Our buy side risk management roundtable roadshow continued on its European leg earlier this month in Luxembourg where we hosted an interactive session with senior risk managers from leading Management Companies (a.k.a. ManCos). You can read more insights from our Amsterdam and London roundtables here.
Here are the highlights, key perspectives, and concerns shared by the participating risk managers.
Fixed Income Data Insights & Inputs
In any analysis, including risk analysis, it is crucial to start with high-quality data. As the saying goes, "garbage in, garbage out." By utilizing comprehensive and precise data, we can conduct precise calculations and derive more accurate insights from it. This emphasizes the significance of obtaining reliable and extensive data to ensure the integrity and validity of our analyses.
Accordingly, we began our event with a discussion on fixed income data insights and inputs, led by Nathan Kirk, one of S&P Global Market Intelligence’s experts on pricing and reference data. He highlighted interesting patterns in government, municipal, and corporate bond yields. He also spoke to recent changes in collateralized loan obligation (CLO) bid-ask spreads which are trading at their tightest range in recent times and reflect the overall strength of the market. A similar trend was noted in leveraged loans where the number of leveraged loans that dealers are quoting is at a 5 year high.
The credit spread curve is crucial for assessing risk within fixed income portfolios and utilizing issuer-specific curves enhances the capacity to measure idiosyncratic risk. To find out more about the relevance of the issuer risk for the overall risk management process in ManCos, we surveyed participants on their coverage of risky discount curves for fixed income securities; 45% reported coverage exceeding 70%, while 27% indicated a range of 50-70%. Some participants noted that they do not use credit curves because they manage only private assets.
The conversation then shifted to the availability and granularity of historical curve data. Some participants emphasized the importance of granularity over a long history, especially during volatile market conditions, while others believed that both granularity and history are equally important. While historical data provides the context and trends over time, granular data offers the precision needed for accurate valuation and risk assessment. Both aspects complement each other and are essential for comprehensive risk management.
These comments suggest that some ManCo managed funds are invested in a wide range of fixed income products and the sophisticated market data (rates and spreads) allow for analyzing the risk of these assets in a proper manner. A lower-level detailed view, that cannot be achieved with proxy-curves, gives risk managers more detailed and nuanced information about their fund investments.
Strategic Priorities for ManCos
According to the 2024 barometer, PwC’s Observatory for Management Companies, Luxembourg’s Management Companies now boast 30% of Europe’s AUM.[1] Many ManCos are scaling their business operations to support a growing range of funds, markets, and services. Alternatively, they may try to balance flexibility and scrutiny in design, distribution, and administration of investments. Either way, they must increase revenue and reduce costs through scale or additional efficiency.
Given this context, the next question revolved around the strategic priorities of ManCos. Specifically in understanding whether they place greater emphasis on technology, market presence, solutions, or services.
The clear first choice of our participants was “Data & Technology” with 60% of the votes.
It was commented that local regulators are increasingly adopting data and technology-driven regulations. However, it was also mentioned that some ManCos may not have complete autonomy in making their own technology decisions, as they are often subsidiaries of parent companies based in other countries.
For even the simplest funds, the amount of data required to comply with regulations has become huge. This has led to a multitude of problems like data overload, challenges with extracting the relevant information in a timely manner and a substantial cost of maintaining large amounts of data. ManCos must handle large, complex, and time-critical demands across multiple entities and jurisdictions. Technology is a must to successfully comply with the wide range of regulations and the data requirements that comes with it.
Market Risk and Value at Risk (VaR)
VaR is widely regarded as one of the most valuable measures of risk. Many European funds, especially those most sophisticated, opt for the VaR approach to fulfill UCITS requirements. UCITS IV regulation establishes strict rules for the computation of VaR and requires regular back-testing to complement VaR estimation. VaR is to be computed and monitored on at least a daily frequency but depending on the strategy being pursued, intraday calculations may also be necessary.
After discussing briefly, the most common VaR applications we moved on to a question about the role of VaR in today’s challenging market and the significance in the risk management process. 89% of the respondents said that they use it for a combination of:
- UCITS or regulatory purposes
- Guideline limits on mutual funds
- Internal risk management
Thus, showcasing the versatility of VaR in ManCo's.
