A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
We price and hedge American options robustly in continuous time.
Study finds adding more information to robust option pricing does not improve bounds.
Efficiently computes robust option prices using multi-marginal martingale transport.
Study optimizes option pricing with robust strategies, ensuring consistency with vanilla option prices.
New method for European option pricing faster and more robust.
In this paper an improved Cuckoo Search Algorithm is developed to allow for an efficient and robust calibration of the Heston option pricing model for American options. Calibration of stochastic volatility models like the Heston is significantly harder than classical option pricing models as more parameters have to be …
Fast probabilistic option price predictions using modular Bayesian inference.
We consider robust pricing and hedging for options written on multiple assets given market option prices for the individual assets. The resulting problem is called the multi-marginal martingale optimal transport problem. We propose two numerical methods to solve such problems: using discretisation and linear programmin…
We propose a robust and stable lattice method which permits to obtain very accurate American option prices in presence of CIR stochastic interest rate without any numerical restriction on its parameters. Numerical results show the reliability and the accuracy of the proposed method.
Paper presents new expansions for option pricing with cash dividends.
Sinh-acceleration speeds up B-spline option pricing.
Paper reduces dimensionality for robust option pricing in 2-asset markets.
Since Hobson's seminal paper [D. Hobson: Robust hedging of the lookback option. In: Finance Stoch. (1998)] the connection between model-independent pricing and the Skorokhod embedding problem has been a driving force in robust finance. We establish a general pricing-hedging duality for financial derivatives which are s…
Weighted Monte Carlo prices exotic options calibrating the probabilities of previously generated paths by a regular Monte Carlo to fit a set of option premiums. When only vanilla call and put options and forward prices are considered, the Martingale condition might not be preserved. This paper shows that this is indeed…
Improved price bounds for multi-asset derivatives using market option data.
We investigate pricing-hedging duality for American options in discrete time financial models where some assets are traded dynamically and others, e.g. a family of European options, only statically. In the first part of the paper we consider an abstract setting, which includes the classical case with a fixed reference …
New method calibrates crypto option prices more robustly.
Unified deep sequential and state-space models for robust option pricing with uncertainty.
AES scheme improves Bermudan and American option pricing for Heston models.
New method for pricing discrete Asian and Lookback options under Heston model.
Enhanced Black-Scholes model for option pricing with stochastic volatility and interest rate variability.
A new FFT method for Heston model option pricing with explicit error bounds.
Reinforcement learning improves option pricing and hedging accuracy.
This paper analyzes hedge errors in Black-Scholes models using finite difference techniques.
Develops a framework for consistent pricing of interest rate derivatives.
Paper uses deep learning to price and hedge options in incomplete markets.
The COS method proposed in Fang and Oosterlee (2008), although highly efficient, may lack robustness for a number of cases. In this paper, we present a Stable pricing of call options based on Fourier cosine series expansion. The Stability of the pricing methods is demonstrated by error analysis, as well as by a series …
Robust, or model-independent properties of the variance swap are well-known, and date back to Dupire and Neuberger, who showed that, given the price of co-terminal call options, the price of a variance swap was exactly specified under the assumption that the price process is continuous. In Cox and Wang we showed that a…
Paper proposes a method to robustly estimate volatility from OTM options.
This paper uses deep learning to price American options under stochastic volatility.
Accounting for model uncertainty in risk management and option pricing leads to infinite dimensional optimization problems which are both analytically and numerically intractable. In this article we study when this hurdle can be overcome for the so-called optimized certainty equivalent risk measure (OCE) -- including t…
New algorithm calibrates local volatility from option prices using deep neural networks.
We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem …
The virtue of an American option is that it can be exercised at any time. This right is particularly valuable when there is model uncertainty. Yet almost all the extensive literature on American options assumes away model uncertainty. This paper quantifies the potential value of this flexibility by identifying the supr…
ETCNN uses neural networks to price American options accurately.
The Heston model is validated for option pricing using theoretical derivations and empirical market data.
We consider the pricing of derivatives in a setting with trading restrictions, but without any probabilistic assumptions on the underlying model, in discrete and continuous time. In particular, we assume that European put or call options are traded at certain maturities, and the forward price implied by these option pr…
Option contracts are a type of financial derivative that allow investors to hedge risk and speculate on the variation of an asset's future market price. In short, an option has a particular payout that is based on the market price for an asset on a given date in the future. In 1973, Black and Scholes proposed a valuati…
Proof that under simple assumptions, such as constraints of Put-Call Parity, the probability measure for the valuation of a European option has the mean derived from the forward price which can, but does not have to be the risk-neutral one, under any general probability distribution, bypassing the Black-Scholes-Merton …
Proposes a method to repair arbitrage in option prices data.
The paper extends Strassen's theorem to include biased martingales for American options.
Paper uses ML for high-dimensional option pricing under uncertain volatility model.
We describe the pricing and hedging of financial options without the use of probability using rough paths. By encoding the volatility of assets in an enhancement of the price trajectory, we give a pathwise presentation of the replication of European options. The continuity properties of rough-paths allow us to generali…
Double no-touch options, contracts which pay out a fixed amount provided an underlying asset remains within a given interval, are commonly traded, particularly in FX markets. In this work, we establish model-free bounds on the price of these options based on the prices of more liquidly traded options (call and digital …
The Black-Scholes theory of option pricing has been considered for many years as an important but very approximate zeroth-order description of actual market behavior. We generalize the functional form of the diffusion of these systems and also consider multi-factor models including stochastic volatility. Daily Eurodoll…
Paper establishes robust asset pricing theorems under uncertainty.
Solves super-hedging for financial models with uncertain prices.
The paper analyzes robustness and sensitivity of rough Volterra stochastic volatility models.