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Intermediate
Risk Model is a quantitative tool for forecasting risk for each security in a coverage universe. Risk models include forecasts of variance for each security and the covariance between each pair of securities, or factors.
Risk models are used for risk analysis and portfolio optimization. The three versions of third-party risk models and example factors include:
Factor models are generated from a multi-factor regression which results in a covariance matrix of factors (ie, size, style, sector) instead of individual security co-movements.
Synonyms: Arbitrage Pricing Theory model, APT model
The process of building a risk model for sale is an endeavor typically attempted only by large service providers like Barra, Axioma, Wilshire, BlackRock and Bloomberg. These firms have large budgets and staff to access and clean investment data on over 25,000 equities globally. Besides this, the technical know-how requires teams of PhD-level employees to review and hypothesize ways in which past data can be statistically screened, categorized and standardized into a tool that predicts risk better than using past observations alone.
It is for this reason that most individuals and professionals, like brokers, financial planners and wealth managers, ignore risk models altogether. Many institutional investors on the other hand subscribe to third-party models in conjunction with their data providers and other software tools. Few firms develop risk models in house.
Tim: Have you heard what we spend on Ann's
risk model every year?
Kay: Nope. It's a closely guarded secret, and
you know what that means.
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10,000 is the number of cells in a 100-by-100 covariance matrix.
Still unclear on the concept of a Risk Model? Check out the 27-video free tutorial series in Excel called Quant 101 where we walk through risk modeling from the start.
Our trained humans found other terms in the category forecasting models you may find helpful.
To learn more about risk, subscribe to the FactorPad YouTube Channel. Our Twitter handle @factorpad is for new content announcements.
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