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Case Study #228 — 300 Software Failure Case Studies

XIV/inverse-VIX ETN 'Volmageddon' collapse

Several exchange-traded notes designed to profit from low market volatility (notably Credit Suisse's XIV) lost nearly all their value overnight, wiping out roughly $3 billion for investors in a single trading session.

2018-02-05
When it happened
Finance
Sector
#228 of 300
Ranked by documented impact
01 — What Happened

The damage was public. The root cause was preventable.

Several exchange-traded notes designed to profit from low market volatility (notably Credit Suisse's XIV) lost nearly all their value overnight, wiping out roughly $3 billion for investors in a single trading session.

Finance
Sector affected
2018-02-05
Date of the event
#228
Rank in the 300 Software Failure Case Studies
2
Distinct root-cause clauses identified below
02 — The Root Cause

What the software actually got wrong

The notes' rebalancing algorithms were all programmed to buy large volumes of VIX futures at day's end whenever volatility spiked, and because so many similar products used near-identical formulas, their simultaneous automated buying drove volatility futures prices into a self-reinforcing spiral that these products could not survive.

01Root Cause

The notes' rebalancing algorithms were all programmed to buy...

What Happened

The notes' rebalancing algorithms were all programmed to buy large volumes of VIX futures at day's end whenever volatility spiked

How Requs AI Catches This

Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.

02Root Cause

Because so many similar products used near-identical formulas, their...

What Happened

because so many similar products used near-identical formulas, their simultaneous automated buying drove volatility futures prices into a self-reinforcing spiral that these products could not survive

How Requs AI Catches This

Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.

03 — How This Gets Caught Before It Happens

Beyond code coverage and "shall" testing

Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.

Requs AI Edge Case

Surfaces this before code exists

Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.

Requs AI Software FMEA

Connects the failure mode to the hazard

Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.

Find the overlooked root causes before they ship.

Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.