IOPS Toolkit for Risk-based Supervision

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IOPS Toolkit for Risk-based Supervision

• Module 2 Quantitative Assessment of Risk

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Overview Quantitative Assessments

• Play an important part RBS poor QA results
  imply higher levels of residual risk to be
  factored into the overall risk analysis or risk
  score
• Quantitative tools can provide a bridge between
  rules-based and risk-based approach to
  supervision
• Quantitative tests can be undertaken by the
  supervisory authority itself or by pension funds
• QA should focus on the risks relevant for the
  type of fund (i.e. funding and solvency issues
  for DB funds, investment returns and volatility
  and reliability of retirement income for DC
  funds)
• Not all risks can be measured in a quantitative
  fashion the overall risk assessment will always
  be a combination of quantitative and qualitative
  factors

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Models

• Quantitative assessment tools make use of models
  3 main limitations
• what is modelled and how including vital
  omitting factors, use of inappropriate data, only
  using recent data in development of models and
  their parameters, failing to take account of
  extreme events, using the same accepted view
• understanding the power and limitations of
  models including their use outside their sphere
  of applicability, hidden assumptions and
  overestimating the power of models which are
  simplified representations
• operational risks around the use of models poor
  documentation, lack of testing, and the misuse of
  data

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Modelling - Recommendations

• It is recommended that models should
• sufficiently represent those aspects of the real
  world that are relevant to the decision makers
  for which the information will be used
• include explanations of how the inputs are
  derived and what the outputs are intended to
  represent
• be fit for purpose in both theory and practice
• include explanations of their significant
  limitations

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Quantitative Regulations

• Quantitative regulations are the starting point
  for quantitative risk analysis
• These can be straight forward limits (such as
  minimum funding rules for DB funds or investment
  restrictions for DC funds)
• Alternatively, these quantitative regulations can
  be risk-based themselves (e.g. factor based
  solvency rules, VaR calcuations)
• Under a risk-based approach, supervisors need to
  consider not only whether quantitative regulatory
  requirements are being met, but whether risks are
  being identified and managed in such a way that
  the requirements will continue to be met in the
  future

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Quantitative Regulations

• Risk-based supervision can incorporate
  quantitative regulations in 3 ways
• Combine rules-based and a risk-based approach
  compliance with quantitative restrictions is
  checked, and if not met a lower score would be
  factored into the overall risk assessment of the
  fund
• Quantitative requirements could be made more
  risk-based but testing whether compliance would
  still hold in adverse circumstances (i.e. by
  stress testing) -the results of these
  stress-tests would then be incorporated in the
  overall risk score
• Where the quantitative regulations are already
  risk-based ,compliance with these risk-based
  regulations would be fed into the overall risk
  score

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Quantitative Regulations DB Funds

• Valuation requirements these can be tested and
  incorporated into an overall risk analysis by
  looking at the valuation assumptions, undertaking
  sensitivity testing of changes in valuation
  assumptions, and stress testing of risks such as
  high inflation
• Minimum funding requirements these can be
  incorporated into an overall risk assessment
  either by checking for compliance (and scoring
  the fund accordingly) or by stress testing the
  funding position to see if the minimum
  requirements would be met under adverse
  circumstances (with the results of such tests fed
  into the risk score)
• Factor-based solvency margins solvency margins
  can be risk-based by requiring higher amounts of
  capital to be held against risky assets (such as
  equities), thereby providing a buffer in case
  such assets decline in value. Either straight
  forward or stress-tested compliance with these
  margins can be incorporated into an overall risk
  score
• Stress-related solvency margins require each
  entity to calculate the additional amount of
  assets it would need to be able to meet its
  obligations under a prescribed stress scenario or
  scenarios. The results are then fed into the
  overall risk assessment

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Quantitative Regulations DC Funds

• Investment limits compliance with these limits
  forms part of the overall risk score
• Minimum return requirements solvency
  requirements backing guarantees would be measured
  and assessed in the same way as DB fund promises
• Value at risk limits these assess the volatility
  of investment returns . Results of such stress
  testing would be incorporated in the overall risk
  score. Where such limits are themselves
  regulatory requirements, compliance would be part
  of the overall risk assessment
• Alternative risk measures attempt to measure
  risk against long-term income requirements (such
  as replacement ratios), with regulators devising
  optimal portfolios for achieving this target. The
  performance of the actual portfolio of a pension
  fund could then be assessed vs. this benchmark
  portfolio. Supervisors could then work this
  analysis into their overall risk assessment via a
  traffic light system.

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Techniques for Quantitative Risk Assessment

• Comparison of valuation assumptions compare
  assumptions with peers, previous assumptions of
  entity, and consideration of current environment
• Analysis of surplus compare actual experience to
  assumptions to assess appropriateness / accuracy
  of assumptions
• Roll-forward calculations financial position
  projected under certain scenarios to assess
  exposure to adverse circumstances
• Duration analysis project cash flows of assets
  and liabilities of fund to determining timing
  mismatches, as well as interest rate sensitivity
• Sensitivity testing test sensitivity of
  valuation results to differences in assumptions
  by recalculating results using alternative
  assumptions
• Deterministic stress testing calculate the
  financial position of a pension entity at current
  or future date to one or more adverse scenarios
• Stochastic stress testing calculate the
  financial position of a pension entity at current
  or future date using computer generated adverse
  scenarios
• Value at risk (VaR) calculations type of
  stochastic stress test measuring adverse market
  movement with a specified probability

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Techniques for Quantitative Risk Assessment
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Integrating Quantitative Tools

• Defined Benefit
• Solvency and funding ratios are the key
  quantitative tests for DB funds above 100
  resulting in low risk score, below implying a
  higher level of risk
• Roll forward and stress tests would be useful
  indicators if funding remains over 100 after
  stress testing in negative scenarios, a low risk
  score would result
• ALM seeks minimise and manage the asset related
  risks as a function of liabilities, thus ALM
  testing could indicate a sign of robust risk
  management at a pension fund

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Integrating Quantitative Tools

• Defined Contribution
• VaR measuring investment volatility can be used
  for DC funds, though is considered controversial
• ALM type measurements to see if DC funds can meet
  income replacement targets are still under
  development
• For DC funds offering guarantees, similar
  standards and solvency stress tests of DB funds
  can be used

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Quantitative Measurement of Non-financial Risk

• Non-financial risk encompasses operational risks
  the degree of complexity of the fund and the
  capacity to handle the complexity. It also
  includes governance, management, internal
  controls and independent review.
• Difficult to quantify, although very useful as
  leading indicators.
• DB funds are inherently complex due to benefit
  design, such as early retirement benefits,
  indexation etc., thus attracting a higher risk
  score.
• DC funds offering a large range of investment
  options or life-cycle investment pooled
  investment returns declaring rates on a
  non-transparent smoothing approach rather than
  market basis and high levels of outsourcing,
  would attract a higher risk score.

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Thank You

• Presentations of practical examples to follow