arXiv research

Generative model prices options and extracts risk-neutral densities.

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…

Framework improves risk neutral density estimation in illiquid markets.

A model-free framework extracts risk-neutral densities from short-dated options.

Proposes a method to construct risk-neutral marginals from arbitrage-free option prices.

Generative model uses DDPMs for risk-neutral derivative pricing.

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…

iCOS method estimates risk-neutral densities and option prices without model assumptions.

Deep Hedging learns risk-neutral vol dynamics for option pricing.

We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies -small samples, limited models and non-holistic validations- by performing a comprehensive comparison of 15 predictive schemes during a time period of over…

We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting $N$-component Gaussian mixture models to option quotes, where $N$ is a small integer (here 4 or 5). These densities are…

The study finds that specific distributions can be used for risk-neutral valuation in Heston's SV model.

This paper provides a neural approach to represent option implied information.

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…

This paper is concerned with the asymptotics for Greeks of European-style options and the risk-neutral density function calculated under the constant elasticity of variance model. Formulae obtained help financial engineers to construct a perfect hedge with known behaviour and to price any options on financial assets.

Optimizes risk-neutral probabilities for derivative pricing.

In this note, we consider European options of type $h(X^1_T, X^2_T,\ldots, X^n_T)$ depending on several underlying assets. We give a multidimensional version of the result of Breeden and Litzenberger \cite{Breeden} on the relation between derivatives of the call price and the risk-neutral density of the underlying asse…

The paper shows how to calculate risk-neutral default probabilities from bid and ask CDS quotes.

Simulates risk-neutral markets using neural spline flows.

Paper introduces benchmark-neutral pricing for long-term contracts.

Currently, machine learning plays an important role in the lives and individual activities of numerous people. Accordingly, it has become necessary to design machine learning algorithms to ensure that discrimination, biased views, or unfair treatment do not result from decision making or predictions made via machine le…

Project estimates risk-neutral dependence from option prices.

The paper reviews historical and modern approaches to asset pricing probability measures.

Paper studies pricing and hedging of nonreplicable insurance contracts using benchmark-neutral approach.

The paper bounds payoffs and option prices in discrete models.

We consider a defaultable asset whose risk-neutral pricing dynamics are described by an exponential Levy-type martingale subject to default. This class of models allows for local volatility, local default intensity, and a locally dependent Levy measure. Generalizing and extending the novel adjoint expansion technique o…

Investment strategy for NYSE stocks minimizes market correlation.

The paper shows that benchmark-neutral pricing minimizes option prices.

Develops a binary tree model for option pricing with skew dynamics.

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…

Developed Merton's model for public companies using observed liabilities.

We develop a framework for interacting with uncertain environments in reinforcement learning (RL) by leveraging preferences in the form of utility functions. We claim that there is value in considering different risk measures during learning. In this framework, the preference for risk can be tuned by variation of the p…

AlphaZeroBeta uses deep reinforcement learning for market-neutral portfolios, outperforming traditional methods.

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…

Framework for transitioning financial models from risk-neutral to real-world measure.

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…

Enhanced Gordon growth model for valuing financial products.

We develop an entropic framework to model the dynamics of stocks and European Options. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. The objective of the paper is to lay down an alternative framework for modeling dynamic…

In this paper, we propose a new method for estimating the conditional risk-neutral density (RND) directly from a cross-section of put option bid-ask quotes. More precisely, we propose to view the RND recovery problem as an inverse problem. We first show that it is possible to define restricted put and call operators th…

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…

New formula for portfolio risk management using conditional PDEs.

The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.

Unified kernel for prediction markets reduces belief variance forecast error.

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.

Simplified matrix generator resolves credit migration model calibration issues.