Stochastic Volatility Modeling

Zusatztext "With this book! Bergomi has actually offered a precious gift to the whole quant community: his very rich and concrete experience on volatility modelling organized in 500 pages and 12 chapters full of insights; and to the academic community as well: new ideas! points of view! and questions that could well feed their research for years."- Julien Guyon! Quantitative Finance"[Stochastic Volatility Modeling] should be read by practitioners! as it is the only one providing a strong quantitative framework to the (Delta and Vega) hedging of Equity derivatives. It should also be read by academics who will benefit from practical insights. It should finally be read by (motivated) students! who will definitely find areas to dig deeper in! both theoretically and numerically [?] This book should be seen as a strong case for the need of a deeper understanding of derivatives' modelling (and their risks). Lorenzo Bergomi provides us here with new tools (variance curve models! metrics such as the At-The-Money Forward Skew and the Skew Stickiness Ratio) as well as new results on hedging and P&L computations of actual trading strategies! which have been so far too much overlooked in mathematical finance research. Welcome to the new era of Derivatives Modelling!"- Antoine Jacquier! Newsletter of the Bachelier Finance Society! November 2017 Informationen zum Autor Lorenzo Bergomi heads the quantitative research group at Société Générale, covering all asset classes. A quant for over 15 years, he is well known for his pioneering work on stochastic volatility modeling, some of which has appeared in the Smile Dynamics series of articles in Risk magazine. He was also the magazine's 2009 Quant of the Year. Originally trained as an electrical engineer and with a PhD in theoretical physics, he was active as a physicist in the condensed matter theory group at IphT, CEA, before moving to finance. Klappentext Written by a leading contributor to volatility modeling and Risk s 2009 Quant of the Year, this book explains how stochastic volatility is used to tackle practical issues arising in the modeling of derivatives. With many unpublished results and insights, the book addresses the practicalities of modeling local volatility, local-stochastic volatility, and multi-asset stochastic volatility. It covers forward-start options, variance swaps, options on realized variance, timer options, VIX futures and options, and daily cliquets. Zusammenfassung Packed with insights! Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives! including:Which trading issues do we tackle with stochastic volatility? How do we design models and assess their relevance? How do we tell which models are usable and when does calibration make sense?This manual covers the practicalities of modeling local volatility! stochastic volatility! local-stochastic volatility! and multi-asset stochastic volatility. In the course of this exploration! the author! Risk's 2009 Quant of the Year and a leading contributor to volatility modeling! draws on his experience as head quant in Société Générale's equity derivatives division. Clear and straightforward! the book takes readers through various modeling challenges! all originating in actual trading/hedging issues! with a focus on the practical consequences of modeling choices. Inhaltsverzeichnis Introduction. Local volatility. Forward-start options. Stochastic volatility: introduction. Variance swaps. An example of one-factor dynamics: the Heston model. Forward variance models. The smile of stochastic volatility models. Linking static and dynamic properties of stochastic volatility models. What causes equity smiles? Multi-asset stochastic volatility. Local-stochastic volatility models. ...

Autorentext

Lorenzo Bergomi heads the quantitative research group at Société Générale, covering all asset classes. A quant for over 15 years, he is well known for his pioneering work on stochastic volatility modeling, some of which has appeared in the Smile Dynamics series of articles in Risk magazine. He was also the magazine's 2009 Quant of the Year. Originally trained as an electrical engineer and with a PhD in theoretical physics, he was active as a physicist in the condensed matter theory group at IphT, CEA, before moving to finance.

Klappentext

Written by a leading contributor to volatility modeling and Risk's 2009 Quant of the Year, this book explains how stochastic volatility is used to tackle practical issues arising in the modeling of derivatives. With many unpublished results and insights, the book addresses the practicalities of modeling local volatility, local-stochastic volatility, and multi-asset stochastic volatility. It covers forward-start options, variance swaps, options on realized variance, timer options, VIX futures and options, and daily cliquets.

Zusammenfassung

Packed with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives, including:

• Which trading issues do we tackle with stochastic volatility?
• How do we design models and assess their relevance? *
• How do we tell which models are usable and when does calibration make sense?* This manual covers the practicalities of modeling local volatility, stochastic volatility, local-stochastic volatility, and multi-asset stochastic volatility. In the course of this exploration, the author, Risk's 2009 Quant of the Year and a leading contributor to volatility modeling, draws on his experience as head quant in Société Générale's equity derivatives division. Clear and straightforward, the book takes readers through various modeling challenges, all originating in actual trading/hedging issues, with a focus on the practical consequences of modeling choices.

Inhalt

Introduction. Local volatility. Forward-start options. Stochastic volatility: introduction. Variance swaps. An example of one-factor dynamics: the Heston model. Forward variance models. The smile of stochastic volatility models. Linking static and dynamic properties of stochastic volatility models. What causes equity smiles? Multi-asset stochastic volatility. Local-stochastic volatility models.