Webinar

Designing Liquid Credit Portfolios: Bquant Enterprise’s Tradable Trackers Workflow, Data and Methodology Unveiled

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Join us for an exclusive Webinar to learn about creation of index-linked liquid credit portfolios. Bloomberg's Tradable Trackers are constructed using a subset of the constituents of the parent index and support innovation of the index-linked credit products market. The methodology outlined in this upcoming event is aimed at providing Fixed Income investors with the tools to hedge bond exposures, identify trading opportunities and efficiently gain credit exposure.

In the current market environment, characterized by escalating funding costs, contracting margins, and liquidity pressures, the credit market participants are eager for a standardized index-linked product that provides a combination of low tracking error, high liquidity, manageable turnover and customization. Developed in BQuant Enterprise, Tradable Trackers workflow provides a high degree of customization and automation. 

During this webinar, we will delve into the following topics:

1. Bond liquidity analysis and modeling
2. Tradable Tracker Index construction and methodology
3. Common Customization examples
4. Smart beta strategies and tracking error minimization
5. Factor-Based strategies


**The Liquid Credit whitepaper will be made available post-webinar upon request. Please contact Bloomberg Sales/DBG about the Liquid Credit software demo and BQuant Enterprise.**

Speakers

Amine Khanjar

Portfolio and Index Research

Bloomberg

Amine Khanjar is a quantitative researcher in the portfolio and risk analytics group at Bloomberg. His research focuses on fixed income analytics, credit selection strategies, liquidity along with risk modelling. Prior to joining Bloomberg in 2016, he was a senior researcher at Barclays Capital. Amine holds a Masters degree in Financial Engineering from New York University (NYU) and an Engineering degree from Ecole Polytechnique in Paris.

Dmitry Belaev

DBG Quantitative Analyst

Bloomberg

Dmitry is a member of the BQuant Enterprise team at Bloomberg, where he specializes in crafting quantitative solutions for buy-side clients and creating cutting-edge research content. He holds a Masters degree in Mathematics of Finance from Columbia University and earned his B.A. in Political Science from the University of Michigan, Ann Arbor.

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