Results 11 to 20 of about 3,977,338 (275)
Calibration Design of Implied Volatility Surfaces [PDF]
SSRN Electronic Journal, 2006 The calibration of option pricing models leads to the minimization of an error functional. We show that its usual specification as a root mean squared error implies fluctuating exotics prices and possibly wrong prices. We propose a simple and natural method to overcome these problems, illustrate drawbacks of the usual approach and show advantages of ...
Kai Detlefsen, Wolfgang Härdle
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Analysis of Implied Volatility Surfaces [PDF]
, 2022 The volatility of financial assets is an important parameter in risk managemant, portfolio trading and option pricing. Implied volatility (IV) is obtained from the well known Black-Scholes (BS) formula for option pricing, when the option price is known.
Schnellen, Marina +3 more
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Journal of Function Spaces, 2021 It has been found that the surface of implied volatility has appeared in financial market embrace volatility “Smile” and volatility “Smirk” through the long-term observation.
Yanli Zhou +3 more
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Calibrating FBSDEs Driven Models in Finance via NNs
Risks, 2022 The curse of dimensionality problem refers to a set of troubles arising when dealing with huge amount of data as happens, e.g., applying standard numerical methods to solve partial differential equations related to financial modeling.
Luca Di Persio +2 more
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Simulation of Arbitrage-Free Implied Volatility Surfaces
Applied Mathematical Finance, 2023 We present a computationally tractable method for simulating arbitrage-free implied volatility surfaces. We illustrate how our method may be combined with a data-driven model based on historical SPX implied volatility data to generate dynamic scenarios for arbitrage-free implied volatility surfaces. Our approach conciliates static arbitrage constraints
Cont, R, Vuletić, M
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Forecasting Implied Volatility Surfaces [PDF]
SSRN Electronic Journal, 2008 This paper introduces a new semi-parametric methodology for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility.
Audrino, Francesco, Colangelo, Dominik
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A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models
Risks, 2020 We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface.
Christa Cuchiero +2 more
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Pricing vanilla options using artificial neural networks: Application to the South African market
Cogent Economics & Finance, 2021 In this paper, a feed-forward artificial neural network (ANN) is used to price Johannesburg Stock Exchange (JSE) Top 40 European call options using a constructed implied volatility surface.
Ryno du Plooy, Pierre J. Venter
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South African Journal of Economic and Management Sciences, 2017 Background: Contingent claims on underlying assets are typically priced under a framework that assumes, inter alia, that the log returns of the underlying asset are normally distributed.
Emlyn Flint, Eben Maré
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Sound Deposit Insurance Pricing Using a Machine Learning Approach
Risks, 2019 While the main conceptual issue related to deposit insurances is the moral hazard risk, the main technical issue is inaccurate calibration of the implied volatility. This issue can raise the risk of generating an arbitrage.
Hirbod Assa +2 more
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