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Robust hedging gans
The availability of deep hedging has opened new horizons for solving hedging problems
under a large variety of realistic market conditions. At the same time, any model-be it a …
under a large variety of realistic market conditions. At the same time, any model-be it a …
Robust deep hedging
We study pricing and hedging under parameter uncertainty for a class of Markov processes
which we call generalized affine processes and which includes the Black–Scholes model as …
which we call generalized affine processes and which includes the Black–Scholes model as …
Deep signature FBSDE algorithm
We propose a deep signature/log-signature FBSDE algorithm to solve forward-backward
stochastic differential equations (FBSDEs) with state and path dependent features. By …
stochastic differential equations (FBSDEs) with state and path dependent features. By …
[HTML][HTML] Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty
TK Siu - Journal of Risk and Financial Management, 2024 - mdpi.com
This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower
and upper estimates for prices of Ether options, that is, options written on Ethereum, with …
and upper estimates for prices of Ether options, that is, options written on Ethereum, with …
An efficient computational method for solving the fractional form of the European option price PDE with transaction cost under the fractional Heston model
This paper introduces a new method for precise option pricing analysis by utilizing the
fractional form of the option price partial differential equation (PDE) and implementing a …
fractional form of the option price partial differential equation (PDE) and implementing a …
Robust Hedging GANs: Towards Automated Robustification of Hedging Strategies
The availability of deep hedging has opened new horizons for solving hedging problems
under a large variety of realistic market conditions. At the same time, any model–be it a …
under a large variety of realistic market conditions. At the same time, any model–be it a …
Valuation of European options under an uncertain market price of volatility risk
We propose a model to quantify the effect of parameter uncertainty on the option price in the
Heston model. More precisely, we present a Hamilton–Jacobi–Bellman framework which …
Heston model. More precisely, we present a Hamilton–Jacobi–Bellman framework which …
Classical Option Pricing and Some Steps Further
V Olkhov - arxiv preprint arxiv:2004.13708, 2020 - arxiv.org
This paper considers the asset price p as relations C= pV between the value C and the
volume V of the executed transactions and studies the consequences of this definition for the …
volume V of the executed transactions and studies the consequences of this definition for the …
Time Discretized Variational Iteration Method for the Stochastic Volatility Process with Jumps
A model for both stochastic jumps and volatility for equity returns in the area of option pricing
is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in …
is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in …
A Bayesian nonparametric approach to option pricing
Accurately modeling the implied volatility surface is of great importance to option pricing,
trading and hedging. In this paper, we investigate the use of a Bayesian nonparametric …
trading and hedging. In this paper, we investigate the use of a Bayesian nonparametric …