Risk Regimes

Lately, economists talk of three phases of the economy, boom, bust and “normal”. These could all be seen as risk regimes. And that these regimes exist for many different risks.

There is actually a fourth regime and for many financial markets we are in that regime now. I would call that regime “Uncertain”. In July, Bernanke said that the outlook for the economy was “unusually uncertain”.

So these regimes would be:

  • Boom – high drift, low vol
  • Bust – negative drift, low vol
  • Normal – moderate drift, moderate vol
  • Uncertain – unknown drift and unknown vol (both with a high degree of variability)

So managing risk effectively requires that you must know the current risk regime.

There is no generic ERM that works in all risk regimes.  And there is no risk model that is valid in all risk regimes.

Risk Management is done NOW to impact on your current risk positions and to manage your next period potential losses.

So think about four risk models, not about how to calibrate one model to incorporate experience from all four regimes.  The one model will ALWAYS be fairly wrong, at least with four different models, you have a chance to be approximately right some of the time.

Explore posts in the same categories: Complexity, Cultural Theory of Risk, Modeling


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2 Comments on “Risk Regimes”

  1. riskviews Says:

    Longer discussion of these ideas at http://www.soa.org/library/newsletters/the-actuary-magazine/2009/december/act-2009-vol6-iss6-ingram.pdf

    More in the upcoming December/January edition of the Actuary.

  2. Excellent piece.

    What if you assume in your modeling that a discrete process switches between risk regimes? Each regime could be represented by its own parameter set. For example, I have used regime-switching lognormal models, where the process describing which regime a price process is in at any time is assumed to be Markov.

    OK, I haven’t tried four regimes, just two: a low and a high volatility regime, but the generalization to four regimes is theoretically possible. Also, the underlying distribution need not be lognormal. Calibration may be difficult, but it can be tried out.

    Thank you for the article.

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