Karan Sood

CFA Level 1: Post 3: Learning Concepts with Practical Example Debtor Days While this concept seems easy on books, its interpretation is critically important Suppose Debtor Days is 40 and Average Debtor is 25cr Basic Interpretation: On an average, it takes around 40 days to collect 25cr from our customers The quicker we are able to collect the money from our debtor, the less problem we face with working capital i.e. payments towards salary, rent, electricity, other overheads and short term debt Things to keep in mind: 1) Compare the debtor days with its competitors and its own past record 2) Always look at a trend and not for just an year Ideally, lesser debtor days are preferable compared to its competitors. Suppose our debtor days is 40 and the industry average is around 70, it’s a good thing. But, sometimes, it might be the case that our credit policy is strict and hence our debtor days are less. So, one of the ideal ways to interpret the debtor days is you need to check the trend of debtor days and also see the trend of the sales growth. If our debtor days is less and our sales growth is good, it could mean that our company possesses some type of bargaining power with our distributor or customers. Practical Scenario: Diagnostic Sector Dr Lal Pathlabs: Major Revenue from B2C - Debtor days of around 14-15 days Krsnaa Diagostic: Major Revenue from B2G – Debtor Days of more than 100 days This is a drastic difference. Krsnaa receives its money quite late compared to Dr Lal. Hence, they would need to take short term loans to pay off its salary,rent,etc You would notice that the short term loans for Krsnaa as % of Equity is higher than that of Dr Lal Pathlabs This reflects in the market sentinment I.e. PE Ratio of the company I have shared the link of this concept on the comment section Do share it, if you found it to be helpful

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Liquidity-Adjusted VaR and Expected Shortfall in Bond Portfolios -- When managing a bond portfolio, traditional Value at Risk (VaR) provides an estimate of potential losses under normal market conditions. However, it ignores one critical factor — liquidity. In fixed-income markets, liquidity risk often spikes during stress events, with widening bid-ask spreads and reduced market depth. This can significantly increase the cost of unwinding positions. -- Consider a portfolio holding corporate bonds and government bonds. Under normal market conditions, the liquidity cost of selling Treasuries is negligible, while investment-grade and especially high-yield bonds carry wider spreads. Liquidity-adjusted VaR (LVaR) builds on standard VaR by adding these costs. For instance, a portfolio with a $100 million exposure may show a VaR of $3 million at 99% confidence, but once adjusted for bond spreads, LVaR could rise to $3.5 million — a 17% increase simply due to transaction costs. -- The effect is even more pronounced in stressed markets. During liquidity shocks (such as the 2008 crisis or the March 2020 selloff), credit spreads widen sharply. High-yield bonds that normally trade with a 50 bps bid-ask spread may suddenly see spreads exceed 200 bps. This pushes the liquidity-adjusted VaR much higher, as forced liquidation would mean selling into a thinner market at deeper discounts. -- Expected Shortfall (ES), or Conditional VaR, further strengthens this picture by measuring the average loss beyond VaR. Liquidity-adjusted ES (LES) captures not just the tail losses from market volatility, but also the additional fire-sale costs of liquidating bonds in illiquid conditions. For example, if ES on the same $100 million portfolio is $5 million, liquidity adjustments under stress could increase it to $6 million or more. -- For bond portfolio managers, these metrics matter because they reflect the true cost of risk — not just from market movements, but also from liquidity constraints. Incorporating LVaR and LES into stress testing and risk frameworks ensures that portfolios are not only market-resilient but also liquidity-resilient, which is crucial in fixed income markets where liquidity can vanish exactly when it’s needed most. -- The below analysis is based on hypothetical numbers and is just provided as an example. #RiskManagement #LiquidityRisk #BondMarkets #VaR #ExpectedShortfall #FixedIncome #StressTesting #MarketRisk #LVaR #LES #Volatility #Treasury #CreditSpreads

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While preparing for FRM Level 2, I spent considerable time understanding Potential Future Exposure (PFE) and how its profile differs across asset classes. What initially appeared to be a straightforward concept turned out to have several layers of nuance — from the impact of volatility and tenor to the nature of underlying payoffs. To consolidate my understanding, I put together concise notes that allowed me to visualise and retain the profiles more effectively. I’m sharing them here for professionals and candidates who may find the topic equally challenging. I hope this helps make the concept clearer and easier to recall, whether you’re studying for FRM or applying it in a real-world risk management context. Always eager to discuss how complex risk concepts translate into practical frameworks for decision-making in the financial industry. #FRM #RiskManagement #CounterpartyCreditRisk #PFE #FinancialRisk #QuantFinance

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Understanding Credit Risk Losses: Expected vs. Unexpected vs. Stress Losses ✨ In credit risk management, losses are not all the same. The classic loss distribution curve splits them into three categories — each treated differently under Basel regulations and IFRS 9. 📊 1. Expected Losses (EL) Normal cost of doing business, covered by provisioning & pricing. IRB (Basel II/III): EL is calculated as PD × LGD × EAD. IFRS 9: Requires banks to provision for Expected Credit Loss (ECL) in financial statements (12-month or lifetime, depending on stage). 📊 2. Unexpected Losses (UL) Rare but plausible losses beyond EL. Basel capital rules: Banks must hold regulatory capital against UL. IRB inputs (PD, LGD, EAD, maturity) feed into RWA and capital calculations. 📊 3. Stress Losses Extreme tail events, too costly to cover fully with capital. Addressed through stress testing, Pillar 2, and supervisory buffers (e.g., ICAAP, CCAR). 👉 In summary: EL → IRB & IFRS 9 (provisioning). UL → Basel capital requirements. Stress Loss → Stress testing & Pillar 2 buffers. 💬 What’s your experience with bridging accounting ECL and regulatory capital EL/UL in practice? #CreditRisk #Basel #IFRS9 #RiskManagement #Banking #DataScience

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