During the discussion, some participants expressed their skepticism about the value of measuring and tracking Expected Shortfall alongside VaR, even in volatile markets. They pointed to its limited added value for the regulatory use case and said that its back testing was quite cumbersome, as not only the occurrence but also the intensity of VaR violations is important.
It was noted that the majority of ManCos manage portfolio risk using historical VaR, while a few also use Monte Carlo as a complementary approach. This is mainly due to the high transparency and traceability of the historical simulation, which is quite convenient when a regulator challenges the VaR figures or asks methodological questions. The participating risk managers explained that they prefer short simulation periods (usually one year), to let the model react quickly to changes in risk regimes.
Stress Testing Portfolios for Market and Liquidity Risk
Stress testing is well suited to assessing the degree of vulnerability of a portfolio in situations of crisis where normal market correlations break down and the mainstream measures of risk such as VaR fail to provide a fair picture of potential losses. For that reason, stress test is an integral part of the UCITS regulation and European investment companies are obliged to measure the potential impact of adverse scenarios on their financial assets.
In its Risk Management Principles for UCITS, the European Securities and Markets Authority (ESMA),[2] describes how Stress Tests:
- Are usually meant to capture the possibility of rare and severe losses which could occur during market shocks, and which are unlikely to be measured by the models as they tend to follow structural breaks in the functional relationships between market variables
- Should cover all quantifiable risks […]
- May reflect subjective scenario hypotheses […] not simply mirror historical conditions
- May contribute to generation of exceptional warnings […]
While stress testing is a well-established and commonly used process, we wanted to delve deeper into the preferred types of stress tests that participants utilize to enhance their calculations beyond VaR. Participants were asked about their priorities for improving stress tests within their investment risk processes.
Two dominant forms of stress tests were identified:
- Predictive/Inferred Market Risk Stress Tests (33%): This approach was favored by the group, particularly in anticipation of upcoming economic and geopolitical events such as a Fed rate cut or the US election. It allows risk managers to assess how future changes may impact portfolio value. Interestingly, risk management professionals at our Amsterdam roundtable ranked this as a slightly lower priority with only 23% of votes, just behind their top priority of Liquidity Risk.
- Liquidity Risk Stress Tests (33%): As liquidity stress testing is a requirement from the European Regulatory body ESMA, risk managers dedicate a significant amount of time to capturing the impact of market liquidity on assets and portfolios. The group discussed the challenges associated with less liquid or illiquid assets; S&P Global, being a leading vendor of liquidity data, is embracing this task. A similar sentiment was shared by risk management professionals from our London roundtable who ranked this as a joint top priority, along with Predictive/Inferred Market Risk Stress Tests with 40% of votes for each.
Analyzing Climate Impacts
As we are all aware, sustainability is increasingly recognized as a critical risk factor. The Commission de Surveillance du Secteur Financier (CSSF) emphasizes the importance of integrating sustainability risks, indicators, and stress tests into the risk management process for IMFs. “A sustainability risk management process notably involves, amongst others, reflecting the relevant sustainability risks, with the corresponding sustainability risk indicators, in the fund’s risk profile, the risk limitation system and the corresponding reporting to the senior management and the board of directors. This also includes, where relevant, the implementation of stress tests and scenario analyses specifically designed towards the relevant sustainability risks for the funds under management.”[3]
It is crucial to consider how firms prioritize analyzing climate impacts and embedding sustainability as a risk factor in their modeling choices. This shift reflects the growing awareness of climate-related risks and the need for sustainable investment strategies.
During our discussions on climate risk, participants were asked about their focus priorities in Portfolio Climate Risk for 2024.
50% of the participants stated that they are currently focusing on analyzing climate impacts, while 20% prioritize embedding sustainability as a risk factor into their modelling choices, and 30% aim to achieve uniform data and scenarios. Interestingly, at our Amsterdam roundtable 43% of attendees voted for embedding sustainability as a risk factor in their modelling choices as their top priority.
