Risk Coding Round
The risk-quant Python round parametric VaR, square-root-of-time scaling, Expected Shortfall, diversification, expected loss and backtest exceptions, run and graded in the browser.
The scenario
The risk technical round. An editor drops you into small, exact numerical functions the building blocks of a risk engine. They're watching for correct formulas and clean numerical code, not cleverness.
Where this shows up
Small, exact risk functions (VaR, scaling, Expected Shortfall, expected loss and exception counting) are the staple objective coding content for risk-quant and model-validation roles.
Firms such as Barclays, Nomura, JPMorgan.
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The drill
Complete each function and Run it against the fixed inputs (Python via Pyodide, in your browser). Submit to grade your outputs against the expected values; floats are compared to a tolerance. Pin the conventions stated in each docstring.
Sample question
Implement parametric_var(value, sigma_pct, z): the parametric (variance-covariance) VaR as a POSITIVE loss, = value * sigma_pct * z.
Included with the Risk pack
The full graded drill is part of the Risk (Market, Credit & Model Risk) pack a one-time purchase unlocks every concept, coding guide, interview playbook and take-home in this track.
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