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Working paper
In: 29th Annual Conference on Financial Economics & Accounting 2018
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In: International Journal of Theoretical and Applied Finance, Band 20, Heft 7
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In: Springer Finance
This book offers recent advances in the theory of implied volatility and refined semiparametric estimation strategies and dimension reduction methods for functional surfaces. The first part is devoted to smile-consistent pricing approaches. The second part covers estimation techniques that are natural candidates to meet the challenges in implied volatility surfaces. Empirical investigations, simulations, and pictures illustrate the concepts. Matthias Fengler took his PhD in Finance at the Humboldt-Universität zu Berlin and is now a quantitative analyst at Sal. Oppenheim, Frankfurt.
In: International Journal of Theoretical and Applied Finance, Band 20, Heft 5, S. 1750035
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In: Orrell D (2024) A quantum model of implied volatility. Wilmott 2024(131).
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In: The quarterly review of economics and finance, Band 79, S. 303-329
ISSN: 1062-9769
In: NBER Working Paper No. w5500
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In: Journal of Financial Economics (JFE), Band 123, Heft 1
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In: Journal of economic dynamics & control, Band 134, S. 104276
ISSN: 0165-1889
In: Economic notes, Band 31, Heft 2, S. 361-377
ISSN: 1468-0300
We propose a market–based approach to the modelling of implied volatility, in which the implied volatility surface is directly used as the state variable to describe the joint evolution of market prices of options and their underlying asset. We model the evolution of an implied volatility surface by representing it as a randomly fluctuating surface driven by a finite number of orthogonal random factors. Our approach is based on a Karhunen–Loeve decomposition of the daily variations of implied volatilities obtained from market data on SP500 and DAX options.We illustrate how this approach extends and improves the accuracy of the well–known 'sticky moneyness' rule used by option traders for updating implied volatilities. Our approach gives a justification for the use of 'Vegas' for measuring volatility risk and provides a decomposition of volatility risk as a sum of independent contributions from empirically identifiable factors.(J.E.L.: G130, C14, C31).
In: Management Science, Band 59
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