The document discusses using Value at Risk (VaR) as a risk measure to demonstrate Treating Customers Fairly (TCF) outcomes to regulators. It explains how VaR fits into a fund's distribution pattern and can flag unexpected risks like volatility spikes earlier than traditional measures. Key Risk Indicators are presented that can help analyze whether funds are performing as expected based on investor guidelines.

1. Treating Customers Fairly Technical Guide to Value at Risk, Key Risk Indicators and Logic Tests by Jon Beckett, BA, ASI, For the CCC Introduction Index

8. Index Distribution Debate: So who cares what pattern a Fund’s returns make? Distribution XYZ Fund holds a mixed portfolio of risk assets with an expected moderate level of Standard Deviation and Return. Distribution Risk is the risk of returns away from the Mean (or expected) return. The patterns of returns could look like any of those below; the majority of patterns are grouped into a few broad categories.. Downside (related) Believe it or not – all 3 patterns could display the same Standard Deviation and the same Mean Return.. The same Fund could exhibit all of these patterns, if the manager changes the Fund Risk by changing strategy, or the Fund’s sensitivity to Market Risk by changing the asset allocation. However the Risk presented to the investor, by each of these distributions, is very different. There are 2 key questions: the frequency (likelihood of loss) and the severity of maximum loss. A. This is the Normal distribution of returns; (otherwise known as the Gaussian or Bell Curve). The frequency of returns are well spaced with the greatest around the Mean. When a Fund returns slightly outside the normal distribution it is usually referred to as a Bi-nominal or non-normal distribution. This Fund’s expected VaR and maximum loss will be moderate/variable and frequency regular. Balanced Managed and Asset Allocation Funds often strive for this.. B. This Fund follows a Uniform Distribution (Sometimes known as a Leptokurtic pattern). The range of return is narrower around the Mean and the frequency of returns higher. That means fewer different returns and more of the same sort of return, both negative and positive. This Fund’s expected VaR and maximum loss will occur frequently but will be less severe. Bond Funds often display this type of pattern.. C. The last is the Negative, Platykurtic pattern; (commonly referred to as the ‘Left-tailed’ Distribution). Often there is a high concentration of returns around the Mean; much like the Uniform distribution, but usually all positive and in the higher return ranges. To the left there is then a long series of acute negative returns - this is the left-tail. The Fund’s expected VaR and maximum loss occurs infrequently but will be more severe on those occasions; (boom-bust cycles). Higher risk funds (E.g. Emerging Markets) often have left-tailed distributions and Funds with low correlated returns to core markets such as REITs and High Yield Bonds.. Frequency over time t Severity Severity Severity

9. Index Downside Patterns: So what frequency and severity would we expect? Downside Bell Curve (related) A. Normal Distribution B. Uniform Distribution C. Left-tailed Distribution Mean Return over time Mean Return over time Mean Return over time Normal – E.g. Core Equity Volatility t B. Uniform – E.g. Bond A. C. Left-tail – E.g. Emerging Market or Specialist

10. Index Hypothetical Example: How Risk changed through the ‘Credit Crunch’ Credit Crunch: Volatility Spikes lead to increased Maximum Losses, returns fall outside of the Expected Range Expected Upper Range of Return Standard Deviation Expected Lower Range of Return VaR Hypo XYZ Fund holds a conventional portfolio of equity and bonds with expected moderate Standard Deviation and Return The Manager’s Mean Return and Standard Deviation are based on the previous 36 month period and won’t immediately reflect the rising VaR in the portfolio. VaR values and rolling short-term KRIs, through the investment period, flag the abnormal risk sooner! Mean Return Value at Risk: indicates the likely maximum size of volatility spikes if they occurred – VaR can quickly flag changes in short-term volatility Fund Returns Volatility (STDEV) Value at Risk (VaR) Portfolio Managers began to invest aggressively into derivatives, high yield and securitised debt securities – this changed the future distribution/risk of the portfolio but will not impact the Standard Deviation of the Fund in the short-term..

11. Bell Curve Index TCF VaR Process KRIs 4. Process: From Distribution to Outcome Step1. PAIR Stress-testing –Indices/Sectors are tested for historical indicators once per annum Step2. Fund Patterns – PAIR test ongoing data and supply fund level reports on a regular basis Left-tail Distribution Funds Positive Distribution Funds Uniform Distribution Funds Normal (Bell Curve) Distribution Funds Marginal Risk Variable Risk! Moderate Risk Higher Risk! Step3. Analysis of Outcomes – Designated Manager, or analyst, analyses Key Risk Indicators (‘KRIs’) against expected outcomes from Step1. Set of logical tests are applied for each distribution type Step4. Flag Outcomes and Actions – Score funds as either ‘Green’, ‘Amber’ or ‘Red’ based on Step3 and prepare the health monitor report for peer review and escalates alerts or action through Global Product Strategy. Framework (related)

13. Value at Risk Factor = Normal Distribution Confidence Level; E.g. 95% (1.96) Standard deviation of historical fund returns. = The number of months (36) or days (20) measured Value at Risk Amount = VaR Factor * NAV NAV = Net Asset Value of the Fund Value at Risk (‘VaR’) is a downside estimate of how much a Fund could lose within a given investment horizon and at a given confidence level. 95% Confidence means that the Value at Risk will not exceed a maximum loss for 95% of the time but for 5% it could. VaR does not indicate the likelihood nor probability of a maximum loss occurring..

14. Confidence % ( Intervals) = About 68% of values in a normal distribution are within one standard deviation (sigma, σ) away from the Mean (μ); about 95% of the values are within two standard deviations and about 99.7% lie within three standard deviations. Each standard deviation multiple (sigma , σ) then provides confidence intervals of expected ranges of return. To be more precise, the area under the bell curve between Mean and +/− each sigma multiple (σ) in terms of the cumulative normal distribution function is given by the error function (erf). To 12 decimal places, the 1 to 6 sigma multiples are: You can then reverse the relation of sigma multiples for a few associated indicators used to describe the area under the bell curve. These values are useful to determine (asymptotic) confidence intervals for other indicators based on a normally distributed curve. The sigma multiples and confidence intervals are shown right. The left table then indicates the proportion (Confidence%) of a given interval and n is a multiple of the standard deviation that specifies the width of each interval. 4.4172 0.99999 3.8906 0.9999 3.29052 0.999 3.09023 0.998 2.80703 0.995 2.57583 0.99 2.32635 0.98 1.95996 0.95 1.64485 0.90 1.28155 0.80 Interval Width (n) 68% 99.999% 99.99% 99,9% 99.8% 99.5% 99% 98% 95% 90% 80% Confidence% 0.999999998027 6 0.999999426697 5 0.999936657516 4 0.997300203937 3 0.954499736104 2 0.682689492137 1 σ Interval