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Sensitivity Analysis in the Dupire Local Volatility Model with Tensorflow

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  • Francois Belletti
  • Davis King
  • James Lottes
  • Yi-Fan Chen
  • John Anderson

Abstract

In a recent paper, we have demonstrated how the affinity between TPUs and multi-dimensional financial simulation resulted in fast Monte Carlo simulations that could be setup in a few lines of python Tensorflow code. We also presented a major benefit from writing high performance simulations in an automated differentiation language such as Tensorflow: a single line of code enabled us to estimate sensitivities, i.e. the rate of change in price of financial instrument with respect to another input such as the interest rate, the current price of the underlying, or volatility. Such sensitivities (otherwise known as the famous financial "Greeks") are fundamental for risk assessment and risk mitigation. In the present follow-up short paper, we extend the developments exposed in our previous work about the use of Tensor Processing Units and Tensorflow for TPUs.

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  • Francois Belletti & Davis King & James Lottes & Yi-Fan Chen & John Anderson, 2020. "Sensitivity Analysis in the Dupire Local Volatility Model with Tensorflow," Papers 2002.02481, arXiv.org.
    • Handle: RePEc:arx:papers:2002.02481
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    1. Francois Belletti & Davis King & Kun Yang & Roland Nelet & Yusef Shafi & Yi-Fan Chen & John Anderson, 2019. "Tensor Processing Units for Financial Monte Carlo," Papers 1906.02818, arXiv.org, revised Jan 2020.
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