It's no secret that traditional risk-management models failed to help investors anticipate the meltdowns of Lehman Brothers Holdings or Bernard Madoff’s ponzi investment scheme. What’s less certain is what will replace those models.

Vying for that honor are a number of new software systems that use stress testing based on various hard-to-predict scenarios and throw in some additional bells and whistles. Once the exclusive realm of the largest and most sophisticated fund-of-funds managers, like Blackstone Alternative Asset Management and UBS, the technology driving risk analytics has become commoditized, with off-the-shelf software packages widely available to investors of all sizes. Companies such as HedgeMark Risk Analytics, Imagine Software, Inalytics, Investor Analytics and PerTrac now all sell versions of such risk mitigation software, giving any investor the chance to assess exactly how its assets would react to extraordinary events.

In contrast, the formerly reigning traditional model, known as Value at Risk or VaR for short, relies on historical price data to construct a distribution of probable outcomes for a portfolio.

“You’re using historical data to estimate what correlations will be between prices and volatility,” says Lance Smith, CEO of New York City–based Imagine Software. “That results in a probable distribution of probable outcomes.”

But an inherent problem with VaR is how to estimate correlations between the historical prices of financial instruments and actual volatility in the market. In the world of statistics, this is known as the “fat tail” problem, which refers to the fact that the tail ends of the curve of distribution of probable outcomes in finance are wider than in normal distributions.