Risky Alternative: Solving Enterprise Risk for Alternative Investments

No one likes getting a physical exam, but when you get to a certain age, it’s a necessity. It can literally be a matter of life and death. You get your blood pressure taken, urinate in a cup, have your height and weight recorded, and have blood drawn. If you’re a smoker or a drinker, and depending on your age, you could possibly get your kidney function checked, and you might even get your lung health analyzed and perhaps your liver, too. Your cholesterol is noted. If you’re a man, perhaps you turn and cough as the physician gets uncomfortably hands-on with you, while women have similarly awkward examinations. 

When done thoroughly, a physical is customized to the individual. Enterprise risk analysis is a lot like that. You can run a parametric value-at-risk (VaR) calculation and throw in a beta number, but that’s about the equivalent of a physician simply taking your blood pressure and weighing you, telling you to cut back on the red meat and whisky, and sending you on your way with a clean bill of health. Sure, you’re probably not at risk of a major heart attack, but you can’t be too certain with a check-up that consists of just one or two rudimentary tests.

Thoroughness is key, both for physicals and risk analysis. Institutional investors are finding this out, and they’re learning that drilling down into enterprise risk is extremely challenging, especially when it comes to investing in alternatives like hedge funds, private equity and real estate.

Pulling in Data

When done well, enterprise risk analysis consists of pulling in data from across the organization, and drilling down to as granular a level as possible—ideally to the position level. It means running volatility, correlation and exposure calculations. It requires factor analysis and making sure to individualize the proper set of at least half-a-dozen factors for each alternative portfolio.

“The quality-of-fit of the model is paramount, and it is ignored in this industry left and right. It’s high time it stops. Engineering does not do that. When they build a bridge, they measure the tolerance and have accuracy numbers associated with every single measurement they do. Medicine does the same thing—science, too. I have no idea why finance doesn’t do it.” Damian Handzy, Investor Analytics

A risk manager at a pension fund that oversees over $200 billion notes that it's challenging to analyze these securities because there is a lack of transparency and there is an absence of frequently-provided data.

"I think our peers are the same, but where we stumble on it is illiquids are different than public market securities. They price differently. They value differently. They transact differently," says the manager. "They have similar drivers to other public-market securities, but they have behavioral differences. Trying to address those in the absence of data is a challenge"

Over the last five-plus years, institutions have steadily increased allocations to the alternative asset management sector. This has led to increased regulatory scrutiny through mandates such as Form PF, Ucits and Solvency II. Additionally, asset owners are becoming more savvy and are demanding greater transparency into investment decisions, especially as hedge funds struggle to deliver returns.

Institutional investors have to play a game of catch-up in order to be on par with the hedge funds they are allocating to, or to even meet the standards of their sell-side intermediaries. This feature highlights three areas of focus for improving enterprise risk functions at institutional organizations: factor modeling, blending volatility and exposure calculations, and improving data management processes. 

State of the Alts 

While institutional investors are increasing their allocations to alternatives, it’s not as if 2015 offered a lot of positive headlines for the space, especially when it came to hedge funds.

According to institutional analytics information provider eVestment, estimated assets and investor flows for all hedge funds in 2015 were $44.6 billion—down from $88.3 billion in 2014—as credit and event-driven funds took a beating. Aggregate performance for hedge funds was -2.12 percent, while the S&P 500 Total Return was up 1.41 percent, according to the consultancy.

Still, eVestment estimates that in 2015, in-flows to hedge funds from institutional investors stood at $50 billion to $60 billion, and that it expects 2016 in-flows to meet or even exceed that range.  

It’s in the Numbers

In mid-January, BNY Mellon released a white paper, Considering the Alternatives: A Practical Look at Enterprise Risk Analysis and Alternative Investments, examining the needs of institutional investors as they allocate to alternatives. One of the key findings was that different approaches to data management throughout the firm can lead to different conclusions about the risks within an investment portfolio. 

Frances Barney, managing director of global risk solutions consulting for BNY Mellon’s clients in the Americas, tells Waters that managers can get tripped up when switching between risk calculations generated in-house and those that are produced by third parties.

“Some of the biggest challenges that we’ve seen are in the data management,” she says. “Depending upon the appetite that an institutional investor has for taking on the data management challenges themselves, or understanding the data management assumptions that their service providers are using in order to provide enterprise risk analysis, they may lead to misleading conclusions if they don’t understand the assumptions correctly.” 

For example, their systems might give them the ability to perform many different types of stress-tests, but if they don’t take the time to understand the assumptions that went into the incorporation of the alternatives in the resulting analysis, that can lead to an incorrect conclusion about the level of risk—or at least different conclusions—if they use different approaches to data management.

“It’s important for anybody who is considering enterprise risk analysis to take the time to understand the ways that data management can affect the results,” Barney says, adding that it’s also crucial to use the most granular detail available in order to evaluate investment risk.

Traditionally, institutional investors don’t have the same level of detail of risk analysis as a hedge fund or an alternative asset manager, which tend to have teams of quants analyzing the ins and outs of every swap, swaption or corporate bond they’re trading. This gap can be exacerbated when you consider that different desks trading different products often use different systems. Hence, aggregating that data can be a nightmare.  

Cobbling together these platforms can be incredibly costly, and for pension and mutual funds, such investments can prove problematic due to their public nature, especially during market downturns.

This makes drilling down into enterprise numbers in order to respond to sudden market changes more challenging, according to Lance Smith, CEO of Imagine Software, which provides risk analytics solutions as well as aggregated reports of position-level data from hedge funds that can be used by institutional investors, which traditionally do not have to that information. In order to gain an enterprise view of risk, some firms are pulling in data from numerous databases, many of which have different models and assumptions. Normalizing that information can be expensive and leaves the door open for breaks and manual errors. Smith says that institutions need to have firm-wide assumptions that are consistent. 

