TY - JOUR
T1 - On the data-driven COS method
AU - Leitao, Álvaro
AU - Oosterlee, Cornelis W.
AU - Ortiz-Gracia, Luis
AU - Bohte, Sander
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data-driven approach
KW - Delta–Gamma approach
KW - Density estimation
KW - Greeks
KW - The COS method
KW - The SABR model
UR - http://www.scopus.com/inward/record.url?scp=85029508377&partnerID=8YFLogxK
U2 - 10.1016/j.amc.2017.09.002
DO - 10.1016/j.amc.2017.09.002
M3 - Article
AN - SCOPUS:85029508377
SN - 0096-3003
VL - 317
SP - 68
EP - 84
JO - Applied Mathematics and Computation
JF - Applied Mathematics and Computation
ER -