Risk Measures in Simulation-Based Business Valuation: Classification of Risk Measures in Risk Axiom Systems and Application in Valuation Practice

Ernst, Dietmar

doi:10.3390/risks11010013

Cited by 1 publication

(2 citation statements)

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“…The basic idea for the latter is to model the tail region with the GPD (see Appendix B) and then derive statements about the risk. Key risk indicators in corporate planning are usually known as value-at-risk, conditional valueat-risk, risk coverage potential, risk bearing capacity, and risk tolerance (Gleißner and Ernst 2019;Ernst 2022bErnst , 2023. Many of the risk indicators mentioned can be derived from quantiles, so we will focus on the calculation of quantiles and briefly distinguish three methods for determining quantiles and assessing tail risks.…”

Section: Assessment Of Tail Risksmentioning

confidence: 99%

“…A company basically has several different options for conducting the risk assessment process. The risk of a company can either be derived from an outside-oriented risk analysis on the capital market or determined internally with respect to the specific risks carried by the company (Gleißner and Ernst 2019;Ernst 2022bErnst , 2023. The latter approach in particular is attracting increasing interest, especially in Europe, and the approaches are being increasingly refined and implemented in tools for corporate planning (Ernst and Gleißner 2022b).…”

Section: Introductionmentioning

confidence: 99%

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Tail Risks in Corporate Finance: Simulation-Based Analyses of Extreme Values

Börner,

Ernst,

Hoffmann

2023

JRFM

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Recently, simulation-based methods for assessing company-specific risks have become increasingly popular in corporate finance. This is because modern capital market theory, with its assumptions of perfect and complete capital markets, cannot satisfactorily explain the risk situation in companies and its effects on entrepreneurial success. Through simulation, the individual risks of a company can be aggregated, and the risk effect on a target variable can be shown. The aim of this article is to investigate which statistical methods can best assess tail risks in the overall distribution of the target variables. By doing so, the article investigates whether extreme value theory is suitable to model tail risks in a business plan independent of company-specific data. For this purpose, the simulated cash flows of a medium-sized company are analyzed. Different statistical ratios, statistical tests, calibrations, and extreme value theory are applied. The findings indicate that the overall distribution of the simulated cash flows can be multimodal. In the example studied, the potential loss side of the cash flow exhibits a superimposed, well-delimitable second distribution. This tail distribution is extensively analyzed through calibration and the application of extreme value theory. Using the example studied, it is shown that similar tail risk distributions can be modeled both by calibrating the simulation data in the tail and by using extreme value theory to describe it. This creates the possibility of working with tail risks even if only a few planning data are available. Thus, this approach contributes to systematically combining risk management and corporate finance and significantly improving corporate risk management. Based on these findings, further analyses can be performed in terms of risk coverage potential and rating to improve the risk situation in a company.

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Section: Assessment Of Tail Risksmentioning

confidence: 99%