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Home > Financial Risk Management Best Practice > Stress-Testing in Asset and Liability Management: A Coherent Approach

Financial Risk Management Best Practice

Stress-Testing in Asset and Liability Management: A Coherent Approach

by Alex Canavezes and Mario Schlener

This Chapter Covers

  • How traditional stress tests are performed and why they are meaningless.

  • How to assign a probability number to a given stress event.

  • Exposition of the frequentist methodology.

  • Exposition of the subjective methodology.

  • Application of the frequentist methodology to a case study in asset management.


In light of recent extreme events, such as the collapse of Lehman Brothers in 2008, both the financial services industry and its regulators have keenly felt the need to complement traditional percentile-based risk management tools (such as value-at-risk (VaR) or economic capital) with stress tests and scenario analyses.

Following the logic of Dermine (2003), asset and liability management (ALM) can be interpreted as the main management tool for controlling value creation and risks in a financial institution. Additionally, ALM should be the main management tool for discussions, in an integrated way, of fund transfer pricing, deposit pricing (for fixed and undefined maturities), loan pricing, the evaluation of credit risk provisions, the measurement of interest rate risk for fixed and undefined maturities, the diversification of risk, the marginal risk contribution, and also the allocation of economic capital.

Learning from the past misbehaviors of all market participants (especially the overreliance on quantitative measures with “statistical entropy,” on diversification, and the assumption that capital is always available), risk management evolved from being just used as a risk-minimization, insurance, or diversification tool to an optimization tool for managing the risk–return profile. This implies that financial institutions have to develop forward-looking models (i.e. to cover tail/extreme events) and decisionmaking tools that cover the amount of available capital, leverage adjustment costs, and the duration mismatches of assets and liabilities.

The fundamental basis for every ALM model is to define future scenarios of risk parameters and value assets and liabilities. One of the main challenges in that process is to come up with scenarios. These scenarios are usually based on historical observations or forward-looking simulations (using Monte Carlo) and typically do not cover tail risks—the so-called extreme events.

It is clear that stress tests are much needed in order to complement the usual VaR measures as a foundation for risk-adjusted decision-making. However, the traditional stress-testing approaches used by market participants and/or requested by the regulators suffer from a fundamental problem: there is no attempt to assign probabilities to the scenarios considered.

A framework is needed to express the likelihood of the various stress scenarios. A specific probability can be given to a stress test in, usually, two ways:

  • on a nonobjective or judgmental basis—for example, by an economist/expert who provides context-sensitive and conditional stress scenarios;

  • on an objective basis using historical data—i.e. one requiring a long period of history in order to observe stressed situations.1

Stress-testing as a risk management tool has been in existence for more than a decade but was not really applied by the financial services industry as an enhancement of the daily decision-making process. The reasons for this reluctance are well explained by Aragones, Blanco, and Dowd (2001) (quoted by Rebonato, 2010):

“…the results of [traditional] stress tests are difficult to interpret because they give us no idea of the probabilities of the events concerned, and in the absence of such information we often don’t know what to do with them. …As Berkowitz [1999] nicely puts it, this absence of probabilities puts ‘stress testing in a statistical purgatory. We have some loss numbers, but who is to say whether we should be concerned about them?’ …[we are left with] two sets of separate risk estimates—probabilistic estimates (e.g. such as VaR), and the loss estimates produced by stress tests—and no way of combining them. How can we combine a probabilistic risk estimate with an estimate that such-and-such a loss will occur if such-and-such happens? The answer, of course, is that we can’t. We therefore have to work with these estimates more or less independently of each other, and the best we can do is use one set of estimates to check for prospective losses that the other might have underrated or missed…”

The main goal in this chapter is to explore ways in which a probability number can be assigned to stress tests in order to make sense of them and be able to integrate them within ALM in a meaningful manner.

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Further reading


  • Balkema, Guus, and Paul Embrechts. High Risk Scenarios and Extremes: A Geometric Approach. Zürich: European Mathematical Society, 2007.
  • Bouchaud, Jean-Philippe and Marc Potters. Theory of Financial Risk and Derivative Pricing. Cambridge: Cambridge University Press, 2009.
  • Rebonato, Riccardo. Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress. Chichester, UK: Wiley, 2010.


  • Aragones, Jose Ramon, Carlos Blanco, and Kevin Dowd. “Incorporating stress tests into market risk modelling.” Derivatives Quarterly 7:3 (Spring 2001): 44–49.
  • Berkowitz, Jeremy. “A coherent framework for stress-testing.” Journal of Risk 2:2 (1999): 5–15.
  • Dermine, Jean. “ALM in banking.” Working paper. INSEAD, July 17, 2003. Online at: [PDF].
  • Greenspan, Alan. Presentation to Joint Central Bank Research Conference, Washington, DC, 1995.

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