Value at Risk: Three Ways to Measure Worst-Case Losses — Portfolio Risk Management

§ 01

VaR Method	Assumption	Strength	Weakness
Parametric	Returns are normally distributed	Fast, simple calculation	Underestimates tail risk (fat tails)
Historical	Future resembles the past	No distribution assumption	Misses unprecedented events
Monte Carlo	Simulated return paths	Most flexible, handles complex portfolios	Computationally intensive, model-dependent

§ 02

Parametric VaR = Portfolio Value × z-score × σ × √t

§ 03

VaR tells you the threshold of a bad day, not how bad it gets. A 95% 1-day VaR of $50K means you expect to lose more than $50K only 5% of the time — but on that 5%, the loss could be $100K, $500K, or worse.

§ 04

Calculate the 95% 1-day VaR for your portfolio using the historical volatility of your holdings. Does the number match your risk tolerance?

§ 05

Your 95% daily VaR is $25K. Over 250 trading days, how many days would you expect losses to exceed $25K?

§ 06

VaR is a useful risk summary but a dangerous false comfort. It works well for normal markets but fails precisely when you need it most — during crises when correlations spike and returns are far from normal.

§ 07

A portfolio has 1-day 95% VaR of $100K. What does this number ACTUALLY mean?

§ 08

Going Deeper — this module contains a deep-dive on adverse selection and the Akerlof lemons dynamic. It is promoted to its own module: see 'Adverse Selection and Market Unraveling' (risk-1b) in this path.

Five questions · AI feedback

Sit with the ideas.

You manage a bond-heavy portfolio. Interest rates have been stable for two years, but you worry about a sudden rate shock. Which VaR method is LEAST appropriate for capturing this risk?

Why:

Sit with the ideas.

Other lessons in this course.