Multi-Asset Class

This paper studies a portfolio-selection problem subject to a cardinality constraint, that is, the number of securities in a portfolio is restricted to a certain limit. The problem is formulated as a cardinality-constrained quadratic programming problem, and a dedicated Lagrangian relaxation method is developed. In contrast to many existing Lagrangian relaxation methods, the approach presented in the paper is able to take advantage of the special structure of the objective function rather than the special structure of the constraints. The algorithm developed here has been applied to track the major market indices, such as the S&P 500, S&P 100, FTSE 100, and FTSE 250, using real data, and the computational results are promising.

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Publication: Optimization Methods and Software
Authors: KOPMAN Leonid, LIU Scott, SHAW Dong

In this paper, a framework is developed to think through the interpretation of an important piece of the new Basel guidelines for setting trading book capital. This piece – the Incremental Risk Charge (IRC) – is intended to cover default and migration risks in the trading book. Any bank that already has approval to use their internal models to set VaR-based trading book capital will be required to come up with an IRC model by the end of 2011. An important aspect of the IRC framework is the provision that positions are rebalanced over the annual horizon so as to maintain a Constant Level of Risk. Much of the model development comes down to how to interpret and implement this provision. I discuss three distinct mechanisms by which rebalancing impacts risk, and present the ideas of how our modeling framework captures each of these. I then perform a benchmarking exercise, applying our model to a number of representative portfolios in order to assess the impact of each of the rebalancing mechanisms. Finally, I compare the results from our model to the capital from the Basel banking book model (the IRB), as well as another IRC model from the literature. This exercise can serve as a template for how we could use our model (and product) to benchmark client or prospect portfolios.

Publication: MSCI Research Insight
Authors: FINGER, Christopher

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 volatility in tandem.

Publication: The Journal of Performance Measurement
Authors: GOLDBERG Lisa, HAYES Michael, MENCHERO Jose, MITRA Indrajit

This paper examines the recent trend of adding leverage to fixed income allocations of multi-asset class portfolios of large asset owners. We show that the optimality of adding leverage from a volatility-reduction perspective depends on the correlations between bonds and equities, the relative volatility of bonds versus equities, and the weights of the two asset classes in the portfolio. If correlations between bonds and equities are negative, adding leverage could reduce the volatility of a portfolio, especially if the weight in fixed income assets is low, leverage is moderate, and bonds have a low risk relative to equities. Negative correlations also increase the likelihood that adding leverage will improve the risk-return profile of the portfolio. Asset owners considering adding leverage to their fixed income allocation can examine these influences to decide whether negative correlations between bonds and equities, a low ratio of bond to equity volatility, and higher risk-adjusted returns of bonds relative to equities are likely to persist.

Publication: MSCI Barra Research Insight
Authors: MELAS Dimitris, RUBAN Oleg

Brinson sector-based attribution explains active return in terms of intuitive allocation and selection decisions. However, it cannot easily disentangle competing industry and style effects. We introduce a special type of factor model with five defining characteristics that exactly replicates the classic Brinson model. We show that this “Brinson-replicating” factor model easily extends to explain more general types of investment processes. In this extension, returns are decomposed into style effects and pure industry effects, net of styles. Moreover, much of the classic Brinson model “stock selection effect” is attributed to contributions from a handful of style factors. We show that in this framework risk and return can be attributed to the same set of decision variables. This provides a means of comparing return contributions on a risk-adjusted basis.

Publication: MSCI Barra Research Insight
Authors: DAVIS Ben, MENCHERO Jose

In this paper, we examine the influences of risk aversion on various aspects of portfolio optimization.  Our main message is that the risk aversion parameters in the Barra Optimizer provide users with the flexibility to control or adjust the risk levels of their optimal portfolios.  They are valuable tools for portfolio managers to explore and customize their portfolio optimization results and investment processes.

Publication: MSCI Barra Research Insight, February 2010
Authors: LIU Scott, XU Rong

Japan’s domestic bonds constitute one of the major asset classes for Japanese pension plan sponsors. In this Research Bulletin, we use the Barra Integrated Model (BIM) to examine the evolution of this market and better understand the relationship between bonds and equities in Japan.

Publication: MSCI Barra Resarch Bulletin
Authors: CHIA Chin-Ping, OWYONG David, SHIMIZU Hidenori

As an alternative to mean-variance portfolio optimization, Barra Optimizer offers users an option to run risk target optimization. Instead of risk being controlled implicitly with the risk aversion parameters, the risk target is explicitly specified by the user. When the risk target is achievable and efficient, the optimized portfolio will have risk (or tracking error) equal to the specified target. The risk target may be too low due to the problem constraints; it may also be too high, that is, not achievable due to the constraints; or it may not be efficient due to the transaction costs and/or asset returns.

