Your volatility model "won" on QLIKE. Did your weights change? New research from @GARCHInstitute shows a level error can move loss by 1.0 and move risk-parity weights by 2e-16. Here is a three-line scorecard for model reviews. https://t.co/rRWYrWgbMJ
AI model validation also needs a market-level view. Our new research video explores how synchronized trading can strain liquidity—even with similarly accurate forecasts. Evidence from a controlled simulation.
Watch the full 13-minute video:
https://t.co/EJZWC8Zmwa
Two volatility-forecasting questions with direct implications for model governance:
• When does a forecast difference actually matter for allocation?
• When can model disagreement provide an early warning of fragility?
Watch:
Different Volatility Forecasts, Same Portfolio? Decision Equivalence Explained
https://t.co/hZ8Fj9AyBa
Can Model Disagreement Warn of Volatility Forecast Failure?
https://t.co/EenZDVt07N
Read:
Decision-Equivalent Volatility Forecasts
https://t.co/SC3t3QyVgY
Forecast Disagreement and Conditional Model Fragility
https://t.co/BbMGHTUgCy
New on SSRN:
Volatility model rankings change with the loss function used for evaluation.
The evidence suggests that a superior model set is often more defensible than a unique winner in finite samples.
https://t.co/4T8owqm2NY