Cognity risk management software meets the needs of investment decision makers to demonstrate a structured investment process through a series of integrated modules including factor modeling, fat-tailed portfolio risk reporting & decomposition, tail-risk budgeting, risk backtesting, stress testing, what-if analysis and quantitative asset screening.
Understanding investor and regulator reporting requirements, Cognity risk analytics include all traditional risk statistics and models. Side-by-side comparisons between real world fat-tailed and traditional models provide valuable insight and ensure confidence.
All Cognity risk analytics modules are fully integrated allowing for settings and results to be carried through the entire analytic process. Each module provides flexible settings for timeframe, data scenarios, and benchmark settings. User defined asset groups and custom risk decomposition templates provide powerful and easy drill-down reporting.
Cognity key modules include:
Market Risk Reporting & Risk Decomposition
Cognity’s powerful multi-asset class aggregation capabilities offer unparalleled reporting and results transparency. User-defined views of aggregated portfolio risk with drill down into multi-level groupings across asset classes and positions are easily created. With its patented fat-tailed framework, Cognity risk management software is the only platform that estimates fat-tailed VaR, Expected Tail Loss (ETL), Expected Tail Return (ETR) and Contribution to Tail Risk across multiple distribution types and alongside classical volatility measures.
Cognity’s powerful reporting framework can decompose risk into custom systematic and specific components and identify which assets or managers concentrate or diversify factor bets. User-defined views of aggregated portfolio risk with drilldown to multi-level exposures across asset classes and positions are easily created.
Cross Asset Class Modeling and Factor Analysis
Providing the most accurate estimates of factor and specific risk, Cognity risk analytics' rapid factor modelling capabilities combine single and multi-factor regression analytics with both linear and non-linear options. Proprietary factors and third party models are easily added to Cognity’s pre-packaged database of leading market factors and indices.
Rolling Analysis & Risk Backtesting
Cognity risk management software detects and monitors unusual changes in factor exposure and distributions through time. Providing early warning of potential shifts in market structures and identifying dangerous holdings changes, Cognity’s robust backtesting capabilities test model reliability and track portfolio VaR and ETL evolution on portfolio, sub-portfolio, position or risk factor levels.
Cognity’s fat-tailed risk analytics framework more accurately assesses the impact of crisis scenarios on VaR, ETL and portfolio value than standard risk management software systems. Pre-defined historical crisis scenarios provide a starting point for construction of complex user-defined stress scenarios. Stress tests on asset and factor correlations, factor exposures, simulation scenarios and distribution parameters may be run individually or in any combination.
Separating tail risk from tail return, Cognity what-if trade analysis provides the most accurate view on the impact of changes in portfolio positions. In the worst of times, what-if analysis based on normal distributions can lead you completely down the wrong path. Cognity’s fat-tailed what-if analysis expands decision support insight, proactively mitigates risk and increases performance.
Quantitative Asset Screening
Cognity risk analytics improve asset and manager selection by exposing risk asymmetry and by ranking with downside adjusted performance measures. Cognity’s rich visual screening environment clearly differentiates between upside potential and downside risk.
Version 4.0 offers enhanced ‘Tempered' Stable Distributions modelling and increased performance through new scenario caching. Users have access to expanded upside tail returns analytics and enhanced tail risk hedging models
Managing and monitoring tail risk is not just about insuring against extreme losses. Boryana Racheva-Iotova describes the potential for expected tail loss measures to feed into tactical portfolio optimisation where variance is traditionally deployed.
FinAnalytica President Dr. Boryana Racheva-Iotova discusses how tail risk management can be a key source of added value in long horizon portfolio allocation and optimization.