Using novel, proprietary data on operational losses from a large government-owned bank in India, we provide evidence that only 3% operational risk events (particularly external frauds) account for more than 80% of aggregate operational losses. Operational losses are power-law distributed and exhibit steep increases in their tail operational value-at-risk. We also show that extreme operational losses in a given year are driven mostly by the previous year’s
NPL (non-performing loan) level, with a one standard deviation rise in NPLs associated with about a 1% rise in extreme operational losses.
Using novel, proprietary data on operational losses from a large government-owned bank in India, we provide evidence that only 3% operational risk events (particularly external frauds) account for more than 80% of aggregate operational losses. Operational losses are power-law distributed and exhibit steep increases in their tail operational value-at-risk. We also show that extreme operational losses in a given year are driven mostly by the previous year’s
NPL (non-performing loan) level, with a one standard deviation rise in NPLs associated with about a 1% rise in extreme operational losses.
