Standard
Computational Methods for Derivatives with Early Exercise Features. / Chiarella, Carl; Kang, Boda; Meyer, G.H.; Ziogas, A.
Handbook of Computational Economics. ed. / Karl Schmedders; Kenneth L. Judd. Vol. 3 Elsevier, 2014. p. 225-275.
Research output: Chapter in Book/Report/Conference proceeding › Chapter
Harvard
Chiarella, C
, Kang, B, Meyer, GH & Ziogas, A 2014,
Computational Methods for Derivatives with Early Exercise Features. in K Schmedders & KL Judd (eds),
Handbook of Computational Economics. vol. 3, Elsevier, pp. 225-275.
https://doi.org/10.1016/B978-0-444-52980-0.00005-0
APA
Chiarella, C.
, Kang, B., Meyer, G. H., & Ziogas, A. (2014).
Computational Methods for Derivatives with Early Exercise Features. In K. Schmedders, & K. L. Judd (Eds.),
Handbook of Computational Economics (Vol. 3, pp. 225-275). Elsevier.
https://doi.org/10.1016/B978-0-444-52980-0.00005-0
Vancouver
Chiarella C
, Kang B, Meyer GH, Ziogas A.
Computational Methods for Derivatives with Early Exercise Features. In Schmedders K, Judd KL, editors, Handbook of Computational Economics. Vol. 3. Elsevier. 2014. p. 225-275
https://doi.org/10.1016/B978-0-444-52980-0.00005-0
Author
Chiarella, Carl ; Kang, Boda ; Meyer, G.H. ; Ziogas, A. / Computational Methods for Derivatives with Early Exercise Features. Handbook of Computational Economics. editor / Karl Schmedders ; Kenneth L. Judd. Vol. 3 Elsevier, 2014. pp. 225-275
@inbook{8677fefbff9f469ab8db69c2a75bf117,
title = "Computational Methods for Derivatives with Early Exercise Features",
abstract = "In this paper we consider various computational methods for pricing American style derivatives. We do so under both jump diffusion and stochastic volatility processes. We consider integral transform methods, the method of lines, operator-splitting, and the Crank-Nicolson scheme, the latter being used to generate the benchmark solution. Overall, we find that the method of lines approach is quite competitive with other methods for the problems considered in this paper. As one goes to higher dimensions it may be necessary to use methods such as the sparse grid approach.",
author = "Carl Chiarella and Boda Kang and G.H. Meyer and A. Ziogas",
year = "2014",
month = jan,
doi = "10.1016/B978-0-444-52980-0.00005-0",
language = "English",
isbn = " 978-0-444-52980-0",
volume = "3",
pages = "225--275",
editor = "Karl Schmedders and Judd, {Kenneth L.}",
booktitle = "Handbook of Computational Economics",
publisher = "Elsevier",
address = "Netherlands",
}
RIS (suitable for import to EndNote) - Download
TY - CHAP
T1 - Computational Methods for Derivatives with Early Exercise Features
AU - Chiarella, Carl
AU - Kang, Boda
AU - Meyer, G.H.
AU - Ziogas, A.
PY - 2014/1
Y1 - 2014/1
N2 - In this paper we consider various computational methods for pricing American style derivatives. We do so under both jump diffusion and stochastic volatility processes. We consider integral transform methods, the method of lines, operator-splitting, and the Crank-Nicolson scheme, the latter being used to generate the benchmark solution. Overall, we find that the method of lines approach is quite competitive with other methods for the problems considered in this paper. As one goes to higher dimensions it may be necessary to use methods such as the sparse grid approach.
AB - In this paper we consider various computational methods for pricing American style derivatives. We do so under both jump diffusion and stochastic volatility processes. We consider integral transform methods, the method of lines, operator-splitting, and the Crank-Nicolson scheme, the latter being used to generate the benchmark solution. Overall, we find that the method of lines approach is quite competitive with other methods for the problems considered in this paper. As one goes to higher dimensions it may be necessary to use methods such as the sparse grid approach.
U2 - 10.1016/B978-0-444-52980-0.00005-0
DO - 10.1016/B978-0-444-52980-0.00005-0
M3 - Chapter
SN - 978-0-444-52980-0
VL - 3
SP - 225
EP - 275
BT - Handbook of Computational Economics
A2 - Schmedders, Karl
A2 - Judd, Kenneth L.
PB - Elsevier
ER -