With continued focus on downside risk measures and fat-tailed and skewed distribution models of asset returns, this latest release includes extensive enhancements to Cognity factor models and risk estimation methods as well as new relative performance overlays that benefit both funds of hedge funds and managers of managers in benchmarking against indices and peer group portfolios.

Analyzing return streams of funds, portfolios, and portfolios of portfolios, CognityFoF now delivers asymmetric Student-t risk estimation methods along with the core asymmetric stable distribution methods, thereby accurately modeling the actual behavior of hedge fund returns. In particular, these Cognity methods allow users to model the varying degrees of tail-fatness and skewness for individual portfolio assets, leading to asset allocations that capture high-return potential while minimizing downside risk. Furthermore, CognityFoF now offers robust nonlinear time series factor models with lagged variables that yield better fitting factor models, resulting in more informative risk analyses. Risk managers can assess the risk of their funds and portfolios based on an optimal set of risk factors obtained using newly added step-wise regression methods. The resulting factor models not only provide insightful risk decompositions and a powerful portfolio stress testing framework, but they also meet the challenge faced by fund of funds managers of reliably backfilling unequal return series.

The Cognity scenario simulation engine supports the most complete set of returns distribution models available to accurately analyze risk. Distribution models supported include historical, stable, symmetric-t and asymmetric-t distributions, as well as the traditional but typically inadequate normal distribution. The more accurate distributions models lead to better VaR estimates and more informative expected tail loss (ETL) estimates of risk. Further, the more informative ETL estimates lead to optimal portfolios with better downside risk performance than mean-variance optimal portfolios.

CognityFoF version 2.9 gives managers of managers the ability to monitor and measure their manager's performance against respective benchmarks or peer groups. Managers who do not have full transparency available can use this returns-based analysis with new benchmark relative reporting capabilities to easily decompose their risk by strategy, sector, or any user-defined category.

Doug Martin, CEO of FinAnalytica, said, "This latest version of CognityFoF offers risk managers and portfolio managers deep and accurate insights into sources and characteristics of the risk in their portfolios. It also supports downside risk optimization of FoF portfolios, providing managers guidance in the allocation process." Martin added, "We see CognityFoF being increasingly used by more generalized multi-manager investment firms."

This new version also includes a host of usability enhancements including pre-defined stress test templates, easy import of returns and portfolios via Excel files, and a completely new optimization constraint editor.