A bank does not accidentally bear risk — it deliberately purchases it. When a dealer writes a credit default swap, sells a structured note, or extends a corporate loan, it is exchanging a fee today for an uncertain future cash flow. The entire machinery of quantitative risk management — VaR, Expected Shortfall, copulas, GARCH — exists to answer one operational question: how bad can the loss distribution get, and how much capital must we hold against it?
This note establishes the vocabulary and framing that the rest of the unit builds on. It defines the main risk types in plain terms, shows how randomness enters through the probability-space formalism, and introduces the loss distribution as the central object of measurement. It then distinguishes risk measurement (statistical estimation of the distribution) from risk management (acting on it through capital allocation, hedging, and securitisation).
Learning objectives. By the end of this note you should be able to:
- define market, credit, operational, liquidity, and model risk with concrete examples from real market events;
- write down the P&L as a random variable and explain why its distribution function is the central object of QRM;
- distinguish the measurement of risk from the management of risk;
- state what quantitative risk management adds over qualitative risk management, and name its principal tool classes.
In this lesson
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- What is risk?
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- The financial risk taxonomy
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- Risk measurement
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- Risk management
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- What quantitative risk management adds
The full lesson, with worked examples and exercise solutions, is for members.
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