lower errors and robustness.
problem Improving option hedging strategies using machine learning.
method Derive sensitivity-based and minimum-variance-based hedging strategies using neural-SDE market models.
result Neural-SDE models achieve lower hedging errors and are more robust than traditional models.
Many independent studies on stocks and futures contracts have established that market impact is proportional to the square-root of the executed volume. Is market impact quantitatively similar for option markets as well? In order to answer this question, we have analyzed the impact of a large proprietary data set of opt…
Model predicts option movements using residual transactions for better market timing.
problem Predicting option movements using standard metrics like open interest and trading volume.
method Analyzes residual transactions, integrates machine learning and regression techniques.
result Identifies early indicators of market trends for better option price forecasting.
In this paper, we focus on option pricing models based on space-time fractional diffusion. We briefly revise recent results which show that the option price can be represented in the terms of rapidly converging double-series and apply these results to the data from real markets. We focus on estimation of model paramete…
The paper proposes machine learning models for option pricing without using historical or implied volatility.
problem Capturing option pricing without traditional volatility inputs.
method Three supervised machine learning approaches using data from multiple assets.
result Trained models outperform or match Black-Scholes formula for option pricing.
The Heston model is validated for option pricing using theoretical derivations and empirical market data.
problem Validating the Heston model for accurate option pricing.
method Theoretical derivations and empirical validations using Monte Carlo simulations and machine learning.
result The Heston model is robust and relevant for current financial markets.
CapOptix uses options theory to price capacity in electricity markets.
problem Traditional capacity market designs fail to account for risk and price shocks.
method Interprets capacity commitments as reliability options and uses Markov Regime Switching Process.
result CapOptix provides more accurate pricing of capacity premia compared to existing mechanisms.
Calibrates carbon futures option pricing using high-frequency data.
problem Estimating equity and variance risk premia for carbon futures options.
method Multifactor stochastic volatility framework with jumps, employing indirect inference.
result Provides insights into carbon futures and option dynamics.
New model prices crypto options by clustering market regimes and using implied volatility.
problem Inaccurate option pricing for volatile crypto markets.
method Time-regime clustering with Implied Stochastic Volatility Model (ISVM).
result MR-ISVM overcomes complexity and adapts to market dynamics.
Modeling option market making with hedging-induced price impact.
problem Tackles the challenge of market making in options markets with price impact.
method Models option order flow using Cox processes and studies the dynamics of inventory and price under hedging-induced impact.
result Establishes the well-posedness of the mixed control problem involving quoting and hedging.
Physics-Informed Neural Network improves option pricing accuracy.
problem Improving option pricing accuracy using machine learning.
method Physics-Informed Neural Network (PINN) applied to Black-Scholes equation.
result PINN model accurately captures option pricing behavior on both simulated and real market data.
The paper models Gasoil options using Brent benchmarks, improving volatility estimation.
problem Inability to directly model illiquid Gasoil options market.
method Jointly models Brent and Gasoil futures prices with a correlated Bachelier model, estimating volatility spread.
result The proposed framework accurately maps Brent implied volatilities to Gasoil implied volatilities.
We develop a trinomial tree model for pricing perpetual derivatives and European options.
problem Pricing perpetual derivatives and European options in a market with two risky assets and a perpetual derivative of one of them.
method We introduce a recombining trinomial tree model, consider a market with two risky assets and a perpetual derivative, and use a replicating portfolio to price options and generate relationships between risk-neutral and real-world parameters.
result We develop implied parameter surfaces for real-world parameters in the model using historical data.
Deep learning improves options trading without market assumptions.
problem Traditional options trading requires market dynamics and pricing models.
method End-to-end deep learning approach that learns from market data.
result Deep learning models outperform existing trading strategies.
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data set…
Paper uses RL to optimize bid-ask spreads for diverse options.
problem Optimizing bid-ask spreads for options with various maturities and strikes.
method Combines stochastic policy with reinforcement learning.
result Proposes an effective approach for market making of options.
Paper presents a novel nonparametric method to price Asian options.
problem Difficulty in pricing Asian options, especially with arithmetic average price.
method Nonparametric Predictive Inference (NPI) for Asian option pricing.
result NPI method provides a more precise and uncertain prediction of future asset prices.
