IMPROVED FRECHET-HOEFFDING BOUNDS ON d-COPULAS AND APPLICATIONS IN MODEL-FREE FINANCE

• 成果类型: Article
• 署名作者: Lux, Thibaut; Papapantoleon, Antonis
• 署名单位: Technical University of Berlin
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/17-AAP1292
• 发表日期: 2017
• 页码: 3633-3671
• 关键词: bivariate distribution-functions; basket options; transportation problems; multiasset options; actuarial science; quasi-copulas; prices; Comonotonicity; strategies; margins

摘要

We derive upper and lower bounds on the expectation of f (S) under dependence uncertainty, that is, when the marginal distributions of the random vector S = (S-1,..., S-d) are known but their dependence structure is partially unknown. We solve the problem by providing improved Frechet-Hoeffding bounds on the copula of S that account for additional information. In particular, we derive bounds when the values of the copula are given on a compact subset of [0, 1](d), the value of a functional of the copula is prescribed or different types of information are available on the lower dimensional marginals of the copula. We then show that, in contrast to the two-dimensional case, the bounds are quasi-copulas but fail to be copulas if d > 2. Thus, in order to translate the improved Frechet-Hoeffding bounds into bounds on the expectation of f (S), we develop an alternative representation of multivariate integrals with respect to copulas that admits also quasi-copulas as integrators. By means of this representation, we provide an integral characterization of orthant orders on the set of quasi-copulas which relates the improved Frechet-Hoeffding bounds to bounds on the expectation of f (S). Finally, we apply these results to compute model-free bounds on the prices of multi-asset options that take partial information on the dependence structure into account, such as correlations or market prices of other traded derivatives. The numerical results show that the additional information leads to a significant improvement of the option price bounds compared to the situation where only the marginal distributions are known.