A novel Monte Carlo approach to hybrid local volatility models

Journal Article (2017)
Authors

A.W. van der Stoep (TU Delft - Numerical Analysis, Centrum Wiskunde & Informatica (CWI), Rabobank)

LA Grzelak (ING Bank, TU Delft - Numerical Analysis)

C. W. Oosterlee (Centrum Wiskunde & Informatica (CWI), TU Delft - Numerical Analysis, ING Bank)

Research Group
Numerical Analysis
Copyright
© 2017 A.W. van der Stoep, L.A. Grzelak, C.W. Oosterlee
To reference this document use:
https://doi.org/10.1080/14697688.2017.1280613
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 A.W. van der Stoep, L.A. Grzelak, C.W. Oosterlee
Research Group
Numerical Analysis
Bibliographical Note
Accepted Author Manuscript@en
Issue number
9
Volume number
17
Pages (from-to)
1347-1366
DOI:
https://doi.org/10.1080/14697688.2017.1280613
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Abstract

We present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant. Finance, 2014, 14, 1899–1922], Piterbarg [Risk, 2007, April, 84–89], Tataru and Fisher [Quantitative Development Group, Bloomberg Version 1, 2010], Lipton [Risk, 2002, 15, 61–66]—and the local volatility model incorporating stochastic interest rates—see e.g. Atlan [ArXiV preprint math/0604316, 2006], Piterbarg [Risk, 2006, 19, 66–71], Deelstra and Rayée [Appl. Math. Finance, 2012, 1–23], Ren et al. [Risk, 2007, 20, 138–143]. For both model classes a particular (conditional) expectation needs to be evaluated which cannot be extracted from the market and is expensive to compute. We establish accurate and ‘cheap to evaluate’ approximations for the expectations by means of the stochastic collocation method [SIAM J. Numer. Anal., 2007, 45, 1005–1034], [SIAM J. Sci. Comput., 2005, 27, 1118–1139], [Math. Models Methods Appl. Sci., 2012, 22, 1–33], [SIAM J. Numer. Anal., 2008, 46, 2309–2345], [J. Biomech. Eng., 2011, 133, 031001], which was recently applied in the financial context [Available at SSRN 2529691, 2014], [J. Comput. Finance, 2016, 20, 1–19], combined with standard regression techniques. Monte Carlo pricing experiments confirm that our method is highly accurate and fast.

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