There was a discussion on carbon impact, and the impact on asset prices and spreads. While carbon reduction was admitted as being an important climate factor, the participants use a wide range of environmental data to manage climate risk.
It was also noted that due to the long-term nature of climate change, testing its impact within short-term horizons can be challenging.
Future of Artificial Intelligence in Risk Management
Artificial Intelligence (AI) is increasingly recognized across industries for its potential to significantly transform day-to-day business activities; risk management being no exception. From automating repetitive tasks to helping conduct insightful analysis, AI frees up valuable time for risk managers to focus on more strategic activities. It can also process and analyze large volumes of data, identify patterns and provide unique insights. Large Language Models (LLMs) and Generative AI have seemingly boundless possibilities as they continue to improve at an impressive rate, none-more-so than distilling relevant information from vast amounts of unstructured data, crucial in helping risk managers optimize their workflow.
To gain a deeper understanding AI implementation in risk management, participants were asked to share their insights on various use cases for AI. Notably, a significant 50% of participants expressed their preference for the potential of automatic generation of risk reports, believing that AI could save time and resources by automatically creating comprehensive risk reports, enhancing report consistency, and delivering critical information in a timely manner. Interestingly, this viewpoint starkly contrasted with the opinion of attendees at our London roundtable, who allocated a mere 6% of votes to this particular use case which highlights the differing priorities between London and Luxembourgish risk management use cases.
Another popular use case for AI amongst 30% of our respondents was risk analysis generated from natural language questions. Some risk managers use internal Chat GPT-like LLMs to interpret natural language queries related to risk and provide unique and insightful risk and investment advice.
Only 10% of Luxembourg roundtable respondents indicated that they could see a role for AI in automatic detection of outliers and early warning signals, whereas 44% of participants in London, and 35% of participants in Amsterdam saw this potential. This difference may be due to the nature of the Luxembourg fund industry’s function as a hub for Undertakings for Collective Investments in Transferable Securities (UCITS) and Alternative Investment Fund Managers (AIFM) funds, which are subject to stringent regulatory requirements, which might lead to a more conservative approach towards new technology like AI.
Modelling of Macroeconomic Scenarios
Even in the absence of any specific regulatory requirements, conducting macro stress tests can be highly beneficial for ManCos. They help in identifying and understanding the potential vulnerabilities of funds from a macroeconomic perspective, and when considering scenarios that stress funds’ idiosyncratic exposures, ManCos can gain a more comprehensive view of the risks involved. Sudden changes in macroeconomic factors such as GDP and inflation can have significant impacts on the performance of funds. Quantifying these impacts can complement traditional risk assessment approaches like VaR calculations or usual stress tests and provide a more holistic risk picture. The roundtable participants were surveyed to assess the advantages of Macroeconomic Risk Modeling, as from the case point above, it can play a decisive role in well informed investment decision making.
The poll revealed that the most significant benefit, with 75% of the votes, is its ability to reveal the relationship between macroeconomic events and asset prices. This finding underscores its importance in helping risk managers to make better informed decisions.
When asked about the tradeoff between the sophistication and transparency of a macroeconomic scenario, participants highlighted the need for some degree of complexity. However, some participants raised concerns about the similarity of granular macroeconomic factors, which could result in multicollinearity in the stress scenario.
We would like to express our gratitude to all the participants who contributed to our insightful discussion on buy-side risk management engagement and expertise, whose valuable insights and expertise made this event truly exceptional. S&P Global Market Intelligence is committed to continuing this dialogue beyond this event to further enhance our understanding and collaboration in the field of risk management, so we’ll be continuing roundtable series across Europe, United States and Asia over the coming months. Please register your interest if you would like more information on these events, or recommend a specific location near you.
[1] “PwC’s Observatory for Management Companies”, PwC, May 23, 2024.
[2] “Risk Management Principles for UCITS”, CESR/ European Securities and Markets Authority (ESMA). February 2009.
[3] “CSSF Thematic Review on the implementation of sustainabilityrelated provisions in the investment fund industry”, CSSF.