Publication: MSCI Barra Research Insights
Authors: KOPMAN Leonid, LIU Scott

Catastrophic events can lead to change in established practices. This paper argues that organizations may get a competitive advantage by applying practices used in Business Continuity Planning (BCP) to the management of institutional portfolios. We propose a framework to prepare for extreme market events and mitigate their impact. This paper describes the required elements of BCP as applied to portfolios, reviews the tools and processes necessary to monitor the level of stress in financial markets, and tests the effectiveness of a number of mitigating strategies.

Publication: MSCI Barra Research Insight
Authors: BRIAND Remy, OWYONG David

The Barra Global Equity Model (GEM2) introduced Volatility, a new factor that provides managers with a tool that enables close control of a portfolios' exposure to global beta. GEM2's Volatility factor includes global beta as its most significant descriptor. In contrast its predecessor, GEM, provided much less control, i.e., only through exposures to country factors. In this Research Bulletin, we backtest a high global beta portfolio using both GEM and GEM2, and we use performance attribution to demonstrate the advantages of using GEM2

Publication: MSCI Barra Research Bulletin
Authors: MSCI Barra Applied Research

Factor models are standards in investment management. For decades, Barra factor models have provided valuable risk forecasts and inputs for the portfolio construction process. Most uses of factor models have targeted longer horizons of months or years. However, we demonstrate in this paper that factor models can also provide accurate risk forecasts for shorter horizons of one to ten days. Furthermore, factor models have the advantage of explaining risk sources and providing consistency in risk management processes across all time horizons.We present a factor model with a methodology appropriately tailored to shorter horizons. Our basic approach is to retain the same common risk factors currently used in the Barra Integrated Model (BIM) and adopt a number of techniques that exploit daily data. As we show for different asset classes, markets, and sectors, this factor model approach yields similarly accurate shorter horizon risk forecasts compared to asset-by-asset approaches.

Publication: MSCI Barra Model Insight
Authors: BARBIERI Angelo, CHANG Kelly, DUBIKOVSKY Vladislav, Fox John

After the market turmoil of the last two years, many institutional investors are revisiting the way they approach asset allocation. For decades, the traditional breakdown of asset classes has been along the lines of equities, fixed income, alternatives, etc., sometimes with domestic versus foreign flavors. The main point we highlight in this Research Bulletin is that many asset classes share the same underlying drivers. While this notion is a familiar one, the insights gained by analyzing common drivers of risk and return across asset classes are not always applied to decisions about asset allocation.  For example, private equity investments share key fundamental drivers with public equities yet are often perceived as a separate and distinct asset class.  Or in the case of corporate bonds, the credit worthiness of the bonds depends on the financial health of the issuing corporation, which in turn is linked to the performance of the company's equity securities.  These types of underlying linkages can be addressed by factor-based models and can be used in factor-based asset allocation schemes.

Publication: MSCI Barra Research Bulletins
Authors: MSCI Barra Applied Research

Traditional models in finance rely heavily on the use of normal (Gaussian) distribution.  Using examples of asset, factor and index returns, we illustrate that the assumption of normality does not capture the empirical properties of returns and volatility alone cannot be relied on as a measure of portfolio risk.  We outline how extreme value theory can help to model the tails of the return distribution and, using data from 1996 to 2007, show how Barra Extreme Risk can improve estimates of Value at Risk for a collection of factor-tilted portfolios.

Publication: MSCI Barra Research Bulletins
Authors: MSCI Barra Applied Research

Publication: MSCI Barra Yearbook
Authors: MSCI Barra Research

Publication: The Journal of Investment Management
Authors: GOLDBERG Lisa

Portfolio analysts often use one set of decision variables for attributing portfolio returns and a different set for attributing risk. This practice obscures the relationship between the sources of risk and return. This article demonstrates how to align the attribution model with the investment process. The attribution methodology can be applied ex ante or ex post. A factor-based investment process illustrates the general framework. Specifically, active return, tracking error, and the information ratio are attributed to a user-defined set of factors that reflect the manager's investment decision-making process. A concrete example with actual market data, a style portfolio, and a parsimonious set of custom factors illustrates how to apply the analysis.

Publication: Financial Analysts Journal
Authors: MENCHERO Jose, PODURI Vijay

Publication: MSCI Barra Research Bulletins
Authors: MSCI Barra Applied Research

The annual yearbook delivers a comprehensive summary of the performance of the MSCI Indices. This year's issue begins with a review of asset classes as viewed through the lens of the Barra Integrated Model.  A valuable guide for institutional investors including plan sponsors and endowment funds.

Publication: MSCI Barra Yearbook
Authors: MSCI Barra

This case study provides an introduction to modeling assets and liabilities for asset owners within BarraOne. We show how to use BarraOne to analyze both assets and liabilities in a shared framework for understanding risk and return. Our example uses zero coupon bond instruments to proxy for liabilities although other proxies may be easily substituted. We illustrate how to analyze the surplus risk of the portfolio focusing on market risk specifically. Other sources of risk—nonmarket or biometric risk—are not addressed here but are discussed.

