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
CRISIL solution*
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
Client impact
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
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