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The Evolution of Quantitative Risk Management in Hedge Funds

Market overview
Quantitative risk management has been a popular topic of discussion among hedge fund managers in 2021. This is because no one likes to lose money, but ultimately funds prefer to lose profits on something they knew was coming, rather than something they could not predict. Risk management is in fact considered a core component of the investment process directed at identifying, measuring and mitigating risk exposures that a portfolio or investment opportunity could face1. Historically, this practice has been carried out using qualitative techniques, however, in recent years, the increased adoption of models for decision-making has led financial institutions to embrace a more quantitative approach to analyse and manage their risk. As a result of this change, the skills required to be a risk manager have transformed to reflect the technical challenges of this field; and so has the role played by risk managers, which is evolving from being a control function in the back-office to being an actual advisor to the trading team.

A similar point was made in a 2021 publication by a trio of quantitative investing experts at Man Group. The fund’s CIO particularly highlighted the importance of investing in technology that can empower risk analysis as well as considering risk management of equal importance to alpha generation, as opposed to a secondary function that only comes into action after the portfolios are in trouble2. Professor Campbell R. Harvey (Investment Strategy Advisor), who co-authored the publication, argues that hedge funds can no longer afford to overlook the vital role played by their risk teams, and that the key to a successful portfolio design is a dynamic risk management framework that mirrors the firm’s signal generation approach2.

In the past 12 months, I have come across quite a few hedge funds with questions concerning how to best position their risk management capabilities and what type of profile to hire within this function. In this piece, I aim to address these questions by exploring the shift from a qualitative to a quantitative approach to risk management and by providing an analysis of the quantitative risk manager profile and their skillset. This piece will also include a commentary on the most effective positioning of the risk management function within a hedge fund’s structure.

The quantitative approach, explained
The value of adopting a quantitative approach to risk management can be explained at different levels. First and foremost, at a conceptual level, it justifies hedge funds’ sustainability. Investors are happy to pay for the standard performance and management fees charged by hedge funds, so long as the returns provided are uncorrelated to the market and can add diversification to their portfolios. Traditionally, this is the CIO’s responsibility, but a quant risk analyst can add significant value to this, by building a platform that can provide highly technical information on the fund’s trades Risk platforms are in fact designed to measure all sorts of strategies taken by other firms and to optimize risk metrics to ensure uncorrelated returns.

A practical example that explains the importance of a quantitative risk management approach can be found in equity factor-based investing. In recent years, equity factors have become quite prominent among investors, especially through the use of ETFs. BlackRock, which has contributed to ETFs’ increased demand, can provide investors with equity factor exposure for a mere 0.30% fee a year. How can hedge funds offer investors the same exposure and charge them their 2 and 20 fee structure? A quantitative equity factor risk model can compute how much risk in a portfolio is coming from factors vs. stocks and can inform risk managers on the correct limits to implement in order to cap factor risk exposure and in turn produce diversified returns. Because of the quantitative nature of equity factor investing, only a risk manager with a strong quantitative skillset is able to implement these limits, which usually cap factor returns at 15% of the overall portfolio PnL.

Another distinct characteristic of quantitative risk management is that decisions are backed by data generated by models and back-tests, as opposed to the qualitative approach that tends to rely solely on the risk manager’s knowledge of the market and gut feeling. Risk limits can cost portfolio managers their revenue, so it’s important to find a balance that allows funds to operate within a safe risk framework that does not sacrifice too much ROI. While non-technical, qualitative risk managers offer an excellent grasp of the markets and can connect well with portfolio managers on trading and regulatory-related topics, ultimately they might be missing out on what data can offer. A quantitative risk analyst, on the other hand, is able to harness historical, market, company and alternative data to their advantage by collecting it, aggregating it and processing it to produce insights and forecasts.
This can be seen again in the equities space, where fundamental traders arguably have quite a good idea of their risk but might lack consistency across the different names they are trading. A quantitative risk methodology can give a portfolio manager a good visibility of the entire universe, resulting in improved consistency across trades.
Similarly, a quantitative approach to risk management using statistical analysis in the fixed income space can add significant value, as it facilitates the trader’s understanding of the movement of the yield curve and any correlations between different points on the curve.

Even those fundamental portfolio managers or start-up hedge funds that can’t afford in-house risk models and turn to purchase ready-made suites of quantitative risk analytics (e.g. Axioma or Barra), end up needing the support of a quantitative risk manager. In fact, these models tend to embrace a generic approach to risk analysis to serve a broad clientele; they also present issues with data quality and missing layers that require, at best, a really strong developer to resolve them. This was the case for a fundamental PM who left Citadel to set up his own hedge fund; rather than hiring a risk management team, he had bought a suite of factor models to automatically manage portfolio risk, but before being able to raise additional money, his investors asked him to hire a quantitative risk manager. While the PM was initially sceptical, the new hire turned out to be an extremely useful addition as they were able to tailor these quantitative models to the specific risk appetite of the fund.

