Ever wondered why two measures—PFE & EEPE—exist in investment banking when they sound so similar? 🤔 One tells you the worst wave you could face 🌊, the other tells you the average swell over time. Both matter, but for very different reasons… 1️⃣ What They Measure PFE – Potential Future Exposure • Definition: The maximum credit exposure a bank might face on a counterparty trade over a specified horizon, at a given confidence level (e.g., 95%, 97.5%, 99%). Purpose: • Used for limit monitoring (counterparty credit limits). • Focuses on extreme but plausible exposure scenarios. Key Characteristic: • Percentile-based — it’s the Xth percentile of the future exposure distribution at each time point. • Ignores average scenarios ⸻———————-————————————— EEPE – Effective Expected Positive Exposure • Definition: The weighted average of Expected Exposure (EE) over the first year of a trade’s life, where each EE is the average positive exposure at a future date. Purpose: • Used for regulatory capital under Basel (especially in Internal Model Method, IMM). • Designed to capture average risk over time rather than just the worst-case percentile. Key Characteristic: • Time-weighted average of means, not percentiles. • Regulatory definition includes discounting short-dated exposures to avoid front-loading capital. ⸻————————————————————— 2️⃣ Why Investment Banks Use Both • PFE is for risk appetite & limit setting — you need to know the “worst case” your counterparty might expose you to so you can set a limit. • EEPE is for regulatory capital — Basel wants an average measure over time to size capital more proportionately to ongoing credit risk, not just the extremes. ⸻————————————————————— 3️⃣ Why the Percentiles Differ This is the key point in your question: • PFE percentile: • Directly picks a high percentile (e.g., 97.5%) from the simulated exposure distribution. • Will always be above the mean unless the distribution is perfectly symmetric and has no volatility. • Sensitive to volatility, optionality, and market shocks. EEPE “percentile” (actually not a percentile): • Based on the mean positive exposure, not a tail statistic. • Even if you simulated exposures at the same time horizon, EEPE is usually lower than the corresponding PFE because it averages out scenarios, not just the tail. • Percentile concept doesn’t directly apply — but if you compared “EEPE vs. the mean of the same timepoint in PFE distribution,” you’d see a gap because of distribution skewness. ⸻————————————————————- Simple Analogy Think of exposure like the height of ocean waves: • PFE = We want to know the height of the biggest waves we might face in the next 5 years at the 97.5% confidence level. • EEPE = We want the average wave height over the year, weighted by time — because that’s what knocks the boat around day-to-day. #PFE #EEPE #CounterpartyCreditRisk #BaselIII #RiskManagement #InvestmentBanking #SACCR #IMM #FinanceInsights#cfbr#creditrisk
Thanks for sharing, Vishal. The ocean wave analogy is spot on.
Summarized very well. Good insights
Helpful insight! 💡
Thanks for sharing! 🙏🏼
Well put, Vishal
💡 Great insight Vishal Maru CFA
💡 Great insight
Well put, Vishal
Definitely worth reading