As a risk platform offering fat-tailed, skewed VaR and Expected Tail Loss (ETL) risk measures, this new release extends the CognityFoF risk budgeting capabilities, allowing fund managers to maximize their expected returns per unit of allocated downside risk using new marginal contribution o ETL, percent contribution to ETL and ETL- based implied return measures. Proactively managing their tail risk in a flexible, interactive and dynamic environment, users can now optimize their returns from a true downside risk perspective, said FinAnalytica.
According to the company, CognityFoF now includes pre defined US and global factor models, integrated with over 600 branded global risk factor indices from sources including Standard & Poor’s, Russell, MSCI, Merrill Lynch, Citigroup, CBOE and Hedge Fund Research, thus dramatically expanding the factor modeling capabilities. Fund of funds managers can immediately measure their factor exposure and then, using their own qualitative research, interactively adjust the models to include any proprietary or exotic risk factors.
The company claims that using a new principal component analysis (PCA) module, custom risk factors can be generated from the extracted components of larger groups of indices and added to the risk decomposition analysis. New rolling time window analysis enables users to detect style drift by tracking changes in factor exposures and tail behavior over time.
FinAnalytica has said that Enhanced stress test capabilities allow users to identify time bomb effects, assess the impact of complex, multi- dimensional market crises, and directly stress their modeling assumptions such as factor betas and correlations. Users can choose from a library of predefined historical crisis scenarios or create and store an unlimited number of their own multifactor stress tests. Any number of stress scenarios can be aggregated into a multi-level stress test that is integrated into the Cognity risk reporting framework.
Doug Martin, CEO of FinAnalytica, said: The core of CognityFoF is in exposing and modeling fat-tailed and skewed returns, estimating tail dependencies, and reporting the most accurate value at risk and expected tail loss measures in the market place. ETL-based risk budgeting is a natural extension of this core, in providing the most insightful and accurate view of implied and marginal returns.