Buy-side risk managers and FactSet’s global head of quantitative analytics gathered for a Risk.net webinar to discuss topical risk management trends for asset managers and to consider the industry challenges posed by the recent COVID-19 pandemic.
SPEAKERS:
- Boryana Racheva-Iotova, SVP, Global Head of Quantitative Analytics and Risk, FactSet
- Racim Allouani, Head of Portfolio Construction and Risk Management, KKR
- Lisa Wang, Director of Investment Risk Management, AllianceBernstein
Intense competition, market volatility, and a demanding regulatory environment continue to raise the stakes in investment risk management. As asset managers grapple with squeezed budgets and elusive sources of return, the unprecedented disruption caused by the COVID-19 pandemic has served as a painful reminder that future gains and innovation will rely on sound risk management principles across the full set of portfolio and compliance risks.
Risk leaders must enable and support new investment platforms, data and analytics capabilities, and operations and strategies while ensuring their enterprises remain sound, secure, and compliant.
One discussion topic was how specific trends that had already begun taking shape in the industry were accelerating as a result of COVID-19. These trends include the requirement for reviewing the approaches towards building asset location mix, asset-liability management, and goals-based investing into wealth management.
The socioeconomic and geopolitical uncertainty caused by the pandemic has already resulted in much more sophisticated approaches towards building asset allocation mix and asset-liability management.
According to Boryana Racheva-Iotova, Senior Vice President and Global Head of Quantitative Analytics and Risk at FactSet, the socioeconomic and geopolitical uncertainty caused by the pandemic has already resulted in much more sophisticated approaches towards building asset allocation mix and asset-liability management.
"A lot of acceleration has been observed within the solutions and advisory groups within the asset management community, as well as the trends towards shifting assets under management to an outsourced CIO, and so forth," she said.
Racheva-Iotova addressed FactSet’s focus on supporting firms and risk managers through ongoing change and upcoming trends.
“We continue to see those trends being quite strong, and through that, of course, the need to support our clients with data, models and solutions in order to execute on those activities more efficiently,” she added.
In addition, there has also been a bigger focus on building a more holistic understanding of risk, specifically liquidity and credit risks. A key question for the market right now is how the recovery will happen from the COVID-19 pandemic and what the recovery process will look like.
Given the market volatility during the crisis, asset managers have needed to get a better grip on understanding alpha and upside potentials and must be able to take advantage of these opportunities.
“We see much more attention on more detailed analysis of both alpha as well as risk, really understanding exposures extremely well and what can lead to disruption of the exposures, dislocation of the exposures, dislocations in the correlations between different asset classes, as well as even between securities and types of risk drivers,” said Racheva-Iotova.
A newer approach to risk has also been a major trend. There has been increased attention on risk budgeting, particularly tail-risk budgeting, tail contribution to risk from factors, as well as a group of assets' securities.
Deliberative risk management is also getting a lot of attention. This approach requires special tools and risk models that necessitate full repricing to capture all the non-linearities that can come with particular trades as well as robust stress testing.
“In terms of stress testing, we see really hugely increased interest in terms of the type of the stress tests, as well as the complexity of the stress tests being built,” said Racheva-Iotova.
Stress tests are being incorporated into earlier and earlier stages of the portfolio construction process.
Stress testing has also evolved for AllianceBernstein. Just as different levels of aggregation are being included more, stress tests are being incorporated into earlier and earlier stages of the portfolio construction process. For example, if a particular amount is allocated to a specific position, strategy, or sector, there needs to be an analysis of the various levels of risks shown within the stress tests.
“Those types of risks get aggregated at an earlier stage of the portfolio construction. So that is one of the things that has been evolving and has really accelerated post-COVID-19,” says Lisa Wang, director of investment risk management at the asset manager.
For Racim Allouani, who oversees portfolio construction and risk management at KKR, the main risk management focus has been ensuring that companies in the private market have enough liquidity to survive the current pandemic, as well as potentially keep going during a further shutdown should things change in the future.
“We might have to pick our battles, and that is a little bit what we are concerned about in the post-COVID world, is that we will probably see some attrition, higher and higher defaults. It might be concentrated in some pockets of the market,” he says.
In credit, the recovery (or lack of) has differed depending on the specific sector. Retail, travel, leisure, and energy have been hit materially, while other sectors like pharma, utilities, and tech are outperforming. While orderly and expected, these dispersions have been crucial to monitor from a risk management perspective for KKR.