“A big problem is data,” Smith says. “You have different desks and in some cases they’re creating derivatives as part of a hedge or their investment strategy. There’s no way of ensuring that they’re using the same assumptions. How do you aggregate risk if the same security is being priced differently across different desks?”

A Matter of Factor

At its core, risk is a complicated domain that involves lots of intricate math, models and assumptions that are run in the background. As a result, factor modeling has taken on greater interest because it has the benefit of simplifying risk to a handful of easy-to-understand factors—when done correctly. 

Factor analysis started to take hold after the introduction of the Fama–French three-factor model, which posited that there were three factors for equity-only portfolios that account for 80 to 90 percent of the variability of risk in such portfolios, says Damian Handzy, founder and CEO of Investor Analytics, which was recently acquired by London-based buy-side risk management and performance vendor StatPro.

The beauty of Fama–French is that its creators, Eugene Fama and Kenneth French, showed that the “goodness-of-fit” for equity portfolios is very high for three factors: size, value and market risk. This was an extension on the Capital Asset Pricing Model (CAPM) and proved revolutionary in the late 1980s and early 1990s. 

The problem is that alternatives are more complex and do not fit neatly into three to four exact factors. Factor models for analyzing risk in alternatives can include five to seven factors—sometimes more—although those factors will change depending on the type of hedge fund, private equity or real-estate investment. What firms most often forget when analyzing alternatives, says Handzy, is the goodness-of-fit exercise. With alternatives, you can usually distill the portfolio into a list of five to seven risk factors, but the key is to find that right mix of factors. And those factors might change over time for derivatives, where they tend not to change for equities, he says.

“That three-to-four factor model tends to be stable for equity ETFs and mutual funds, where factor models in alternatives tend to be dynamic,” Handzy says. “The quality-of-fit of the model is paramount, and it’s something that’s ignored in this industry left and right. It’s high time it stops. Engineering does not do that. When they build a bridge, they measure the tolerance and have accuracy numbers associated with every single measurement they do. Medicine does the same thing—science, too. I have no idea why finance doesn’t do it.”

To help establish goodness-of-fit, Sebastián Ceria, CEO of risk software provider Axioma, says that for factor analysis, firms must be able to correlate those factors to the investment and to understand how they behave in certain markets. When done well, you can relate any security in the market to all the factors out there because you can map the price movement of the security to the movement of the factor, he says.

“You need to map the illiquid investments to these factors. By doing so, because you know how these factors behave on an intra-day or daily or weekly basis, you can calculate how an illiquid investment, like a private equity firm, will behave. Move everything to a common ground, so that when you analyze the risk of your portfolio, you can compare apples with apples, even though you’re holding very different kinds of securities,” Ceria says.

The head of portfolio strategy for a US-based asset manager with over $1.5 trillion under management, adds that firms are increasingly turning to third-party solutions to help manage risk because these tools have become vastly more sophisticated, even if they are off-the-shelf products. “These tools are becoming more democratized,” says the manager. “Now you can buy analytics software off-the-shelf and run sophisticated analyses to show how much return is coming from factors like value or momentum or whatever.”

Under-Exposed 

It might seem logical, but the savviest firms are those that can efficiently run and dissect both exposure and volatility measurements, rather than relying on one or the other. As to the aforementioned “getting a physical” analogy, true enterprise risk requires institutions to move beyond simple VaR and stress-tests. 

Users want to understand exposures to different factors, and to blend the two, adds Boryana Racheva-Iotova, president of risk analytics specialist FinAnalytica. What is my exposure to that factor, and what is my return-contribution from that factor? From there, they can make a more informed investment decision from those measures.

“Risk cannot be collapsed to a single number or two or three numbers,” she says. “You need a matrix of risk measures and different approaches to risk will reveal different aspects of the risk profile of a particular fund.”

And, as the BNY Mellon report notes, it’s important for firms to evaluate volatility-based measures as one element of a broader risk management framework that also includes exposure, correlations and stress-testing. 

“Five or 10 years ago, a single model that used the same buckets and assumed that those factors are relevant for every investor might have been appropriate, but more and more institutional investors are questioning the assumptions in the models and are wanting to make sure that the analysis is set up in a way that makes sense for their particular investment process,” says BNY’s Barney. 

Investor Analytics’ Handzy says that to monitor risk in a portfolio, you need a range of at least 10 different models, approaches and measures. You need betas to the right factors. You need correlations to the right factors. You need exposure analysis. You need Monte Carlo simulations. You need historical simulations. You need stress-testing. You need all of these and more. 

If you want to ensure your personal health, you get a thorough physical. If you want to ensure the health of your enterprise, you employ a thorough risk program. There aren’t short cuts when it comes to risk.

“I cannot underscore this more,” Handzy says. “You do not want one risk model, or two risk models, or three risk models for your entire portfolio. No risk model works all the time; you want multiple risk models. You need to understand when they’re good, when they’re believable, when they’re actionable, and when you have to second-guess them. That takes expertise and subtlety.” 

Salient Points

• Hedge funds continue to struggle to deliver positive returns—even compared to the S&P 500 Index—but institutional investors are still expected to allocate $50 billion to $60 billion in 2016 to these alternative structures, according to eVestment. 
   
• Cobbling together data from different desks can be incredibly costly and makes it challenging to drill into problematic enterprise numbers in order to respond to sudden market changes. A unified system is preferable. 
   
• Factor modeling has taken on greater interest because it can simplify risk down to a handful of factors. 
   
• Institutions need to evaluate volatility-based measures as one element of a broader risk management framework that also includes exposure, correlations and stress-testing.