Publication: MSCI Barra Model Insights
Authors: MSCI Barra

The Barra Integrated Model (BIM) is a multi-asset class model for forecasting the asset and portfolio level risk of global equities, bonds, currencies, and commodities. The model uses innovative methods to couple broad asset coverage with the detailed analysis of Barra's models that focus on particular markets. This makes it suitable for a wide range of investment purposes, from conducting an in-depth analysis of a single-country portfolio to understanding the risk profile of a broad set of international investments.

Publication: Research Notes
Authors: HEMMATI Fati, HSIEH Agnes, PUCHKOV Anton V, STEFEK Dan

The Barra Integrated Model is a global-multi-asset class model that forecasts asset and portfolio level risk for global equities, bonds and currencies.  Using innovative methods to couple broad asset coverage with detailed local market models, BIM provides in-depth analyses for single-country and global portfolios across asset classes.  The Barra Integrated Model for equities is a major innovation that brings together more than 40 customized single market models in a unified global framework.  Each locally estimated model captures the unique industries, style influences and idiosyncratic nature of that market.  BIM, the union of these detailed single market models, provides a comprehensive global analysis that incorporates these purely local sources of risk and return.  In contrast, classical global equity models reflect local influences largely through a single local market or country factor.  There are a number of ways in which BIM can enhance the investment process and portfolio results.  First, the bottom-up construction and local depth provided by BIM results in significantly more accurate estimates of tracking error and its decomposition.  This additional level of granularity allows for precise control over portfolio construction and diversification, and allows portfolio managers to deliver more consistent results.  Second, BIM's unified framework allows regional portfolio managers, risk officers and chief investment offers to rely on a single model for portfolio contruction, risk management and enterprise risk analysis throughout the organization, yielding a common language and framework invaluable for global asset management firms.  Finally, the many local factors identified in the Barra Integrated Model reveal new sources of return that can be exploited in active investment strategies to ehance risk-adjusted returns.

Publication: MSCI Barra Newsletter
Authors: SENECHAL Edouard, SOMERVILLE Sara

This case study focuses on some of the features that make BarraOne an innovation in risk management technology. We will demonstrate that, with the power of BarraOne, traditional (Barra) risk reporting and flexible reporting can be combined to ensure that sources of portfolio risk are completely consistent with the investment strategy, effectively allocating the risk budget.

Publication:
Authors: THILGES Brad

The first article in this series explained the first principles behind the Brinson model. It also showed how simple it is to calculate exact multi-period attributes at the total level using those first principles. In the second article, we looked at different ways to measure the value added by stock selection. This article is about how to measure the value added by asset allocation and currency allocation. It is based on the example on pages 21-40 of Karnosky and Singer (1994). The Karnosky Singer spreadsheet shows the data and equations for this example. The example has been edited so that it adopts the perspective of an Australian investor, rather than a US investor. The numbers, however, remain unchanged. In particular, the example was constructed before adoption of the Euro, so the example still uses DEM as the currency for Germany.

Publication:
Authors: LAKER Damien

Risk management for a financial enterprise requires both the aggregation of positions across asset classes and the understanding of risks inherent in those positions. Thie is by no means a trivial task for modern organizatins, which may control many hundreds of thousands of positions invested in a variety of instruments traded across the globe. In order to obtain a solution, smplifications have typically been made both at the level of aggregation and risk analysis. For example, assets may be "grouped" according to their similarity, with a single group of assets being treated as homogeneous. While these simplifications may be unobjectionable for some purposes, certain applications demand a more sophisticated approach. Fortunately, the technology supporting this latter set of applications has evolved to a level where shortcuts in modeling the decision are no longer necessary. This article focuses on the business role of enterprise-wide risk analysis, where consistency is desirable between the analysis a the single portfolio level and the aggregated enterprise level. The clear implication is that the technology applicable to a single portfolio must be implemented enterprise-wide.

Publication:
Authors: RUDD Andrew, SHEIKH Aamir

Data mining is easy. To understand why data mining is easy, we must first understand the statistics of coincidence. Let's begin with some non-investment examples. Then we will move on to investment research.

Publication:
Authors: KAHN Ronald N.

Every promising investment strategy on paper loses value on                           implementation. Insight, especially into the relationship between transaction costs and value added, can help minimize this loss.

Publication:
Authors: KAHN Ronald N.

Investment managers undertaking TAA model-building have the choice of a wide range of statistical software packages to aid their research efforts. The new Altis System has at least two advantages over standard statistical software: Altis links the estimation procedure to the database and portfolio optimization capabilities of the World Markets Model; and it is explicitly constructed to facilitate out-of-sample testing. This allows easy application of important reliability checks. In this article, we show how this facility of Altis allows the investment manager to avoid serious in-sample biases which might otherwise seriously degrade the reliability of TAA models.