For other firms, there has been a gradual increase in demand for more comprehensive risk analysis. At AllianceBernstein, the risks at the portfolio level as well as various layers of aggregation, whether it be at country level, sector level, strategy level, individual stock, or individual-positions level, all must be factored into risk management analysis and decisions.
That has developed into looking at “multiple lenses” of risk, says Wang.
“The other trend we have observed is also to bring in a more integrated risk, in the sense that we care about obviously the actual allocation to individual stocks, we care about the risks contribution coming from individual positions, we care about what their stress test characteristics are like, we care about their liquidity. So, for the allocation for the portfolio construction, we are looking at multiple lenses of risks as well,” she says.
More data and more diverse data is continually needed as the spectrum of risk drivers grows.
To be able to manage and review this comprehensive risk analysis means there is a greater demand for larger datasets and for pre-designed sets of reports to present that data in the most consistent way possible. This helps risk managers stay on top of volatile sectors, too.
The importance of data is a growing trend among buy-side risk managers, particularly as the use of machine learning and artificial intelligence is on the rise. More data and more diverse data is continually needed as the spectrum of risk drivers grows.
“But that data needs to be useful data, and it should be data that helps us to isolate the risk-related signals instead of just introducing noise into the risk modeling process,” says Racheva-Iotova.
For FactSet, risk data involves two perspectives: the breadth and quality of datasets and lookback periods, which can be meaningful. For example, the relationship between equity markets and credit default swaps 20 years ago is hard to fathom because there may be a lack of data from that period and some markets might not have existed, such as CDS. Machine learning can help in these circumstances.
Alternative datasets are becoming increasingly important and can constantly change the risk parameters as the market landscape becomes more complicated, and new types of risk forces and risk drivers are constantly changing the profile.
“Those are alternative new datasets that are definitely helpful, but, again, you need to have a particular purpose, you need to have a particular goal, and then certainly look for the right data sets,” says Racheva-Iotova. “In some instances, the datasets themselves will have to be first of all analyzed through machine learning techniques in order to extract the relevant signals before incorporating them into the risk modeling.”
The use of big data has been particularly helpful to KKR during the COVID-19 pandemic. The firm used “high-frequency data” like the patterns of credit cards, spending, re-openings, and hospital data, and a lot of big data that have not typically being tapped into to any great extent.
“This helped analyze which parts of the economy would be reopening faster. We were doing that in China, for example, because they faced the whole crisis before the West. So we have been using much more data, including alternative data, in this episode, for example, of COVID-19, so even for the private side,” says Allouani.
Managing risk in today’s world of asset management goes hand-in-hand with bringing on the right technology solution.
Managing risk in today’s world of asset management goes hand-in-hand with bringing on the right technology solution. Larger businesses have an advantage given their deep pockets and Allouani feels that it could be “difficult” for mid- and small-sized players to continue to stay relevant without combining the right technology with the necessary talent pool.
“It needs to go hand-in-hand with the necessary talent who can understand the benefits of the technology, apply it for the purpose of the particular asset manager, and for the purpose of the investment approaches and investment mandates that they have,” he says.
While theoretical innovation has been around for decades, practically implementing that within an organization is the harder aspect of a risk manager’s job.
“This is the type of proven innovation that is a must for everybody. This type of innovation is something that definitely needs to be observed within the risk management process, no matter the size of the asset manager,” says Allouani.
For some small- to medium-sized players who want a comprehensive set of risk analytics but don't necessarily have the same budget as some of the bigger players, the key is to plan this in a more structured way.
Technology can also be a strength in this kind of scenario. For example, business intelligence type analytics can help link together with the risk data. The type of processing that is generated from this business intelligence software can save businesses a lot of time in terms of building their risk presentations.
“If you want to do different layers of risk into your individual portfolios, into your individual sectors, a lot of the business intelligence type software is a tool you could actually use for the small- to medium-sized players. So you don't have to build everything from scratch and have a large budget dedicated to a technology development effort,” said Wang.
Technology has also played a role for AllianceBernstein from an operational risk perspective during the COVID-19 pandemic and accelerated another trend that was already starting to take hold of the asset manager. That had already been a concerted effort to move to a virtual work environment that has picked up pace since March.
“Within our own firm, we have actually been migrating to a virtual work environment even before COVID-19. So even when we are in the office, we don't have to log in to a particular desktop. We have virtual desktops set up so we can actually log in from anywhere within our building. Post-COVID-19, we are logging in through a VPN, but the virtual desktop environment has already been set up so that transition itself is seamless,” said Wang.
The panelists were speaking in a personal capacity. The views expressed by the panel do not necessarily reflect or represent the views of their respective institutions.
Post Comment
