Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)

Using Python to Find the Replication Error if Delta Hedge a Trinomial Tree Option Over Many Short Periods

Authors
Nuoxing Shang1, Yujia Liu2, Zewei Lin3, *
1Jiangsu Tianyi High School, No.18 Erquan Road Xishan District Wuxing City Jiangsu Province, 214101
2Jiangsu Tianyi High School, No.18 Erquan Road Xishan District Wuxing City Jiangsu Province, 214101
3School of Economics and Finance, Queen Mary University of London, London E1 4NS, United Kingdom
*Corresponding author. Email: eltham_nj@hotmail.com
Corresponding Author
Zewei Lin
Available Online 8 April 2022.
DOI
10.2991/assehr.k.220401.136How to use a DOI?
Keywords
Python; Delta hedgem; replication error; Monte-Carlo estimation
Abstract

In this paper, the researcher creates a model for trinomial tree option pricing with multiple time periods by using Monte-Carlo estimation and Python. However, the delta hedging strategy needs to be improved to minimize the replication error.

Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license.

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Volume Title
Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
8 April 2022
ISBN
978-94-6239-560-2
ISSN
2352-5398
DOI
10.2991/assehr.k.220401.136How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license.

Cite this article

TY  - CONF
AU  - Nuoxing Shang
AU  - Yujia Liu
AU  - Zewei Lin
PY  - 2022
DA  - 2022/04/08
TI  - Using Python to Find the Replication Error if Delta Hedge a Trinomial Tree Option Over Many Short Periods
BT  - Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022)
PB  - Atlantis Press
SP  - 713
EP  - 718
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220401.136
DO  - 10.2991/assehr.k.220401.136
ID  - Shang2022
ER  -