Frances Barney | Managing Director, Global Risk Solutions Consulting – Americas
Andrew Lapkin | Chief Executive Officer, HedgeMark
Guy Holappa | Managing Director, Global Risk Solutions Consulting
Jim Nevola | Senior Risk Consultant, Global Risk Solutions
Derek van Vliet | Senior Risk Consultant, Global Risk Solutions
Enterprise risk analyses, such as stress testing and scenario analyses are increasingly popular with institutional investors. Alternative investments, such as hedge funds, private equity and real estate present challenges to enterprise risk analysis.
In this white paper, BNY Mellon’s Global Risk Solutions and HedgeMark Risk Analytics, a BNY Mellon company, explore these issues and offer examples of possible solutions.
Key Observations and Insights to Best Practices Include:
Risk management can mean different things to different people. Enterprise Risk can include all the many aspects of risk that affect an organization including but not limited to market risk, reputational risk, regulatory risk, compliance risk, operational risk, and legal risk.
Enterprise Risk Analysis can also mean forward-looking (“ex-ante”) risk calculations that estimate investment risks across multiple asset classes. This paper focuses on the definition of Enterprise Risk Analysis that estimates ex-ante risks of an investment program with multiple asset classes owned by a single organization, such as a pension plan or a charitable foundation. Ideally, this risk management discipline should be a component of a larger “enterprise-wide” risk management framework that also considers risks other than investment risk.
Over the past decade, institutional investors have become more aware of the importance of risk management across asset classes. Since the advent of modern portfolio theory, portfolio managers have understood risk to be important in selecting and monitoring investments within a single portfolio strategy. Evaluating risk across investment strategies has become common practice more recently. It used to be that reviewing a few risk measures based on the volatility of monthly return streams such as a Sharpe Ratio or Information Ratio would be considered an effective risk management process.
With the continuing increased focus on risk by regulators and other stakeholders, many institutional investors require a more comprehensive understanding of how risk operates across investments within an entire investment program. Some regulators require reporting pertaining to stress testing and scenario analysis, such as through Form PF for U.S. investment advisers to hedge funds, and pursuant to Solvency II for insurance companies and UCITS for European investment funds. These new risk reporting requirements have generated the need for sophisticated risk calculation tools and services to help address these changing requirements. Many firms are establishing a separate Chief Risk Officer function to supplement the risk management responsibilities inherent within the investment management functions. These new risk management functions require a significant amount of data to enable evaluation of investment risks across asset classes.
In considering investment risk, two common approaches to risk management are based on the definition of risk as return volatility or the definition of risk as exposures.
Exposure-based risk analysis identifies the relevant risk factors for each investment, enabling the preparation of summaries of portfolio allocations to various categories of exposures or factors, or investment characteristics.
Investment strategies or broad asset classes have characteristics that may be more relevant to one strategy than another. For example, duration is a key risk characteristic for fixed income investments but less relevant for common stock. Some characteristics can be relevant across asset classes. Credit ratings are often associated with bonds but may also be associated with the issuers of common stock. Economic sectors are most often associated with common stock, but corporate debt and private equity investments can also be associated with economic sectors.
With increased focus on risk by regulators and other stakeholders, many institutional investors require a more comprehensive understanding of how risk operates across investments within an entire investment program.
Currency or country exposure is relevant for most investments, and liquidity is also a consideration for all investments. Each of these measures provides insights into different aspects of investment risks, but the estimation and aggregation of these risks requires transparency into portfolios.
A covariance matrix enables an understanding of the volatility of the total fund relative to the correlations of each of the component asset classes and sub-strategies or investments. This approach enables an understanding of which investments contribute to the total risk of the fund and which investments help off-set some of the risks of other investments.
The covariance matrix is generally based on historical returns to estimate how investments move in relation to each other, so ex-ante risk is also based on ex-post returns. Often, risk systems generate covariance matrices based on several months or several years of daily returns of each of the specific securities. Some risk systems also calculate covariance matrices based on monthly returns of the investments.
Covariance matrices based on daily or monthly returns makes sense in evaluating the relative risk profile of publicly traded liquid investments that are priced frequently. Many institutional investors have been increasing their allocations to alternative investments such as private equity, real estate and hedge funds, which often do not provide sufficient liquidity or transparency to facilitate ex-ante analysis. Real estate and private equity investments are often priced quarterly, so the apparent risk of these investments based on daily volatility alone could underestimate the actual risk of losing money in the event an investor wanted to sell. The typical return profile of private equity includes an initial period of negative returns followed hopefully by a prolonged period of growth, resulting in an initial public offering and payouts to investors. This return profile is appropriately measured by long-term internal rate of returns rather than independent monthly or daily time-weighted rates of return. Private equity investments will often provide transparency into the underlying companies, but these companies are not valued frequently and detailed cash flow information generally is not provided for specific underlying companies. Real estate investments often provide transparency about underlying investments as well on a quarterly frequency. Hedge funds may be constructed with liquid or illiquid investments, but hedge fund managers often share limited information periodically with investors. Hedge fund managers that provide descriptions of exposures and characteristics in manager letters often define summary categories differently. For example, one manager’s definition of Europe might include the United Kingdom and all countries in the European continent, while another manager might define Europe to mean only those countries in the European Economic and Monetary Union. Investors with large allocations to alternatives often spend a significant amount of resources and time finding ways to normalize summary data so exposures can be aggregated across managers and asset classes.
Institutional investors that want to incorporate alternative investments into ex-ante risk analysis have to make some assumptions. These assumptions come with different levels of data management requirements that could lead to different levels of confidence about the reasonableness of the potential conclusions. Ex-ante risk calculations estimate the volatility of each investment’s return stream in absolute terms and relative to other investments and benchmarks.
Where detailed holdings are available, taking the time to incorporate the most detailed information enables the most flexibility in viewing different slices of data, and the most confidence in the accuracy of the calculations. This is particularly important with hedge funds or any portfolio containing derivatives, because derivatives may move in non-linear ways that can only be captured if the detailed terms and conditions of the derivatives have been modeled. Where hedge funds may not be willing to provide full transparency to investors, they may be willing to provide transparency to third-party risk aggregators using non-disclosure agreements. These risk aggregators can then provide calculated risk exposures using the underlying holdings to the investors without revealing the underlying holdings in detail.
Where detailed holdings are not available even to third party risk aggregators, managers will often provide summary exposures. Where detailed holdings are available but the investments are illiquid, or where summary exposures only are available, it is necessary to proxy these summary exposures with relevant indexes that represent relevant factors of interest to the investor.
Some investors with smaller allocations to alternative investments may choose to assume that an entire portfolio or even asset class is not worth the trouble of estimating detailed factor exposures. In this case, it is possible to proxy an investment or asset class to an index with various factor adjustments that is intended to mimic the performance of the asset.
How do these different choices affect the resulting risk analysis? Does detailed data management matter? To help evaluate these questions, we constructed sample portfolios using the most detailed data available. We next compared the apparent risk these detailed portfolios reveal when we repeated the analysis on the same portfolios using summary exposures or top level approximations.
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