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.
Simulates risk-neutral markets using neural spline flows.
Study shows physical drift affects put-call parity enforcement, not just option payoffs.
Unified kernel for prediction markets reduces belief variance forecast error.
The paper shows that benchmark-neutral pricing minimizes option prices.
Generative model uses DDPMs for risk-neutral derivative pricing.
In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. With…
Novel method prices call options using Pearson diffusion processes.
We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricti…
We describe a model for evolving commodity forward prices that incorporates three important dynamics which appear in many commodity markets: mean reversion in spot prices and the resulting Samuelson effect on volatility term structure, decorrelation of moves in different points on the forward curve, and implied volatil…
Deriving option prices from operational-time Markov lattices
Most models for barrier pricing are designed to let a market maker tune the model-implied covariance between moves in the asset spot price and moves in the implied volatility skew. This is often implemented with a local volatility/stochastic volatility mixture model, where the mixture parameter tunes that covariance. T…
Optimizes risk-neutral probabilities for derivative pricing.
We investigate the relation between the fair price for European-style vanilla options and the distribution of short-term returns on the underlying asset ignoring transaction and other costs. We compute the risk-neutral probability density conditional on the total variance of the asset's returns when the option expires.…
The paper shows how to calculate risk-neutral default probabilities from bid and ask CDS quotes.
The 1993 Laplace transform approach of Geman and Yor is a celebrated advance in valuing Asian options. Its insights are fundamental from both a mathematical and a financial perspective. In this paper, we discuss two observations regarding the financial relevance of its results. First, we show that the Geman and Yor Lap…
Project estimates risk-neutral dependence from option prices.
Generative model prices options and extracts risk-neutral densities.
We design three continuous--time models in finite horizon of a commodity price, whose dynamics can be affected by the actions of a representative risk--neutral producer and a representative risk--neutral trader. Depending on the model, the producer can control the drift and/or the volatility of the price whereas the tr…
To convert standard Brownian motion into a positive process, Geometric Brownian motion (GBM) is widely used. We generalize this positive process by introducing an asymmetry parameter which describes the instantaneous volatility whenever the process reaches a new low. For our new process, …
Deep Hedging learns risk-neutral vol dynamics for option pricing.
Proposes a method to construct risk-neutral marginals from arbitrage-free option prices.
Two models incorporate market microstructure noise into asset pricing and option valuation.
Develops a binary tree model for option pricing with skew dynamics.
The study models credit risk using Merton's framework and binomial trees.
The risk-neutral option pricing method under GARCH intensity model is examined. The GARCH intensity model incorporates the characteristics of financial return series such as volatility clustering, leverage effect and conditional asymmetry. The GARCH intensity option pricing model has flexibility in changing the volatil…
The paper bounds payoffs and option prices in discrete models.
Paper introduces benchmark-neutral pricing for long-term contracts.
We consider a class of assets whose risk-neutral pricing dynamics are described by an exponential Lévy-type process subject to default. The class of processes we consider features locally-dependent drift, diffusion and default-intensity as well as a locally-dependent Lévy measure. Using techniques from regular perturba…
This paper highlights the role of risk neutral investors in generating endogenous bubbles in derivatives markets. We find that a market for derivatives, which has all the features of a perfect market except completeness and has some risk neutral investors, can exhibit extreme price movements which represent a violation…
Study on VIX options pricing in SABR model, showing infinite prices due to volatility explosion.
Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…
A new method calculates implied volatilities without using option prices.
Quantum Portfolios of quantum algorithms encoded on qbits have recently been reported. In this paper a discussion of the continuous variables version of quantum portfolios is presented. A risk neutral valuation model for options dependent on the measured values of the observables, analogous to the traditional Black-Sch…
Framework improves risk neutral density estimation in illiquid markets.
This review covers learning under concept drift, including detection, understanding, and adaptation.
In this paper we consider the pricing of variable annuities (VAs) with guaranteed minimum withdrawal benefits. We consider two pricing approaches, the classical risk-neutral approach and the benchmark approach, and we examine the associated static and optimal behaviors of both the investor and insurer. The first model …
New method recovers BSDE from financial data without ergodicity.
This paper surveys options pricing under arithmetic Brownian motion and derives formulas for various types of options.
Identifies features most relevant to concept drift in data.
A risk-neutral valuation framework is developed for pricing and hedging in-play football bets based on modelling scores by independent Poisson processes with constant intensities. The Fundamental Theorems of Asset Pricing are applied to this set-up which enables us to derive novel arbitrage-free valuation formulæ for c…
In this paper we present a new multi-asset pricing model, which is built upon newly developed families of solvable multi-parameter single-asset diffusions with a nonlinear smile-shaped volatility and an affine drift. Our multi-asset pricing model arises by employing copula methods. In particular, all discounted single-…
New method detects when models influence their own drift in real-time data streams.
Unified framework matches equity and bond yields.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. Albeit many attempts were made to deal with drift, formal notions of drift are application-dependent and formulated in various degrees of abstraction and mathematical coherence. In this contribu…
A new drift detection method based on autoregressive models.
Adaptive sampling detects local concept drift with limited labels.
Algorithm detects concept drift and adapts models in streaming data.