On the data-driven COS method

Álvaro Leitao, Cornelis W. Oosterlee, Luis Ortiz-Gracia, Sander Bohte

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

Abstract

In this paper, we present the data-driven COS method, ddCOS. It is a Fourier-based financial option valuation method which assumes the availability of asset data samples: a characteristic function of the underlying asset probability density function is not required. As such, the presented technique represents a generalization of the well-known COS method [1]. The convergence of the proposed method is O(1/n) in line with Monte Carlo methods for pricing financial derivatives. The ddCOS method is then particularly interesting for density recovery and also for the efficient computation of the option's sensitivities Delta and Gamma. These are often used in risk management, and can be obtained at a higher accuracy with ddCOS than with plain Monte Carlo methods.

Original languageEnglish
Pages (from-to)68-84
Number of pages17
JournalApplied Mathematics and Computation
Volume317
DOIs
Publication statusPublished - 2018

Keywords

  • Data-driven approach
  • Delta–Gamma approach
  • Density estimation
  • Greeks
  • The COS method
  • The SABR model

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