Michael Hayes leads Model Validation and Best Practices Research, a team dedicated to developing the most effective modeling approaches for the full spectrum of clients’ investment problems. In addition, the team defines standards for model validation for all MSCI analytics and produces validation content to help inform clients’ modeling decisions. Previously, Michael held roles in quantitative portfolio management, multi-asset-class risk management and regulatory solutions. He holds a Bachelor of Arts in chemistry from Princeton University and a doctorate in chemical physics from the University of Colorado Boulder.
Research and Insights
Articles by Michael Hayes
Looking Beyond Japan to Understand Risks to the Yen7 mins read Blog | Sep 15, 2022 |
By remaining steadfast in its commitment to zero yields, the Bank of Japan may have sacrificed the ability to control its own destiny.
Are Russian Stocks Worthless?3 mins read Blog | May 26, 2022 |
Investors in Russian securities have faced sizable hurdles trying to manage and value their positions. Our research of the CDS market suggests that Russian stocks are essentially worthless, in contrast to the prices listed on the Moscow Exchange.
Assessing Private Infrastructure in a Multi-Asset-Class Portfolio4 mins read Blog | Aug 4, 2021 |
Private infrastructure is a popular element of institutional capital allocations, and increased focus on renewable or carbon-neutral infrastructure may mean significant new investment opportunities. What role could it play in a multi-asset-class portfolio?
Cross-Currency Credit Spreads: Mind the Gap5 mins read Blog | Feb 8, 2021 |
An issuer’s credit spread should be consistent when measured in the USD- or EUR-denominated markets, because both are measuring the same credit risk. Yet divergence can occur as a result of liquidity or supply-demand imbalances, such as those in the COVID crisis.
Using Risk Analytics to Highlight Opportunities in Volatile MarketsBlog | May 18, 2020 |
Risk analytics can serve many functions for an institutional investor, including compliance, risk management, portfolio management and trading and strategy development. They may also highlight new opportunities that may be unique to volatile markets.
Managing risk-model uncertainty through a crisisBlog | Mar 27, 2020 |
The impact on risk policy has been similar across market crises, as investors consider how to use their models in the new regime. We describe adaptive modeling for internal and external risk policy, and long-view backtesting to support decision-making.
Repo-market turmoil may not spell SOFR’s endBlog | Oct 24, 2019 |
Investors and the media have begun to worry that the secured overnight financing rate (SOFR) — the U.S. interest-rate benchmark meant to address issues with and replace USD LIBOR — may introduce a new set of problems. Are the concerns justified?
MSCI Multi-Asset Class (MAC) Factor Model ValidationResearch Report | Aug 20, 2019 |
The MSCI Multi-Asset Class (MAC) Factor Model introduces several major advances in risk modeling, including systematic MAC strategy factors, a next-generation fixed income model, and improved equity models. This document demonstrates the value of the MSCI MAC Factor Model in forecasting risk, based on (1) visual inspection of the risk forecasts and realized returns, and (2) statistical tests. The MSCI MAC Factor Model is evaluated on an absolute (stand-alone) basis, and is also compared with...
CDS Hedging: Exploring all the OptionsBlog | Jan 23, 2019 |
The credit-default-swap (CDS) market previously offered a cost-effective means to make short-term hedges or place bets on an individual issuer’s credit.
Is the bond-equity hedge slipping away?Blog | Nov 1, 2018 |
In October, the 10-year U.S. Treasury yield hit a 7-year high in response to strong economic news, contributing to the second major equity sell-off this year.1 If positive moves in yield continue to drive down equities, this would mean an end to the hedge between stocks and bonds that has been in effect since around 2002. Investors may seek alternative means of diversification, with potentially deep ramifications for strategic asset allocation decisions and multi-asset class strategies.
Best Practices for Predictive Stress Tests in RiskManager and BarraOneResearch Report | Apr 17, 2013 |
A predictive stress test based on historical data can be a valuable tool in contemplating large market shocks. When the degree of extrapolation becomes excessive, or the prediction is based on a tenuous historical correlation, the predictive stress test can lead to unintuitive results that do not conform to reasonable market and economic expectations. This Research Insight offers best practices for users of RiskManager and BarraOne who want to design stress tests with correlated factors;...
Minimizing ShortfallResearch Report | Jan 26, 2011 |
This paper describes an empirical study of shortfall optimization with Barra Extreme Risk. We compare minimum shortfall to minimum variance portfolios in the US, UK, and Japanese equity markets using Barra Style Factors (Value, Growth, Momentum, etc.). We show that minimizing shortfall generally improves performance over minimizing variance, especially during down-markets, over the period 1985-2010. The outperformance of shortfall is due to intuitive tilts towards protective factors like...
Extreme Risk AnalysisResearch Report | Jul 27, 2010 |
Risk analysis involves gaining deeper insight into the sources of risk, and evaluating whether these risks accurately reflect the views of the portfolio manager. In this paper, we show how to extend standard volatility analytics to shortfall, a measure of extreme risk. Using two examples, we show how shortfall provides a more complete and intuitive picture of risk than value at risk. In two subsequent examples we illustrate the additional perspective offered by analyzing shortfall and...
How Well Can the Risk of Financial Extremes Be Forecast?Research Report | Apr 13, 2010 |
Extreme events are an important source of financial risk, but they present special challenges in quantitative forecasting. In this paper we describe an empirical approach to forecasting extreme risk and evaluate its accuracy out-of-sample on a range of factor-based strategies and pair trades. Our results show that for a large majority of strategies, our model is more consistent with market behavior than a conditional Normal model.
The Long View of Financial RiskResearch Report | Jan 4, 2010 |
An extended history of market returns reveals aspects of financial risk that are not evident over short timescales. The most enduring risk measure is variance, which quantifies short-term regularities in return dispersion. An alternative measure, shortfall, quantifies the risk of extreme market moves, and calls for a deep history to inform its forecasts. Both variance and shortfall are convex, meaning that they tend to promote diversification and can be used in optimization. By offering a...
Extreme Risk ManagementResearch Report | Feb 1, 2009 |
Quantitative risk management relies on a constellation of tools that are used to analyze portfolio risk. We develop the standard toolkit, which includes betas, risk budgets and correlations, in a general, coherent, mnemonic framework centered around marginal risk contributions. We apply these tools to generate side-by-side analyses of volatility and expected shortfall, which is a measure of average portfolio excess of value-at-risk. We focus on two examples whose importance is highlighted by...
Evaluating Risk Forecasts with Central LimitsResearch Report | Mar 1, 2008 |
We show that for a diverse collection of 74 US equity portfolios, risk forecasts based on an extreme value theory model greatly outperform a conditional normal model with a 23-day halflife.