In portfolio risk management a lot of focus and attention is (rightly) put on models and methodologies used to compute ex-ante risk measures. And in the context of a multi-asset class universe which is vast by nature, perfect data (market data, terms and conditions provided by the user) and bug-free algorithms are not always possible.
Therefore, one of the key challenges within the portfolio risk management space is to ensure that any risk analytic produced is sound and reliable
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In this whitepaper, we:
- take a deeper dive into two accepted tree-based techniques to detect outliers: isolation forest (IF) and random cut forest (RRCF)
- show how efficient these methods can be to detect PL vectors that may need further investigation
- explain how this process can be automated for risk managers to flag impacted portfolios
Author
Hassan Ennadifi
Executive Director, Product Specialist
Download Our Whitepaper
