Configuration and control: next-level risk analytics for alternative assets

Fragmented data and siloed analytics hinder firms, preventing them from unlocking their full potential. At a Risk.net panel session, convened in collaboration with KWA Analytics, experts delved into the evolving needs of traditional and alternative asset industries, including the transformative impact of advanced data analytics and model training within new customisable frameworks

Risk managers, traders and portfolio managers each have unique requirements in the types of risk analytics and how they consume them, but they also have common ground.

Siloed data handling and analytics can severely constrain these cohorts, especially across diverse asset classes, when tools and systems are restricted to each function.

Today, next-generation platforms make it possible to configure models and analytical reporting to meet each of their requirements. Targeted capabilities within the platforms can allow financial services firms to further customise analytics based on asset class.

The KWA Beacon Platform, a cloud-native platform designed for cross-asset use, is one such bespoke solution. It is solving the complications caused by these silos, with a customisable and more collaborative solution than just relying on individual applications or numerous Excel spreadsheets. 

At a Risk.net panel session convened in collaboration with KWA Analytics, experts discussed the evolving needs of traditional and alternative asset industries, the transformative impact of advanced data analytics, and model training within new customisable frameworks. This article explores the key themes from the session.

Advanced analytics

Advanced data analytics empowers trading firms to make faster, more informed and more accurate decisions. By leveraging data to optimise strategies and manage risk, financial institutions can achieve a competitive edge while adapting to ever-changing market conditions.

Nevertheless, siloed data and analytics create challenges, especially when common needs exist beyond these silos. The Beacon platform stack approach provides common building blocks configured to suit bespoke needs. 

Paul Rodriguez, sales engineer at Beacon, described it as being akin to a mixed box of Lego bricks and a complete Lego model built from a set. A ‘Beacon-style Lego model’ sits somewhere in the middle, he added.

“We look at some of the open-source components and some internal libraries as being the mix-and-match set of Lego, and then try to rebuild a Beacon within the client’s own infrastructure,” he said.

These bricks, Rodriguez explained, give asset managers the components they need to build their trading and risk applications, with time-series data storage sitting at the bottom, covering market data such as trades and positions. Other bricks in the stack include market interface and raw market data, with trading books and portfolios sitting at the top.

Cutting-edge solutions and innovative functionalities within such platforms can enable companies across diverse asset classes in energy, commodities, capital and real estate markets to move into the future with scalable and cloud-native solutions – all while enabling the agile management of large-scale technology and supporting modern development and operation processes.

Scalable stack

The multidimensional platform can capture sparse data in a time series and enables portfolio teams to conduct bi-temporal data modelling: For instance, rerunning second- or third-quarter risk reporting as it was known to exist then, and as it exists later, and allowing users to identify any delta difference due to position correcting or updated market data that may have caused evaluation or risk differences.

One step above that is market modelling using the data captured and benchmark tenors to build curves, calibrate surfaces and capture closing prices across markets.

Up another level is a full set of exchange-traded and over-the-counter instruments. Each instrument simply needs to know how to price itself, lifecycle itself and reach down a level into the market models to retrieve the data it needs to achieve that. “So, for a simple interest rate swap or bond instrument, for example, it knows which markets it needs to look at to get its discount factors and what it needs to project its cashflow,” said Rodriguez.

Higher up the stack sit trades and positions in instruments, enabling the platform to capture trades streaming in from multiple data sources.

At the very top are applications and application programming interfaces. The technology has a framework that allows users to build in user interfaces that can run on the cloud, eliminating the need for JavaScript knowledge.

Model training and digital transformation

Agile technology is crucial across the board. Ram Meenakshisundaram, senior vice-president of quantitative services at KWA Analytics, emphasised the value in having customisable dashboards and on-demand capabilities to assist firms with a risk methodology challenge.

The advantages of the platform’s full set of development tools can extend Beacon components or even write new ones, he added. “It is applicable across real estate assets, funds of funds, securitised products and traditional asset classes such as volatility swaps and volatility derivatives.”

As a private yet open-source system, modifications are easier to configure and implement, Meenakshisundaram noted.

KWA Analytics has onboarded a large alternatives manager and commercial real estate services provider to the platform to centralise and scale analytics by better managing large spreadsheets that are used for modelling commercial real estate.

One approach would have been to dissect calculations in the spreadsheets and build a dashboard for them. But KWA’s developers took another angle, bringing data directly into the platform and modelling within. “Basically, getting rid of their spreadsheets while still providing firms with real-time analytics,” said Meenakshisundaram.

“We can export data out to Excel, and analysts can still use their spreadsheets where all the calculations are being done on Beacon and offload it into the grid. So the user has a single architecture, and a single view of data and analytics.” Additionally, the size and complexity of spreadsheets will reduce considerably in comparison with what trading desks have been utilising outside of such platforms.

On the real estate use case, Meenakshisundaram explained how Argus – a third-party data analytics firm – implements many of their properties, and models some of their risk analytics and calculations. “We took all of those cashflows coming in from a [real estate] property and mapped it back into the Beacon objects. Now it’s treated as being homogeneous with the rest of the Beacon system.”

This allowed the tool to handle all of the necessary scenario assumption between the real estate and the funds, while its open-source nature allowed KWA to customise it.

Meenakshisundaram further added that KWA “will build and provide the full source code for all assets” before handing it back to the user – the asset manager and real estate firm in this case. “They can customise it to their heart’s content or call us again, and we’ll model it.” 

In summary

There is no doubt that advanced data analytics is crucial to traditional and non-traditional asset classes, and next-level analytics platforms offer flexibility and versatility across asset classes without the need to build from scratch, as appetite for new capabilities grow.

Flexibility on modelling and being able to shock diverse parameters within any portfolio are cutting-edge risk analytics capabilities for now but will become even more important in the future.

As financial institutions and asset managers innovate and diversify across asset classes, set up new trading desks, products and teams, the need for common infrastructure and shared financial modelling components will beckon, and platforms that enable firms to align these needs, instead of reinventing the wheel, will stand out.

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