Fears of a global slowdown have sent U.S. stock markets plummeting recently. Given FAANG stocks (Facebook, Apple, Amazon, Netflix and Google) have been a dominant force in driving U.S. market performance higher over the past few years, did these stocks lead the market’s downward trajectory?

Given high market valuations, some investors worry that a market pullback may be at hand. We saw markets gyrate earlier this year — what if volatility returns? How investors respond to changing market conditions may depend on their time horizons.

Heading into 2016, MSCI examined 12 stress points globally to be used in quantifying the effect on portfolios of a range of shifts in markets, liquidity and the macroeconomy. These stress points include the prospect of additional interest-rate hikes by the Federal Reserve, weakness in the eurozone and a deceleration in Chinese economic growth.

This Model Insight provides empirical results for the new Barra Emerging Markets Model, including detailed information on the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include backtesting results and a thorough side-by-side comparison of the forecasting accuracy of the new Emerging Markets Model and the Global Equity Model (GEM3).

This paper provides empirical results for the new Barra Europe Equity Model (EUE4), including details on factor structure, commentary on the performance of select factors, analysis of the explanatory power of the model, and an examination of the statistical significance of the factors. Furthermore, these notes include a side-by-side comparison of forecasting accuracy for EUE4 and EUE3.

The Barra North America Stochastic Model (NAMS1) applies the stochastic methodology framework to the US and Canada equity markets. These detailed Research Notes discuss an overview of the model specifications, offer insights into the model behavior and applications, and provide the results of extensive backtests from 1995-2012.

The Barra North America Stochastic Factor Model (NAMS1) is the second in a family of statistical factor models developed by MSCI, following the launch of the Barra Europe Stochastic Factor Model (EURS1) in September 2012. This overview document succinctly describes the NAMS1 specifications and provides the results of extensive backtests from 1995-2012.

Predicting risk at short horizons requires a delicate balance between two effects. On the one hand, it is best to give more weight to recent observations, as these contain the most relevant data; on the other hand, giving too much weight to recent observations can lead to increases in sampling error. In this paper, we study how to optimally balance these two effects through appropriate model calibration. Central to this challenge is the identification of a reliable measure of risk forecasting...

This document provides empirical results and analysis for the new Barra US Equity Daily Model (USE4D). These notes include extensive information on the structure, the performance, and the explanatory power of the factors. Furthermore, these notes also include a thorough side-by-side comparison of the forecasting accuracy of the USE4D Model and the USE4S Model, the short-horizon monthly version.

Cross-sectional volatility is given by the standard deviation of a set of asset returns over a single time period.  CSV is critical because it represents the opportunity to outperform a benchmark.  In this Research Insight, we present an exact methodology for decomposing CSV into contributions from individual factors.  Our approach treats countries, industries, and style factors on an equal basis.  We employ our framework to investigate several relevant questions in the...

In this paper we examine three approaches for capturing equity risk premia. In the 'simple' approach, the manager goes long stocks with positive exposure and shorts stocks with negative exposure, but makes no effort to control for other exposures or to minimize risk. In the 'pure' approach, the manager selectively retains only exposure to the desired factor, while hedging all other exposures.  In the 'optimized' approach, the manager constructs the minimum-risk portfolio with unit...

This report introduces the new multiple horizon versions of the Barra Canada Equity Model (CNE4) - Canada Equity Model Short Term (CNE4S) and Canada Equity Model Long Term(CNE4L). Both versions use daily returns data while accounting for serial correlations in aggregating daily factor returns to longer horizons. The new multiple horizon models provide more responsive risk forecasts than the existing model,CNE4. In addition to using higher frequency returns the new multiple horizon models also...

We compare the accuracy of risk forecasts from single-country models and GEM2 for portfolios concentrated in single countries.  We find that single-country models provide more accurate risk forecasts, consistent with intuition.  This demonstrates the importance of local factor structure for modeling intra-market risk.

This document contains research notes for three new Barra single country equity models: the Kuwait Equity Model (KWE1), the Qatar Equity Model (QAE1), and the United Arab Emirates (UAE) Equity Model (AEE1). Assets in these markets are included in the latest version of Barra Integrated Model (BIM207), via these new models. Along with our existing single country models for Bahrain, Oman, and Saudi Arabia, these new models complete our coverage of equities in the six Gulf Cooperation Council...

This document describes the new Barra Global Equity Model, GEM2, and provides an in-depth comparison with the model it replaces, the Global Equity Model (GEM). With GEM, originally released in 1989, Barra pioneered the use of factor models for the forecasting of global equity portfolio risk. In the nearly 20 years since the launch of GEM, changes have occurred that warrant the introduction of a completely new model. These changes fall into two categories.First, additional data sources are...