Impact of the SNB decision on SST reporting companies

in Financial Services, 06.03.2015

On 15 January 2015, the Swiss National Bank (SNB) ended its three-year-long cap of 1.20 CHF against to the euro, triggering a record rise in the value of the Swiss franc against the euro. At the same time, the SNB lowered the interest rates on certain deposit account balances by 0.5 percentage points to -0.75%.

This decision triggered a significant decline in Swiss equity markets, a considerable drop in all CHF cross rates and Swiss Government bonds yielded negative for maturities up to 10 years, reaching a low of -30bps, the lowest ever recorded for sovereign redemption yield.

We published our initial thoughts on the impact of this decision and the associated market responses on insurers in an article published in our latest Insurance magazine Clarity on Life Insurance matters. This short blog provides an update on the impact on insurers.

FINMA Response

On 23 February, FINMA responded to the current economic environment and required all SST (Swiss Solvency Test) reporting companies to add a chapter to their SST Report on the effect of the SNB decision to remove the euro cap and the lowering of the base rate.  In this new chapter, the following sections are required:

  • Impact on Assets and Best Estimate Liabiltiy and thus the Risk-Bearing Capital as at 1 February 2015
  • Impact on Target Capital as at 1 February 2015

Thus all insurers reporting SST need to recalculate their full SST as at 1 February 2015.

Operational issues

This is likely to cause a number of operational issues for two main reasons.

  • Firstly, the SST report is still required to be submitted by 30 April 2015, so essentially insurers have little over 2 months for the 1 February valuation (since they were only informed of the requirement on 23 February), compared to the usual 4 months for the 1 January valuation.
  • Secondly, insurers have internal processes in place to complete an SST valuation annually or semi-annually. Some larger insurers calculate their SST ratio quarterly for internal purposes but few are likely to be in a position to calculate it on a monthly basis at such short notice.  In fact, many insuers do not even perform an accounting close on a monthly basis on the local OR basis, let alone the market value basis.

However, some companies will most likely adopt some simplifiying assumptions in order to carry out this recalculation. Any such simplifications are to be detailed in the chapter for FINMA’s review.

Long term impact on risk management processes

Many insuers are already considering calculating the SST ratio more frequently in order to inform management decisions and as part of a drive towards better overall risk management.  This ad-hoc request by FINMA will only put added focus on more flexible and reactive risk management systems.

Often insurers make use of “lite modelling” techniques such as replicating portfolios or Least Squares Monte Carlo (LSMC) as part of risk management initatives such as Daily Solvency Monitoring (DSM) to enable them to quantify the impact of such market events on a far more frequent basis than would be possible using full stochastic liability models.

Risk modelling

The standard model allows, at least in theory, for market events such as those observed following the SNB decision.  In particular, this is due to the use of extreme scenario testing, where multiple market risk factors occur simultaneously and the Delta-Gamma model which implicitly allows for negative interest rates, as it is based on the multivariate normal distribution.

However, those insurers using stochastic valuation to estimate the market value of their liabilities make use of an Economic Scenario Generate (ESG) with an underlying interest rate model.  This model may well not allow for negative interest rates.  This would result in an inappropriate Best Estimate Liability.  In addition, market risk is often calculated by first fitting a Replicating Portfolio (RP) of assets to best match the liabilities and then stressing the difference between the Market Value of Assets and the RP.  As the RP would have been calibrated based on non-negative scenarios, it is unclear how appropriate the resulting market risk would be, even when using a risk model allowing for negative interest rates.  Additionally, some internal models used in the market also do not allow for negative interest rates, i.e. those based on geometric Brownian motion, further exacerbating the problem.

 

 

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