In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underlying's return, whereas gradual changes in implied volatility would indicate market inefficiency. Using minute-by-minute data on S&P 500 inde…
In this paper we provide evidence that financial option markets for equity indices give rise to non-trivial dependency structures between its constituents. Thus, if the individual constituent distributions of an equity index are inferred from the single-stock option markets and combined via a Gaussian copula, for examp…
Proposes a new model to price options considering market forces beyond Black-Scholes.
problem Tackles the limitations of the Black-Scholes model in capturing unexpected market behaviors.
method Uses the analogy between quantum harmonic oscillator and financial market dynamics to propose a new market force-driven model.
result Shows how various market forces can be incorporated to modify option pricing, providing practical applications.
Based on empirical market data, a stochastic volatility model is proposed with volatility driven by fractional noise. The model is used to obtain a risk-neutrality option pricing formula and an option pricing equation.
Marketron model extended to option markets, solving incomplete market challenges.
problem Tackling the challenge of incomplete markets in option pricing.
method Utility-based pricing approach, dual solution of optimal investment problem, Hamilton-Jacobi-Bellman (HJB) equation, novel calibration method.
result The Marketron model calibrated to option markets can reproduce statistical properties of underlying asset's log-returns.
Panoptic trades options without oracles on Ethereum.
problem Trading options without relying on oracles.
method Perpetual, trustless, instant-settlement protocol on Ethereum.
result Trustless, permissionless trading of options on Uniswap v3.
Paper offers a simpler solution for managing complex financial options.
problem Managing a large number of financial assets with diverse dynamics.
method Developed a simple analytical approximation for market making.
result Shows significant flexibility over existing market making strategies.
The paper analyzes binary option markets with exogenous information and price sensitivity.
problem Analyzing binary option markets with exogenous information and price sensitivity.
method Derive and analyze a continuous model of binary option markets with exogenous information, using Filippov surfaces and general assumptions on purchasing rules.
result Price always converges when exogenous information is constant, and price sensitivity affects price lag vs. information.
Enhances binomial model with machine learning for microstructure effects.
problem Traditional binomial models ignore market microstructure effects like bid-ask spreads.
method Augments binomial tree with Random Forest classifiers trained on market data.
result Achieves 88.25% AUC in forecasting price movements using real-world data.
We study Vanna-Volga methods which are used to price first generation exotic options in the Foreign Exchange market. They are based on a rescaling of the correction to the Black-Scholes price through the so-called `probability of survival' and the `expected first exit time'. Since the methods rely heavily on the approp…
Study option pricing in sideways markets and target zones.
problem Option pricing in sideways markets and target zones.
method Closed-form option pricing formulas for sideways markets and target zones.
result Closed-form option pricing formulas for sideways markets and target zones.
Variational autoencoders help estimate missing volatility data.
problem Estimating missing points on partially observed volatility surfaces.
method Derive latent variables, construct synthetic surfaces fitting available data.
result Synthetic volatility surfaces can be used for stress testing and exotic option valuation.
The paper uses option theory to estimate corporate bond liquidity spreads.
problem Estimating liquidity spreads for corporate bonds.
method Option-theoretic approach considering risk-free rate volatility and credit risk.
result The model provides a robust tool for pricing illiquid bonds.
This paper designs a new on-chain option that amortizes perpetual options for blockchain environments.
problem No equivalent standard for on-chain options exists, leading to high-frequency oracles and liquidation engines failures.
method Develops an amortizing perpetual option contract tailored to blockchain constraints, introducing a decentralized market framework.
result Demonstrates that the new contract functions as a risk primitive for DeFi, enabling applications like endogenous collateralization and de-peg insurance.
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
problem Improving vanilla option pricing accuracy during and before the pandemic.
method Combinational Mutation Strategy of Differential Evolution (CmDE) algorithm for bi-objective optimization.
result Algorithm approximates real market vanilla option prices more accurately than Black-Scholes.
GG distribution improves option pricing for negatively skewed spot price distributions.
problem Inaccurate Black-Scholes model for negatively skewed spot price distributions.
method Applied Generalized Gamma (GG) distribution as a Risk-Neutral Density (RND) for Heston's SV model.
result GG distribution better matches market option data with negatively skewed spot price distributions.
Model accurately calibrates FX market skew for exotic options.
problem Inconsistent prices from different models for FX derivatives.
method Fully parameterized local volatility model with numerical methods.
result Model provides reliable prices for daily trading.
Enhanced options trading strategies using advanced portfolio optimization.
problem Generating consistent positive returns in high-frequency options trading.
method Advanced portfolio optimization techniques applied to SPY options data.
result Sophisticated strategies incorporating advanced Greeks show potential in high-frequency trading.
In this work we consider three problems of the standard market approach to pricing of credit index options: the definition of the index spread is not valid in general, the usually considered payoff leads to a pricing which is not always defined, and the candidate numeraire one would use to define a pricing measure is n…
Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first de…