Complex valuation challenges in focus – Insights: “Quantitative Advisory”

published on 24 June 2025 | reading time approx. 2 minutes

In an increasingly complex and uncertain world, traditional valuation methods regularly reach their limits. In particular, specific procedures are required when dealing with increased planning uncertainty, asymmetric payout profiles (e.g. employee options or valuation of companies close to insolvency), contractual clauses (e.g. earn-outs) and complex financial instruments or derivatives. Mathematical-stochastic methods - such as Monte Carlo simulations or option pricing models - often need to be applied in order to determine reliable values and thus make economically sound decisions.

Our “Insights”-section "Quantitative Advisory" provides a comprehensive insight into current developments and practical issues against this background. It covers the following topics in six separate articles:

DLOM: The marketability discount in (company) valuation

In the Anglo-American valuation literature, it is widespread for investors to demand a price discount for non-fungible shares due to their higher risk. This so-called discount for lack of marketability (DLOM) can, for example, be highly relevant for valuations in accordance with IFRS 2, IFRS 13 or Section 409A of the United States Internal Revenue Code. This article examines the motivation and use cases of DLOM and discusses the strengths and weaknesses of benchmark studies and option pricing model-based methods.

Consideration of early exercise in the valuation of employee stock options

Generally, IFRS 2 stipulates that all employee stock options must be recognized in the statement of profit & loss. This requires an option valuation, whereby the expected term is a decisive input parameter. In principle, call options should not be exercised prematurely unless the company pays a dividend. However, empirical studies show that employees tend to exercise granted options early. This article provides an overview of how this behavior can be taken into account when valuing employee options.

The influence of ESG on the valuation of derivatives

Environmental, social and governance (ESG) criteria are becoming increasingly important in the opportunity and risk analysis of companies and markets. They therefore also influence the pricing of derivatives. ESG derivatives based on sustainability criteria, in particular ESG performance, require adapted valuation procedures. This article provides an overview.

Repricing of employee stock options

Share-based payments are widely used by listed companies and start-ups and are suitable for retaining employees in the company. Through employee stock option plans, companies grant their employees the option to buy equity instruments at a certain exercise price and thus participate in the company's growth in value. However, if the value falls over time, the original exercise price may become unattractive for employees. The employer then has the option of counteracting this by reducing the exercise price - known as repricing. This article examines the consequences of accounting for such a change.

The application of the option pricing model for the valuation of complex capital structures

The option pricing model (OPM) is a method for allocating the equity value to different classes of securities in a company's capital structure. The OPM is particularly important in the valuation of start-up companies, as different rights, such as liquidation preferences, are often granted to investors in financing rounds. This article provides an overview of the procedure based on a practical example.

Monte Carlo simulation for the earn-out valuation: distribution assumptions and thresholds

Earn-outs are variable purchase price components that cushion uncertainties about future performance in corporate transactions. The development of relevant key figures (e.g. sales or EBITDA) is modeled using certain distribution assumptions. Threshold values, such as caps and cliffs or floors, influence the payout profile and limit risks or opportunities. Monte Carlo simulations help to map these uncertainties and enable a well-founded evaluation of earn-outs. This article provides an overview.