Search Results - isPartOf:"Computational Management Science"

Exploring non-analytical affine jump-diffusion models for path-dependent interest rate derivatives

Silva, Allan Jonathan da; Baczynski, Jack - In: Computational management science 21 (2024) 1, pp. 1-32

Persistent link: https://www.econbiz.de/10014636797

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

Zephyr, Luckny; Lamond, Bernard F.; Lang, Pascal - In: Computational management science 21 (2024) 1, pp. 1-44

Persistent link: https://www.econbiz.de/10014636803

Distributions and bootstrap for data-based stochastic programming

Chen, Xiaotie; Woodruff, David L. - In: Computational management science 21 (2024) 1, pp. 1-21

Persistent link: https://www.econbiz.de/10014636807

Distributional robustness, stochastic divergences, and the quadrangle of risk

Rockafellar, Ralph Tyrrell - In: Computational management science 21 (2024) 1, pp. 1-30

Persistent link: https://www.econbiz.de/10014636812

A simplified Wiener-Hopf factorization method for pricing double barrier options under Lévy processes

Kudryavtsev, Oleg - In: Computational management science 21 (2024) 1, pp. 1-30

Persistent link: https://www.econbiz.de/10014636822

Procurement auctions with losses

Heymann, Benjamin; Jofré, Alejandro - In: Computational management science 21 (2024) 2, pp. 1-20

Persistent link: https://www.econbiz.de/10014636862

A bilevel optimization approach of energy transition in freight transport : SOS1 method and application to the Ecuadorian case

Villamar, Daniel; Aussel, Didier - In: Computational management science 21 (2024) 2, pp. 1-30

Persistent link: https://www.econbiz.de/10014636869

Optimizing hedonic editing for multiple outcomes : an algorithm

Egozcue, Martín; Fuentes García, Luis - In: Computational management science 21 (2024) 2, pp. 1-25

Persistent link: https://www.econbiz.de/10014636872

Some robust inverse median problems on trees with interval costs

Le Xuan Dai; Nguyen Kien Trung; Le Phuong Thao; Pham Thi Vui - In: Computational management science 21 (2024) 2, pp. 1-25

Persistent link: https://www.econbiz.de/10015195790

The nested Sinkhorn divergence to learn the nested distance

Pichler, Alois; Weinhardt, Michael - In: Computational Management Science 19 (2021) 2, pp. 269-293

The nested distance builds on the Wasserstein distance to quantify the difference of stochastic processes, including also the evolution of information modelled by filtrations. The Sinkhorn divergence is a relaxation of the Wasserstein distance, which can be computed considerably faster. For this...

Persistent link: https://www.econbiz.de/10014497512