There are hundreds of companies offering risk-related services to the financial services community. Yet it is not clear that any of them are effectively addressing one of the most pressing needs facing the industry today: how to accurately measure investment risk in a multiasset-class portfolio. This challenge remains the Gordian Knot for risk professionals and, with interconnectivity and internationalization, the need to address this fundamental challenge is intensifying.
In the U.S., the Dodd-Frank Act inadvertently created industry blind spots by focusing on perceived risks to the overall financial system. This has diverted attention and resources from the actual needs of investors. Unfortunately, any changes made to the act will likely be incremental. In Europe there has been a greater focus on investment and portfolio risk through UCITS V, AIFMD, and Solvency II, and these at least offer some foundational best practices for all investors to consider.
The background to multiasset risk challenge is understandable. Investors, and the firms that have historically built technology solutions for them, have generally specialized in an asset class or in the case of hedge funds, a particular strategy.
Unfortunately, even in this post-crisis era there is still inadequate modeling of complex instruments held within larger portfolios, such as collateralized debt obligations, swaps and other derivatives. Many models are too simplistic in an era in which tail risk, fat tails and risk budgeting have all become mainstream topics. To compound the problem, with most of the attention on regulations, banks, issuers and ratings agencies, evaluating pre- and post-trade investment risk at the portfolio level is often glossed over.
Risk measures within single asset types, or tightly constructed portfolios without derivatives or highly structured instruments, have generally been well tested with mature risk/quantitative measures. Managers and investors in equities can be comfortable comparing the risk and return dynamics of a portfolio against an index.
The challenge comes as investments increase in diversity and the need arises to analyze them within a common framework. The absence of these building blocks leaves a number of important questions that still cannot be confidently answered: Should an investment be made in a new asset class, should allocations among asset classes be changed next quarter, and what risks, if any should be hedged?
It's clearly important to understand how disparate asset classes perform related to each other — generally described as correlation. Investors seek diversification to reduce overall portfolio losses in the event of a crisis or significant loss in one or more asset classes or investment allocations. Most risk management systems assume a fixed or constant correlation — even in the cases of significant shocks. This type of assumption can create a misleading environment for investors when the correlations between asset classes shift dramatically, such as in the last crisis.
Typical analytical systems have add-on capability and risk measures at an instrument and portfolio level, as well as by portfolio or even rolled up for multiple portfolios. But the underlying data needs to be transparent, timely, and available at same frequencies; otherwise, it is introducing other risks and will be much less effective.
We know boards and clients ask for measures to be twisted or extended, such as pushing forecast horizons out to a year and beyond, which creates low value at best and often misleading data points. Pre-crisis analytics were far too simplistic to explain the actual risk of instruments such as CDOs. We face similar dangers in approaches too often deployed to cover multiasset-class portfolios. Risk management is about creating questions but we should first start by questioning the efficacy of the measures themselves.
The reality is that many measures are non-constructive in a multiasset-class situation. This causes understandable frustration. It seems obvious, but if you do not have confidence in your ability to model an investment and prove it has merit both on a standalone and portfolio context, then it is usually best to pass. This is clearly one reason the California Public Employees' Retirement System exited hedge funds in 2014, for example.
The risks may be increasing as the investment landscape hovers on the brink of an abrupt change. The long-held bias towards earning reasonable if not stellar returns through investing in traditional fixed-income instruments is no longer a reality, and the returns enjoyed in the past will not continue.
The pressure on investors to replace maturing bonds will put even greater demands on risk management practices that can model new investment opportunities as well as their impact on overall portfolios. If analytical approaches and practices aren't leveraged with eyes wide open to answer this need, not only will business opportunities be lost, but many in the industry will be competing with one arm tied behind their back. And in a competitive world, that's not a winning strategy.
David Merrill is head of risk and portfolio analytics, InvestTech Systems Consulting.