A Quantitative Framework for Structured Products Portfolio Optimization

Posted by Matteo Tesser on 6 April 2016 | 0 Comments

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In Fairmat every day we calculate risk/return profiles for more than 9000 structured retail products providing useful information to Funds Managers, IFAs and Investors through our product comparison services and our products testing reports.  This is a strong commitment for our team, and we aim to include more than 20,000 new products by the end of this year.

Besides that, In the past months, several users manifested interest in structured portfolios construction and analyses. We were already providing structured portfolios fair values and Greeks monitoring tools, and we realized we could try to offer some improvement with respect to the actual structured portfolios construction techniques used in the industry.

Currently, the construction of structured portfolios is usually performed empirically by experts or by the means of heuristics. This is mainly due to the several implementation challenges staying behind the construction of a quantitative implementation.

 Our accurate terms/payoffs database, the availability of market data and the expertise spread on our team provided us all the ingredients to develop a quantitative framework for testing risk / performance profile of any structured products allocation.

Testing the risk/return profile of a portfolio allocation requires the joint simulation of the underlying risk factors (which could be performed with Monte Carlo based forward looking methods or with historical based bootstrapping methods), and the mapping of the non linear relationships between the risk factors and the products payout (which can be derived from term terms data). As you may notice these types of analyses require a lot of market data (underlying history, issuers credit scoring, terms information), plus robust simulation models and computational power. 

We bundled this framework into the Fairmat Cloud platform within our new portfolio designer tool which enables users to assess given structured products allocations or to generate products allocation proposals (or rebalancing proposals) for different risk aversion levels just by specifying design goals, as it happens in shares/funds asset allocation packages.












Figure 1: In the figures risk/return profiles for structured products are indicated in grey, while the current products allocation and four different proposal are indicated in orange.

Our structured portfolios designer tool will propose efficient portfolios starting from simple input like the initial selection of products, the allocation budget, and the preferred risk measure. Every other detail is handled by the platform including market data and the access to a curated database of retail products and the possibility of autonomously mapping custom OTC products.

If you want to know something more you can read this presentation or instantly try the Fairmat Cloud platform which also works with a pay-as-you-go mechanism in which users can start to use the platform gradually and are billed just for the valuations they request.

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