The quantitative risk manager profile
At first glance, the typical quantitative risk manager profile presents a similar skillset to that of a traditional, front office quantitative analyst/researcher. In both cases, strong mathematical modelling knowledge is required to make decisions on the best methodology to adopt in different situations. Programming skills are also essential in order to navigate, organize and analyse data and to automate processes. Soft skills, like communication and the ability to establish relationships, are of paramount importance too, especially when working alongside investment professionals.
On the other hand, because of the more holistic and broader nature of risk management, the duties of a quant risk manager differ substantially from those of a front office quant analyst. For this reason, it is important that funds looking to adopt a more quantitative approach to manage their risk are able to identify and understand these differences.

A quant risk analyst can add value to risk management activities in several ways; for instance, as previously discussed, they can impose limits on returns from specific investments to help portfolio managers produce uncorrelated and diversified returns. These metrics are not limited to return optimisation, but they also extend to capital preservation; through the use of historical data, a quant risk manager can build models to predict “worst case scenarios” and to forecast potential future losses, and can place specific drawdown limits on portfolios. Scenario analysis is indeed a core function of risk management; through a quantitative approach, risk managers are able to come up with different metrics that generate comprehensive scenarios which can help the fund understand and mitigate even the smallest risk exposure.
Additionally, just like many other investment areas, risk management is witnessing significant technological advancements, including – primarily, the automation of specific processes through machine learning and AI, such as anomaly detection and generation of reports or risk limits. Recently, numerous quant risk managers have mentioned using algorithms to scan through Reddit conversations to pick up market information that can apprise their risk models. Ergo, the presence of a quantitative risk professional is crucial for investment firms to be able to keep up with market innovations.

Risk management & portfolio management: a powerful partnership
As mentioned before, risk management has historically been considered a back-office function within a hedge fund’s structure. While portfolio managers recognise the importance of limiting losses as much as possible, the general perception among front office staff is that meetings with risk managers are a once-a-month business discussing matters that risk takers are already aware of and resulting in imposed risk limits, preventing the fund from making bigger profits.

The aim of this articles is to invite readers to adopt an alternative perspective on the role played by the risk management function and its relationship with alpha generation and portfolio management teams. For instance, what if risk management wasn’t just about risk limits and guidelines, but could instead help portfolio managers trade at their full potential? Risk managers play a key role in constructing and optimising portfolios, by providing granular details on the size and direction of specific positions, not only to off-set risk, but to avoid sacrificing too much PnL. Their input is also vital for more risk-averse fund managers, who may not be inclined to take enough risk in their bets; risk managers are able to provide insights on the risks that portfolio managers can afford to take, as they have a well-detailed overview of the different portfolios and a quantifiable understanding of the risk appetite of the fund.
Funds supportive of the alignment between risk management and risk taking teams can also enjoy huge benefits from a talent acquisition point of view; these days, risk managers are attracted to firms that allow them to work closely with the portfolio managers and to be direct contributors in the design of the firm’s investment process.

Case study
While it may be difficult to quantify increased returns as a result of the work conducted by quantitative risk managers, there is evidence that, through the use of quantitative techniques and a close collaboration with the investment team, this function can play a key role in protecting downside risk.
This was the case for a $40 Bn AUM hedge fund specialised in equities strategies across the long/short and quantitative/systematic spectrum. Prior to 2020, the fund had predominantly relied on its risk management activities to be handled by a team of experienced risk analysts with strong Economics/Finance backgrounds but limited quantitative skills. Shortly after the Covid-19 pandemic started, the CRO realised that his risk management team alone wouldn’t have been able to sustain the pressure that the volatile markets were putting on the business. Coming from a quantitative background himself, he understood the importance of hiring a quantitative risk manager, not only to build a quantitative framework, but to work with portfolio managers on ad-hoc risk management projects. To improve the existing risk framework, the new hire started working on the heavy-lifting of the code, which resulted in the creation of stronger foundations for risk applications and enhanced data integrity and quality. Once the framework had been improved, the quant risk manager was able to focus on the development, back-testing and implementation of new equity factor risk models, designed to specifically support the different investment styles of the firm. By adopting these tailored models, the portfolio managers were able to better identify, monitor and manage risk in their portfolios which, combined with improvements that the quant risk manager made on portfolio construction techniques, resulted in limited losses. In spite of reported returns not being impressive, following the new hire, this hedge fund managed to end the year unscathed, unlike many of its competitors in the equities space that had not been relying of a quantitative risk management framework.

The technological advancements of the past decade have had a strong impact on the way hedge funds operate, resulting in fund managers adopting a model-driven approach not only for their signal generation process, but for their risk management practices, too. In a post-pandemic era where hedge funds are questioning their approach to risk management and working towards strengthening this function, quantitative risk analysis has emerged as a topic of interest for many market participants. As discussed in this piece, quantitative risk managers are able to build a robust risk management framework by utilising advanced, data-driven techniques and models to minimise losses and to contribute to the generation of successful strategies. This shift, combined with a tighter alignment of the risk management group to the investment team, can add significant value to the fund’s investment process. Hedge funds that will adapt to these changes will be able to secure the best quant risk talent and, as a result, to reap off the rewards of minimal losses and maximal returns. Ultimately, fund managers are only as good as their teams.


[1] Kenton, W. (2021). Risk Management in Finance. Investopedia [Online]. Available from:

[2] Rattray, D/ Harvey, C.R. & Van Hemert, O. (2021). Strategic Risk Management: Designing Portfolios and Managing Risk. Wiley [Online]. Available from: