Valuation risk

Valuation risk is the financial risk that an asset is overvalued and is worth less than expected when it matures or is sold. Factors contributing to valuation risk can include incomplete data, market instability, financial modeling uncertainties and poor data analysis by the people responsible for determining the value of the asset. This risk can be a concern for investors, lenders, financial regulators and other people involved in the financial markets. Overvalued assets can create losses for their owners and lead to reputational risks; potentially impacting credit ratings, funding costs and the management structures of financial institutions.[1]

Valuation risks concern each stage of the transaction processing and investment management chain. From front office, to back office, distribution, asset management, private wealth and advisory services. This is particularly true for assets that have low liquidity and are not easily tradable in public exchanges. Moreover, issues associated with valuation risks go beyond the firm itself. With straight through processing and algorithmic trading, data and valuations must remain synchronized among the participants of the trade processing chain. The executing venue, prime brokers, custodian banks, fund administrators, transfer agents and audit share files electronically and try to automate such processes, raising potential risks related to data management and valuations.

To mitigate this risk it is important to provide transparency and ensure the integrity and consistency of the data, models and processes used to process and report calculations within valuations for all participants.

Background

The growth and diversity made in financial engineering has led to highly creative and innovative strategies where new products and new structures are offered at very fast pace on the market. As most innovations are first proposed on over-the-counter (OTC) markets, they tend to rely on financial models, sometimes combining several models together. Financial models typically build on underlying assumptions and require calibration to a breadth of scenarios, business conditions and variations of the assumptions increasing the model risk.

The shock wave which affected the credit and capital markets following the burst of the US sub-prime mortgage crisis in late 2007, tested most underlying assumptions and had sweeping effects on a number of models that would unlikely be calibrated for extreme market conditions, or tail risk. This led to an emergency call for transparency and assessments of exposure from the financial institutions’ clients, shareholders and managers, echoed by the regulators. In this process, it appears that market exposure and credit exposure intricately mix into a single notion of valuation risk.

Managing valuation risk

Valuation risks result from data management issues such as: accuracy, integrity and consistency of static data. Accuracy and timeliness of information such as corporate events, credit events, or news potentially impact them. Streaming data, such as prices, rates, volatilities are even more vulnerable as they also depend on IT infrastructure and tools therefore adding a notion of technical and connectivity risk.

Some financial institutions have set up centralised data management platforms, open to multiple sources of static and streaming data where all financial instruments traded or held can possibly be defined, documented, priced, historised and distributed across the enterprise. Such centralisation facilitates data cleansing, historising and auditing, allow organisations to define and control pricing and valuation procedures as required for compliance. For OTC instruments, the platforms also involve the definition and storage of underlying information such as yield curves and credit curves, volatility surfaces, ratings and correlation matrices and probabilities of default.

In addition, an important aspect of managing valuation risk is associated with model risk. In search of transparency, market participants tend to adopt multiple model approaches and rely on consensus rather than science. In the absence of deep and liquid market transactions, and given the highly non-linear nature of some of the structured products, the mark-to-model process itself requires transparency. To achieve this, open pricing platforms may be linked to the centralised data warehouse. Those platforms are capable of using multiple models, scenarios, data sets with various distribution and dispersion models to price and re-price under ever-changing assumptions.

The final aspect of managing valuation risks relates to the actions that can be taken within the firm as a result of the assessments of exposures and sensitivities reported. The management of tail risks should also be reviewed so that allocating economic capital weighted by a very low probability of occurrence of an event amounted to considering a normal distribution of events or simply overlooking the tail risk.

References

Greg N. Gregoriou (2009). "23.2". Operational Risk Toward Basel III: Best Practices and Issues in modeling, management and regulation. p. 486. ISBN 9780470451892.

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[1] Greg N. Gregoriou (2009). "23.2". Operational Risk Toward Basel III: Best Practices and Issues in modeling, management and regulation. p. 486. ISBN 9780470451892.