A new technique for calibrating stochastic volatility models: the Malliavin gradient method

DOI10.1080/14697680500531676zbMath1136.91430OpenAlexW2033200020MaRDI QIDQ5484638

Christian-Oliver Ewald, Ai-Hua Zhang

Publication date: 21 August 2006

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: http://www.ssoar.info/ssoar/handle/document/22081

zbMATH Keywords

Malliavin calculusMonte Carlo simulationgradient methodscalibrationstochastic volatility modelsvalue at risk

Mathematics Subject Classification ID

Related Items (6)

Computation of option Greeks under hybrid stochastic volatility models via Malliavin calculus ⋮ Modelling fundamental analysis in portfolio selection ⋮ IMPLIED VOLATILITY FROM ASIAN OPTIONS VIA MONTE CARLO METHODS ⋮ Sensitivity of option prices via fuzzy Malliavin calculus ⋮ On the non-equilibrium density of geometric mean reversion ⋮ Utility based pricing and exercising of real options under geometric mean reversion and risk aversion toward idiosyncratic risk

Uses Software

• PLCP

Cites Work

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• The minimal entropy martingale measures for geometric Lévy processes
• Applications of Malliavin calculus to Monte Carlo methods in finance
• Local volatility in the Heston model: a Malliavin calculus approach
• An Anticipating Calculus Approach to the Utility Maximization of an Insider
• On the Existence of Minimax Martingale Measures
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• Minimax and minimal distance martingale measures and their relationship to portfolio optimization

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