Project 9: Value at Risk (VaR) Calculator

Build a tool that estimates portfolio Value at Risk using historical and parametric methods.

Project Overview

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate Weekend
Main Language Python
Alternative Languages R, Julia
Knowledge Area Risk metrics
Tools CSV data
Main Book “Risk Management and Financial Institutions” by John Hull

What you’ll build: A VaR calculator that reports losses at a chosen confidence level.

Why it teaches quant: Risk is as important as return. VaR makes downside explicit.

Core challenges you’ll face:

  • Computing historical returns
  • Implementing parametric VaR
  • Explaining confidence levels

Real World Outcome

You will input a portfolio and get VaR estimates for different confidence levels.

Example Output:

$ python var.py --portfolio portfolio.csv --alpha 0.95
Historical VaR: -2.3%
Parametric VaR: -2.1%

Verification steps:

  • Compare historical vs parametric results
  • Validate percentile calculations

The Core Question You’re Answering

“How bad can losses get, and with what confidence?”

VaR is the standard language of portfolio risk.

Concepts You Must Understand First

Stop and research these before coding:

  1. Returns distribution
    • How do you compute portfolio returns?
    • Book Reference: “Risk Management and Financial Institutions” by John Hull, Ch. 9
  2. Confidence levels
    • What does 95% VaR actually mean?
    • Book Reference: “Risk Management and Financial Institutions” by John Hull, Ch. 9
  3. Normal approximation
    • When is parametric VaR appropriate?
    • Book Reference: “Risk Management and Financial Institutions” by John Hull, Ch. 10

Questions to Guide Your Design

  1. Portfolio inputs
    • Will you support weighted portfolios or single assets?
    • How will you handle missing price data?
  2. Method selection
    • Will you implement historical, parametric, or both?
    • How will you compare results?

Thinking Exercise

Percentile Intuition

If you have 1000 daily returns, what return corresponds to the 95% VaR?

Questions while working:

  • Why is VaR a tail statistic?
  • What does it ignore about extreme losses?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “What is VaR and how is it interpreted?”
  2. “What is the difference between historical and parametric VaR?”
  3. “Why can VaR underestimate risk?”
  4. “What confidence level is commonly used?”
  5. “What is expected shortfall?”

Hints in Layers

Hint 1: Starting Point Start with historical returns and percentile extraction.

Hint 2: Next Level Add parametric VaR using mean and std deviation.

Hint 3: Technical Details Use negative sign conventions consistently.

Hint 4: Tools/Debugging Plot the return distribution and mark the VaR cutoff.

Books That Will Help

Topic Book Chapter
VaR basics “Risk Management and Financial Institutions” by John Hull Ch. 9
Parametric VaR “Risk Management and Financial Institutions” by John Hull Ch. 10
Return distributions “Risk Management and Financial Institutions” by John Hull Ch. 9

Implementation Hints

  • Use consistent time windows for return calculations.
  • Normalize portfolio weights to sum to one.
  • Provide multiple confidence levels for comparison.

Learning Milestones

  1. First milestone: You can compute historical VaR.
  2. Second milestone: You can compute parametric VaR.
  3. Final milestone: You can explain VaR limitations clearly.