MODEL UNCERTAINTY AND ITS IMPACT ON THE PRICING OF DERIVATIVE INSTRUMENTS

By Rama Cont. 2006

401 citations recorded by the source corpus.

Uncertainty on the choice of an option pricing model can lead to “model risk” in the valuation of portfolios of options. After discussing some properties which a quantitative measure of model uncertainty should verify in order to be useful and relevant in the context of risk management of derivative instruments, we introduce a quantitative framework for measuring model uncertainty in the context of derivative pricing. Two methods are proposed: the first method is based on a coherent risk measure compatible with market prices of derivatives, while the second method is based on a convex risk measure. Our measures of model risk lead to a premium for model uncertainty which is comparable to other risk measures and compatible with observations of market prices of a set of benchmark derivatives. Finally, we discuss some implications for the management of “model risk.”

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/be0ee356d53fecd6200b71c49d1fe5e18f172e39

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Citations in the corpus

Risk Measures and Robust Optimization Problems (2006, background): “Cont, R. Model uncertainty and its impact on the pricing of derivative instruments.”
The Devil is in the Tails: Actuarial Mathematics and the Subprime Mortgage Crisis (2010, background): “Cont (2006) proposes a framework to quantitatively measure model uncertainty which, while written in the context of derivative pricing, is of wider interest.”
The Worst Case for Real Options (2010, methodology): “The framework of our paper follows the ambiguity aversion definition by Epstein and coauthors [5, 17], which is recently used by [22] in the context of pricing options.”
ARBITRAGE BOUNDS FOR PRICES OF WEIGHTED VARIANCE SWAPS (2010, background): “ the hedging instruments. This fundamental problem has motivated a large body of research ranging from asset pricing through portfolio optimisation, risk management to macroeconomics (see for example Cont (2006), F¨ollmer et al. (2009), Acciaio et al. (2011), Hansen and Sargent (2010) and the refere”
Dynamic hedging of synthetic CDO tranches with spread risk and default contagion (2010, background): “A systematic study (both simulation-based and empirical) of the model risk associated with the hedging of credit derivatives, perhaps building on the insights from Cont (2006), is therefore a logical next step.”
Risk aggregation with dependence uncertainty (2014, methodology): “When F is a continuous distribution, H(cn) = D(cn). On the other hand, cn is exactly the smallest possible a such that F on I = [ F−1((n− 1)a), F−1(1− a) ] satisfies the mean condition necessary for the CM property. See, for example, (7) in Proposition 2.1 of Wang and Wang (2011) for more details on”
Duality theory for optimal investments under model uncertainty (2005, background): “We also refer to Cont [ 4 ] for a further discussion of (1) in the theory of model uncertainty.”
A New Approach to Assessing Model Risk in High Dimensions (2015, neutral): “For example, Cont (2006) found bounds on prices of contingent claims, incorporating model risk on the choice of the risk neutral measure used for pricing.”
Model risk and capital reserves (2010, background): “by Cont (2006) , who studies the impact of model risk on pricing.”
Robust Strategies for Optimal Order Execution in the Almgren–Chriss Framework (2012, background): “The ubiquitous existence of model uncertainty was first emphasized by Knight (1921), but only few systematic approaches to this phenomenon exist to date; we refer to Cont (2006) and, for an overview over some material, to Föllmer & Schied (2011) and the references therein.”
UTILITY MAXIMIZATION UNDER MODEL UNCERTAINTY IN DISCRETE TIME (2013, background): “Turning to different but related nondominated problems in the mathematical finance literature, [3] studies robust maximization of asymptotic growth under covariance uncertainty, [15] analyzes optimal arbitrage under model uncertainty and [7] and [34] consider risk management under model uncertainty.”
Path dependent volatility (2008, background): “Then the essential and hard problem is to determine the " correct " hedging strategy: indeed it is well-known that the hedge parameters are strongly model-dependent even for call and put options (cf. Davis 2004; Cont 2006).”