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Pension Fund Asset Risk Management

Pension Fund Asset Risk Management. Monitoring market risk. Tony de Graaf Principal Risk Manager. Disclaimer.

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Pension Fund Asset Risk Management

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  1. Pension Fund Asset Risk Management Monitoring market risk • Tony de Graaf • Principal Risk Manager

  2. Disclaimer All material contained herein is indicative and for discussion purposes only, is strictly confidential, may not be reproduced and is intended for your internal use only. This document has been solely prepared for discussion purposes and is not an offer, or a solicitation of an offer, to buy or sell any security or financial instrument, or any investment advice. This policy does not confer any rights to any third parties. PGGM Investments has taken all reasonable care to ensure that the information contained in this document is correct, but does not accept liability for any misprints. The information contained herein can be changed without notice.

  3. Agenda Trends in pension fund assetrisk management Pension fund balance sheet risk management Asset risk measurementandattribution Stress testing AIFMD risk management measures

  4. Trends in pension fund asset risk management • Pension fund boards want tobe ‘in control’ • Transparancy • Increasinginterest in goodexecution, robust operations andcountervailing power, less in ‘alpha’ skills • Understand whatyouinvest in • Highercompexity must pay-off • Delegationmaynot lead toless control • Detailed monitoring of investment process • Detailed investment restrictions • Between pension fund andasset manager • Betweenasset manager andexternalmanagers • Awareness of liquidity risk and counterparty risk

  5. Balance sheet risk management

  6. Investment process Pension liabilities 100% nominal discounted SBM 15% equities • 5% Private Equity • 5% Listed Real Estate • 5% Private Real Estate 5% Commodities • 45% Government Bonds • 10% Credits 5% High Yield 5% Local Ccy Bonds 70% Currency hedge Implementation 3.000 stocks 500 bonds 20 commodity futures Asset swaps Interest Rate Swaps Cross currency swaps Etc. ALM 30% equities 5% commodities 65% fixed income 50% interest rate hedge

  7. Balance sheet risk monitoring

  8. Coverage Ratio at Risk (CRaR)

  9. Monitoring liquidityandcontrollability

  10. Asset risk measurementandattribution

  11. Popularasset risk measures • Tracking Error: • Value at Risk: • Relative Value at Risk : • ExpectedShortfall:

  12. Considerations • Forward lookingperiod (day, month, year) • Backward lookingperiod(months, year, multiple years) • Ex-ante or ex-post • Staticvsdynamic portfolio (reinvestments?) • Historical returns frequency (1D, 3D, 5D, 21D) • Weightingschemeforhistorical returns (equal, decay factor, long memory) • Overlapping vs. non-overlapping returns • Returns distribution • Dependencestructure (standard multivariate distribution, copula) • Parametric vs. Monte Carlo

  13. Risk attribution • Static vs. dynamic • Allocation versus selectioneffect(similarto performance attribution) • Breakdown accordingto the fund management process • Countries • Sectors • Instrument types • Risk type • Interest rate, spread, FX, … • Maturitysegments • Equity factors

  14. PGGM example

  15. Classical risk attribution Euler: ifthen Therefore: With the portfolio weights vector We defineMarginalVaR: In a normalparametricframework, we have: We cannow present a break down of VaR (or TE, or ES) thatsumsto portfolio VaR

  16. Incorporatingallocationandselection effect in TE Example: benchmark canbedivided in sectors, fund manager over/underweights sectors and over/underweights on security level Portfolio weightto security: Portfolio weightto security: Portfolio weightto sector: Benchmark weightto sector: Benchmark return sector : Relative return:

  17. Incorporatingallocationandselection effect in TE (2) See RiskMetrics working paper ‘Risk attributionforasset managers’ byJorge Mina (2002) The sameresultscanbeobtainedforVaRusingmarginalVaRs: With: and

  18. Dynamic risk attribution As per the start (above) and end (below) of the analysis period

  19. Dynamic risk attribution (2) • Asset 3 has a largerimpact on ΔMVaRthenasset 4, although the parameters forasset 3 didn’t change • Attributioncannotbebroken down into single parameters

  20. New methodfordynamic risk attribution Some definitions: - - etc. On top of this, we define: whentwo or more indices are equal, e.g. andwhen the indices aren’t in increasing order, e.g. Then we define the contributions: etc.

  21. New methodfordynamic risk attribution (2) We then have: Because And Now we assignallhigher-order contributionsto the lower-order contributionsbased on the absolute values of the lower order contibutions. So, forthe second order contributions we have: Andfor the third order contributions: etc.

  22. New methodfordynamic risk attribution (3)

  23. New methodfordynamic risk attribution(4) Comparewithattributionbased on MVaR! Drawback: computationally intensive See article in “De Actuaris” by Tony de Graaf (2012)

  24. Returns based risk measurement • Ex-post TE or VaRattribution • Returns basedstyle analysis

  25. Stress testing

  26. Stress testingforasset managers • Applicable at instrument level • Methodology must besensitivetoall instrument characteristics • Onlykey risk drivers needtobespecified • Secondary risk drivers must follow in a consistent manner • Resultsshouldreflectcurrent market sensitivitiesanddependencies

  27. The predictive stress test See article ‘Stress Testing in a Value at Risk Framework’ byPaul Kupiec (1998) If and , then: with: This gives: In a normal framework, this amounts to multivariate linear regression.

  28. The predictive stress test • Each instrument is valued as a function of its risk factors: • Determinesensitivites of the risk drivers to the specified scenario factors: • The sensitivitiesdepend on market volatilitiesandcorrelations, simplelinearregressiongives the approximation: • Varyingthe estimationperiod, onecan get anythingfrom a structuralrelationto a short-term trend

  29. Predictive stress test example • Scenario: Credit Crisis 2008 H2 • Specified in scenario S&P 500 and USD • In this example, S&P 500 loses 29% and USD gains 13% (against EUR) • Betas estimated over an 8-year period, using weekly returns

  30. Predictive stress test example (2) Volatilities Correlations

  31. Predictive stress test example (3) Comparewith: Predictedresults Scenario 2008 H2 realisation

  32. AIFMD • Mandatoryfornon-UCITS investment funds • Gross & commitment leverage • Fund liquidity • Regularmeasurement • Stress test

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