Chen, B. and Kankanhalli, M. (2019) Pricing average price advertising options when underlying spot market prices are discontinuous. IEEE Transactions on Knowledge and Data Engineering, 31(9), pp. 1765-1778. (doi: 10.1109/TKDE.2018.2867027)
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Abstract
Advertising options have been recently studied as a special type guaranteed contract in online advertising. An advertising option is a contract which gives its buyer a right to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Many studies on advertising options have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Second, jump-diffusion stochastic models are used to describe the underlying spot market price movement which incorporate several important statistical properties. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.
| Item Type: | Articles |
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| Additional Information: | This work is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centre in Singapore Funding Initiative. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Chen, Dr Bowei |
| Authors: | Chen, B., and Kankanhalli, M. |
| College/School: | College of Social Sciences > Adam Smith Business School > Management |
| Journal Name: | IEEE Transactions on Knowledge and Data Engineering |
| Publisher: | IEEE |
| ISSN: | 1041-4347 |
| ISSN (Online): | 1558-2191 |
| Published Online: | 27 August 2018 |
| Copyright Holders: | Copyright © 2018 IEEE |
| First Published: | First published in IEEE Transactions on Knowledge and Data Engineering 31:1765-1778 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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