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Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measures

Saeed Marzban(saeed.marzban***at***hec.ca)
Erick Delage(erick.delage***at***hec.ca)
Jonathan Y. Li(jonathan.li***at***telfer.uottawa.ca)

Abstract: In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the contract to the same level of risk. Focusing for the first time on the context where risk is measured according to convex risk measures, we establish that the problem reduces to solving independently the writer and the buyer's hedging problem with zero initial capital. By further imposing that the risk measures decompose in a way that satisfies a Markovian property, we provide dynamic programming equations that can be used to solve the hedging problems for both the case of European and American options. All of our results are general enough to accommodate situations where the risk is measured according to a worst-case risk measure as is typically done in robust optimization. Our numerical study illustrates the advantages of equal risk pricing over schemes that only account for a single party, pricing based on quadratic hedging (i.e. epsilon-arbitrage pricing), or pricing based on a fixed equivalent martingale measure (i.e. Black-Scholes pricing). In particular, the numerical results confirm that when employing an equal risk price both the writer and the buyer end up being exposed to risks that are more similar and on average smaller than what they would experience with the other approaches.

Keywords: Option pricing, risk hedging, convex risk measures, incomplete market, dynamic programming, numerical optimization

Category 1: Applications -- OR and Management Sciences (Finance and Economics )

Category 2: Robust Optimization

Category 3: Other Topics (Dynamic Programming )

Citation: Technical report, Les Cahiers du GERAD G–2020–02, GERAD, HEC Montréal, Canada.

Download: [PDF]

Entry Submitted: 01/22/2020
Entry Accepted: 01/22/2020
Entry Last Modified: 01/22/2020

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