Risk management technology provider of the year: MSCI

Macro risk model offers means to stress test multiple worst-case scenarios

Samprabhu Rubandhas, MSCI

Buy-side Awards 2016

Uncertainty is the defining characteristic of the current investment landscape, and asset managers often struggle with how to reflect macroeconomic-level factors – Brexit, or an oil shock or rate hike, for example – in their portfolio risk management. MSCI is offering a macroeconomic stress-testing framework that should help.

MSCI's Macroeconomic Risk Model can be used to create stress tests by user-defined risk factors, and comes with stress tests for a number of potential market-influencing events, such as Grexit or the rise of populism, for example.

MSCI provides best-practice correlation periods for the stress tests, but users can also define their own parameters and see the results side by side. "The idea is not to provide a crystal ball, but to offer a framework for communication and the discussion among stakeholders of macroeconomic events and their impact on portfolios," says Samprabhu Rubandhas, head of technical sales for Europe, Middle East and Africa at MSCI who is based in London.

The macroeconomic risk model is a recent addition to MSCI's RiskMetrics RiskManager multi-asset class risk platform that covers the investment process all the way from asset allocation through to reporting. Another area where MSCI has enhanced its platform to address current trends is in the modelling of private assets.

The search for yield has driven firms towards assets such as real estate and private equity, with many assuming they have to be treated separately in their risk management framework because of their unique characteristics and lack of data.

Now more asset managers are saying they need a cohesive risk framework that incorporates private asset classes. We have taken a lead and have integrated private asset class models with our existing asset class models
Samprabhu Rubandhas, MSCI

"Now more asset managers are saying they need a cohesive risk framework that incorporates private asset classes. We have taken a lead and have integrated private asset class models with our existing asset class models," says Rubandhas. MSCI has acquired property data provider IPD and works with private equity data specialist Burgiss. "We take their data and apply our expertise in terms of translating and formatting it to make it usable by clients," says Rubandhas.

Liquidity risk is another issue that has been exercising both firms and regulators since the financial crisis. MSCI already had research and development underway in this area prior to 2008, and in 2013 added multi-asset class liquidity risk measurement to its suite of risk tools.

LiquidityMetrics enables users to perform portfolio liquidity capacity checks and assess the underlying time horizon required for liquidation, and stress-test the liquidity of the portfolio under alternative trading scenarios and market impact conditions. With the theory and practice of liquidity risk management still evolving, MSCI is endeavouring to keep its application at the leading edge through continued R&D and monitoring of academic literature.


Given the difficulties of the current investment environment and the soaring expense of regulatory compliance, buy-side firms are desperate to reduce their costs, with a prime target being IT. "Ten years ago, there was an acceptance of a variety of tools in the risk and performance ecosystem. Now with budget pressures and a focus on the cost of maintaining five or 10 different systems, there is an appetite for consolidation," says Rubandhas.

MSCI aims to offer firms a route to a simplified and more cost-effective investment IT infrastructure through the RiskManager platform, which integrates risk management, performance measurement and liquidity risk management across asset classes and analytical and reporting functions. Key to a successful consolidation process is data management, and MSCI has the expertise born of many years providing market data and loading it into a multiplicity of systems. "We are able to get a client's in-house data, enrich it if necessary and load it on to the MSCI platform. We can also help with loading it on to other platforms, perhaps in the middle office or front office, or simply optimise their workflow across platforms. This all helps reduce the total cost of ownership of systems," says Rubandhas.

Servicing clients that include some of the largest asset managers globally with massive portfolios means scalability of computational performance is essential. MSCI has invested significantly in processing power and its systems are currently performing more than one trillion calculations per day, covering two million time series, more than 17 million client positions and 220,000 benchmarks. "Our systems are built for scale, so large asset managers can be confident we can handle their needs," says Rubandhas.

Firms have a choice of how they access RiskManager. "For clients that have small- to medium-position volumes, our highly interactive cloud-based web application is the best choice, as there is no need for hardware implementation," says Rubandhas. Larger firms that already have a front-end system in place can access the platform via web services integration, while those with high volumes of positions or portfolios to run through the application can use MSCI's managed service customised to their requirements.

RiskManager models risk from the bottom-up with full repricing of positions and the use of granular factors that capture market exposures and basis risk, as well as allowing attribution and decomposition of risk by factor, broad risk categories or custom categories.

Granular approach

It was this granularity of methodology, as well as the ability of the platform to handle credit risk for illiquid instruments that led a major Scandinavian public sector pension fund to implement MSCI's system. The fund has a strict mandate with clearly defined asset allocation and tracking error limits.

"We are using RiskManager to monitor relative market risk, as well as measure the expected potential future exposure to our counterparties. The granularity of the model makes it straightforward for us to understand and to decompose the risks along the dimensions we require," says the head of risk at the fund.

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