Skip to main content

Pricing Options with Portfolio-based Option Trading Agents in Direct Double Auction.

Musial-Gabrys, K., Abdullaev, S. and McBurney, P., 2016. Pricing Options with Portfolio-based Option Trading Agents in Direct Double Auction. In: Kaminka, G.A., ed. ECAI 2016. IOS Press, 1754-1755.

Full text available as:

[img]
Preview
PDF
paper_Abdullaev.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

2MB
[img]
Preview
PDF
FAIA285-1754.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

240kB

DOI: 10.3233/978-1-61499-672-9-1754

Abstract

Options constitute integral part of modern financial trades, and are priced according to the risk associated with buying or selling certain asset in future. Financial literature mostly concentrates on risk-neutral methods of pricing options such as Black- Scholes model. However, using trading agents with utility function to determine the option’s potential payoff is an emerging field in option pricing theory. In this paper, we use one of such methodologies developed by Othman and Sandholm to design portfolioholding agents that are endowed with popular option portfolios such as bullish spread, bearish spread, butterfly spread, straddle, etc to price options. Agents use their portfolios to evaluate how buying or selling certain option would change their current payoff structure. We also develop a multi-unit direct double auction which preserves the atomicity of orders at the expense of budget balance. Agents are simulated in this mechanism and the emerging prices are compared to risk-neutral prices under different market conditions. Through an appropriate allocation of option portfolios to trading agents, we can simulate market conditions where the population of agents are bearish, bullish, neutral or non-neutral in their beliefs.

Item Type:Book Section
Series Name:Frontiers in Artificial Intelligence and Applications
Issue:285
Group:Faculty of Science & Technology
ID Code:24646
Deposited By: Symplectic RT2
Deposited On:07 Sep 2016 13:29
Last Modified:14 Mar 2022 13:58

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -