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Counter-cyclical Margins for Option Portfolios

  • Yuanyuan CHEN
  • , Qi WU*
  • , Duan LI
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

We propose a counter-cyclical initial margin model for option portfolios. Our model explores the intrinsic netting within a given portfolio of European options and outputs a constant upper bound of the maximum possible loss. This feature would allow option clearinghouses and regulators to gauge the tightest margin levels that are stable. We compare our model with the scenario-based SPAN model and the sensitivitybased SIMM model in terms of the netting efficiency and the procyclical property. Using the SPX options and the interest rate swaptions as examples, we quantify the minimum amount of additional margins needed to make them fully counter-cyclical. We then show how to strike a balance between risk-sensitivity and counter-cyclicality if needed by mixing our model flexibly with a prevailing risk-sensitive margin model.
Original languageEnglish
Article number104572
JournalJournal of Economic Dynamics and Control
Volume146
Online published25 Nov 2022
DOIs
Publication statusPublished - Jan 2023

Funding

Yuanyuan Chen acknowledges the support by NSFC grants 72001105 and 72171109, and the Jiangsu Provincial NSFC grant BK20190278. Duan Li acknowledges the support from the Hong Kong Research Grants Council [Grants CUHK414513 and CUHK14204514] and is grateful for the support from the Patrick Huen Wing Ming Chair Professorship of Systems Engineering and Engineering Management. Qi Wu acknowledges the support from the Hong Kong Research Grants Council [General Research Fund 14206117, 11219420 and 11200219], CityU SRG-Fd fund 7005300 and is grateful for the support from the CityU-JD Digits Laboratory in Financial Technology and Engineering, HK Institute of Data Science. The work described in this paper was partially supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies.

RGC Funding Information

  • RGC-funded

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