Lattice structures for the stochastic comparison of call ratio backspread derivatives with an application
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Palabra(s) clave:
Call ratio backspread derivative, integrated survival function, lattice, stochastic order
Fecha de publicación:
Editorial:
Statistical Institute of Catalonia
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Citación:
Descripción física:
Resumen:
The comparison of investments in fnancial derivatives is an appealing topic in the optimization of resources. A relevant derivative is the call ratio backspread. Motivated by the need to compare investments in such derivatives, a new family of stochastic orders is introduced. That permits to reach decisions on the allocations of funds in those derivatives under general conditions and without assuming specifc probability distributions of the asset prices. Characterizations of the orders are developed. Special emphasis is placed on the existence of infma and suprema in such dominance criteria, which leads to lattice structures on some special spaces and to the reduction of some optimization problems with stochastic dominance constraints. The method is illustrated with an application using real data from fnancial markets.
The comparison of investments in fnancial derivatives is an appealing topic in the optimization of resources. A relevant derivative is the call ratio backspread. Motivated by the need to compare investments in such derivatives, a new family of stochastic orders is introduced. That permits to reach decisions on the allocations of funds in those derivatives under general conditions and without assuming specifc probability distributions of the asset prices. Characterizations of the orders are developed. Special emphasis is placed on the existence of infma and suprema in such dominance criteria, which leads to lattice structures on some special spaces and to the reduction of some optimization problems with stochastic dominance constraints. The method is illustrated with an application using real data from fnancial markets.
Patrocinado por:
This work was supported by the Spanish Ministry of Science and Innovation under Grant MCI-20-PID2019-104486GB-I00 and Grant MCIU-22-PID2021-123461NB-C22, and Principado de Asturias Government under Grant AYUD/ 2021/50897.
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