The write-down of Credit Suisse’s contingent convertible bonds to zero following its takeover by UBS has left investors questioning just how much risk there is in these instruments. Assurances from regulators don’t seem to have calmed markets.

A corporate bond’s yield curve is similar to the well-known Treasury yield curve. Could the deviation of a corporate bond’s yield from the yield curve, or mispricing spread, be a useful signal for credit investors?

In the midst of the global banking crisis, looking at corporate-credit markets through a factor lens may provide a clue as to how much further the turmoil could spread.

Factor analysis of corporate credit spreads following the failure of Silicon Valley Bank shows the bond market has recognized both issuer-specific and systematic consequences.

By remaining steadfast in its commitment to zero yields, the Bank of Japan may have sacrificed the ability to control its own destiny. 

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.

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?

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.

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.

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.

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?

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...

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.

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.

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;...

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...

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...

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.

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...

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...

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.