Enhancing Portfolio Risk Decomposition and Surveillance for a Leading US Multi-Strategy Hedge Fund to Minimize Hedging Costs While Limiting Exposures

The client faced challenges in assessing and monitoring the risk of complex portfolios. A US-based multi-strategy hedge fund was keen to develop a robust and customizable risk management system easily accessible to portfolio managers and risk teams.

A.      Solution construct

• During consultative discussions, it was recommended to build a multifactor model to estimate cross-sectional risk and develop the requisite mechanisms and a custom index
• Additionally, a suggestion was made to develop a single source of truth dashboard to monitor key metrics for portfolio and risk managers across the firm, enabling easy access to MSCI Barra models
• To deploy a real-time risk monitoring framework using BarraOne resources

B.      Execution

• Worked with trading desks across asset classes to identify and define the relevant factors such as value, volatility, momentum, growth, etc.
• Combined multifactor models to analyze the overall risk scenario, calculating factor risk contribution using variance-covariance and asset weight matrices
• Assisted in hedging risk factors at different levels and analyzed the performance of baseline vis-à-vis custom index on out-of-sample data
• Collaborated with the MSCI team to understand their programmable API to fetch data (rather than the GUI platform). Developed a Python code to get risk and performance attribution data from Barra API on a daily, weekly and monthly basis, as needed
• Developed a Power BI dashboard to monitor metrics such as return, volatility, tracking error, Z-score, exposure by factor (country, currency, industry and style), contribution to total risk and active risk
   

• Increased granularity of risk factors and surveillance helped reduce the portfolio hedging cost
• A broader benchmarking approach was adopted through the creation of a customized index comparable with the market benchmarks
• Client risk managers could access Barra risk and performance attribution data through simple SQL queries, rather than manually using Barra GUI or working through API
• Enhanced efficiency within the risk monitoring process and developed SOPs for dashboard maintenance - worked with a daily combined file size of over 1 GB and more than 1,800 risk and performance metrics at an asset-level detail

*Crisil team is proficient in using Barra, Wolfe, Black-Litterman, BHB and Brinson models