Real assets: getting the score right

Investors and regulators are continuously placing higher demands on risk management, particularly with regard to the transparency and quality of risk ratios. The extensive legal regulations, however, do not alter the fact that the success of an investment depends on the ability to correctly estimate, manage and appropriately deal with the risks before and during the term of the investment.

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Investors and regulators are continuously placing higher demands on risk management, particularly with regard to the transparency and quality of risk ratios. The extensive legal regulations, however, do not alter the fact that the success of an investment depends on the ability to correctly estimate, manage and appropriately deal with the risks before and during the term of the investment.

By Rainer Jung

Investors and regulators are continuously placing higher demands on risk management, particularly with regard to the transparency and quality of risk ratios. The extensive legal regulations, however, do not alter the fact that the success of an investment depends on the ability to correctly estimate, manage and appropriately deal with the risks before and during the term of the investment.

Risk valuation of real assets requires special approach

Managing capital investments on the basis of a normal distribution assumption is simply not enough. A major difference exists between the risk assessment of real assets and classic financial market investments. The quantitative approach frequently used for the risk assessment of the latter is unable to effectively model dynamic changes over a period of time. Quantitative models have the great advantage of being able to compile huge quantities of information, but this is offset by their inability to make allowance for individual risks. More is needed for a conclusive risk valuation of real assets than can be offered by purely quantitative data which tend to be restricted to easily-measurable risks.

Regulatory interdependencies

Due to the possibility of regulatory interaction between, for example, Solvency II and Basel III, which classic models fail to take into account, it is absolutely imperative that a complementary risk assessment is carried out on the basis of integrated qualitative and quantitative criteria.

Capturing risks in advance

Identifying the key risk drivers for the qualitative valuation processes presupposes a fundamental understanding and in-depth specialist knowledge of the assets in question. In an ideal scenario, the risks are identified before the investment is made, i.e. during the selection process. In the case of energy generation systems, consideration should be given in particular to categories such as the construction phase – construction risks, operation phase – operational risks, market volume and volume risks, contract structure and financing structure. Each is then broken down into individual valuation parameters so that adequate analysis and valuation can be undertaken.

For example, valuation consists of two stages. Stage 1 focuses on qualitative factors which are entered into a scoring card. In stage 2, the quantitative valuation is carried out using the ‘Monte Carlo simulation’ that leads to a statistical set of probabilities of outcomes. After combining the results of the scoring card with the scoring model, a final project valuation is made in a third step.

The information required for the classification and evaluation is mostly taken from the due diligence reports and existing project documentation and is supplemented by empirical values of internal asset specialists.

At the second stage, a quantitative analysis is made to determine the probability of default of the investment in the event of changes in variable external influences (e.g. construction costs, operating costs, etc.), using a Monte Carlo simulation. Individualised standard deviations of the parameters to be simulated are entered for this purpose.

The key performance indicator for the analysis is the probability of default of the interest rate and redemption payments of the project in question. This is defined as the budgeted internal rate of return (IRR). In every simulation run, the actual IRR is compared with this pre-defined rate of return over the length of the project. Each time the actual IRR is smaller than the return on the budgeted IRR, a default scenario is deemed to exist.

The total of the default scenarios is added together and placed in relation with the total number of scenarios as a percentage. The percentage of the simulations with a default event represents the concrete probability of default in the scoring model.

 

Rainer Jung is head of methodology and group risk management at Alceda Asset Management

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