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.
Optimizes risk-neutral probabilities for derivative pricing.
Generative model uses DDPMs for risk-neutral derivative pricing.
The paper shows how to calculate risk-neutral default probabilities from bid and ask CDS quotes.
Proposes a method to construct risk-neutral marginals from arbitrage-free option prices.
The study finds that specific distributions can be used for risk-neutral valuation in Heston's SV model.
The paper bounds payoffs and option prices in discrete models.
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 …
Simulates risk-neutral markets using neural spline flows.
Project estimates risk-neutral dependence from option prices.
We investigate the existence of affine realizations for Lévy driven interest rate term structure models under the real-world probability measure, which so far has only been studied under an assumed risk-neutral probability measure. For models driven by Wiener processes, all results obtained under the risk-neutral appro…
Online learning has traditionally focused on the expected rewards. In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied. Both the bandit and full information settings are considered. The performance of several existing policies is analyzed, an…
Generative model prices options and extracts risk-neutral densities.
New formula for portfolio risk management using conditional PDEs.
We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem. We evaluate our approach using the S\&P 500 market option prices from 1996 to 2015. A comprehensive cross-validation study shows that our approac…
Deep Hedging learns risk-neutral vol dynamics for option pricing.
This paper proposes to model asset price dynamics with a mixture of diffusion processes where the instantaneous volatility of the underlying diffusion process contains a random vector. The marginal probability distributions of the proposed process can match exactly the risk-neutral distributions implied by both spot va…
In a discrete-time financial market, a generalized duality is established for model-free superhedging, given marginal distributions of the underlying asset. Contrary to prior studies, we do not require contingent claims to be upper semicontinuous, allowing for upper semi-analytic ones. The generalized duality stipulate…
Novel method prices call options using Pearson diffusion processes.
Develops a binary tree model for option pricing with skew dynamics.
The paper introduces a new short rate model with memory components.
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…
Paper introduces benchmark-neutral pricing for long-term contracts.
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…
Trading strategy advantage based on information asymmetry.
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…
The importance of counterparty credit risk to the derivative contracts was demonstrated consistently throughout the financial crisis of 2008. Accurate valuation of Credit value adjustment (CVA) is essential to reflect the economic values of these risks. In the present article, we reviewed several different approaches f…
Framework improves risk neutral density estimation in illiquid markets.
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.
Our derivation of the distribution function for future returns is based on the risk neutral approach which gives a functional dependence for the European call (put) option price, C(K), given the strike price, K, and the distribution function of the returns. We derive this distribution function using for C(K) a Black-Sc…
This paper surveys options pricing under arithmetic Brownian motion and derives formulas for various types of options.
We provide explicit conditions on the distribution of risk-neutral log-returns which yield sharp asymptotic estimates on the implied volatility smile. We allow for a variety of asymptotic regimes, including both small maturity (with arbitrary strike) and extreme strike (with arbitrary bounded maturity), extending previ…
Entropy based ideas find wide-ranging applications in finance for calibrating models of portfolio risk as well as options pricing. The abstracted problem, extensively studied in the literature, corresponds to finding a probability measure that minimizes relative entropy with respect to a specified measure while satisfy…
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…
Unified framework matches equity and bond yields.
Two new methods for option pricing without or with a riskless asset.
Method uses trinomial trees to price nontraditional options.
Developed Merton's model for public companies using observed liabilities.
Paper derives Thiele's equation for unit-linked policies in a stochastic volatility model.
Enhanced Gordon growth model for valuing financial products.
Simplified matrix generator resolves credit migration model calibration issues.
Framework for transitioning financial models from risk-neutral to real-world measure.
Unified kernel for prediction markets reduces belief variance forecast error.
Solves ambiguity in incomplete markets by minimizing price measure entropy.
We build on the work in Fackler and King 1990, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our approach allows for possible time de…
iCOS method estimates risk-neutral densities and option prices without model assumptions.