Name: Quantify and Optimize the Impact of Risks on Corporate Sustainability: A Stochastic Model to Quantify and Optimize the Impact of Operational Risks on Corporate Sustainability (en Inglés)
Price: 158014.00 COP
Availability: InStock
Author: Mariana Bravo Sepúlveda; Daniel N Wilke; Jorge Andres Polanco
ISBN: 9786207648603

portada Quantify and Optimize the Impact of Risks on Corporate Sustainability: A Stochastic Model to Quantify and Optimize the Impact of Operational Risks on Corporate Sustainability (en Inglés)

Quantify and Optimize the Impact of Risks on Corporate Sustainability: A Stochastic Model to Quantify and Optimize the Impact of Operational Risks on Corporate Sustainability (en Inglés)

Formato

Libro Físico

Editorial

Lap Lambert Academic Publishing

Autor

Mariana Bravo Sepúlveda; Daniel N Wilke; Jorge Andres Polanco

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9786207648603

Quantify and Optimize the Impact of Risks on Corporate Sustainability: A Stochastic Model to Quantify and Optimize the Impact of Operational Risks on Corporate Sustainability (en Inglés)

Mariana Bravo Sepúlveda; Daniel N Wilke; Jorge Andres Polanco (Autor) · Lap Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Quantify and Optimize the Impact of Risks on Corporate Sustainability: A Stochastic Model to Quantify and Optimize the Impact of Operational Risks on Corporate Sustainability (en Inglés)"

Operational risk has been widely studied and there are international guidelines that provide procedures for the correct management of operational risk; however, this has not been studied from a corporate sustainability point of view. Therefore, this work seeks to find a way to model and optimize the impact of operational risks on corporate sustainability. The methodology used is based on the assignment of two distribution functions for the creation of a probabilistic model that allows quantifying the probability of occurrence (frequency) and the expected monetary impact (severity) on the sustainability variables (environmental, social, and economic). The result is a statistical convolution through Monte Carlo simulation which makes it possible to quantify aggregate losses to finally make an optimization process of the variables and estimate the financial impact. Therefore, this study extends the literature on risk quantification, proposing a stochastic model that quantifies and optimizes the operational risks that are related to corporate sustainability. The proposed model offers a practical way to quantify operational risks related to corporate sustainability.