Publication:
Authors: CHAUMETON Lucie, CONNOR Gregory

Insight Four: In the previous issue we learned that information ratios, as the key to active management, depend on both skill and breadth. In this issue we will learn the constituent parts of alphas, the fundamental inputs for active management.

Publication:
Authors: KAHN Ronald N.

Several issues arise when analyzing plan-wide risk. What is the appropriate plan benchmark? Dies it consist of a set of investable indices? How does it reflect the liabilities that are the true underlying target for the plan? More general still, how should we define risk? Should we use value at risk or standard deviation? Should we define risk relative to the benchmark? Even after choosing a particular risk definition, we must identify all the factors which drive risk. Finally, should our forecast of risk use simply historical returns data or specifically analyze the current plan holdings? In this paper we will discuss these issues and provide for concreteness a detailed case study.

Publication: RogersCasey Research Insights
Authors: CESARE Christopher J., DEMAKIS Drew W., KAHN Ronald N.

In 1994 many mutual fund investors experienced staggering and unexpected losses. Amid the resulting public outcry, the U.S. Securities and Exchange Commission considered how to better inform the public of investment risk, and asked for public commentary. The response, from many thousands of individual investors, far surpassed the SEC's expectations. The public has considerable interest in mutual fund risk. As the leading provider of risk models and analytics to the institutional investment community, Barra responded with two reports to the SEC. The first, 'Quantitative Measures of Mutual Fund Risk: An Overview,' outlined the basic issues and choices as informed by our twenty years of modeling risk. The second report, 'Forecasting Mutual Fund Risk: Current Holdings or Past Performance?' followed up with a study comparing methods for forecasting mutual fund risk. This Barra Research Insights document collects these two reports. While the topic specifically focuses on mutual fund risk, we believe the issues and conclusions will interest our institutional clients.

Publication:
Authors: KAHN Ronald N.

Information ratios determine value added.

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Authors: KAHN Ronald N.

Macroeconomic risk analysis can provide an intuitive framework for equity portfolios. Traditionally, investors and researchers have considered the macroeconomic approach, the fundamental approach, and the statistical approach to risk modeling to be mutually exclusive. The conventional wisdom is that if you want the forecasting accuracy of the fundamental approach, you cannot also have macroeconomic intuition. This is incorrect. Barra is now developing analytics to provide macroeconomic analysis consistent with our accurate fundamental models of risk. Our fundamental factors completely  "include" the macrofactors, which we can extract. This approach extends to a natural framework for asset/liability management and opens the door to enterprise-wide risk modeling.

Publication:
Authors: KAHN Ronald N.

Investment opportunities overseas continue to attract the interest of money managers in the U.S., despite the staggering drop of equity markets in developed and emerging countries  earlier this year. Portfolios that once held a few select blue chip stocks in Europe and Japan are now holding increasing portions in regions such as Southeast Asia and South America as well as in a variety of stocks within developed markets. And the current trend of overseas investment has led many index providers to enhance and expand their global indices.  Barra conducted a survey of U.S. equity money managers to determine new trends in international investing. We asked 23 managers to describe their level of activity and general strategy in overseas markets. Here's what they said.

Publication:
Authors: BERTOLOTTI Andre

Which mutual funds will be next year's winners? Conventional wisdom in the investment community says that to predict future performance, look at past performance. But does it help to know last year's? Do winners repeat? The idea that winners repeat is so obvious and popular, it has spawned an entire mini-industry devoted to documenting past winners. Mutual fund performance reviews regularly appear in publications like Barron's, Business Week, and Consumer Reports.  Services such as Morningstar and Lipper exist to publish mutual fund rankings.  Pension plan consultants closely examine past performance before recommending managers, and successful managers proudly document their past performance. All this suggests that everyone choosing active managers, from pension plan sponsors to  individual investors, thinks past performance predicts future performance. Is this true?  In this article, we will review past investigations into this question, and then present new results looking at performance of active equity and fixed income managers over the past decade. (1)

Publication:
Authors: KAHN Ronald N., RUDD Andrew

An investor who wishes to purchase securities that reflect a 'socially responsible' investment choice, can select from a universe of securities that have been screened according to certain ethical criteria. These constraints may exclude companies in industries regarded as undesirable, or "sinful." But do such exclusions lead to potential underperformance relative to the market? We examined this question with respect to companies in the gambling, liquor and tobacco industries from January, 1980 through December, 1993 as a follow-up to a study published previously in the Barra Newsletter.(1)

Publication:
Authors: LUCK Christopher, TIGRANI Vida

Barra, in collaboration with Standard and Poor's Corporation, has constructed the S&P/Barra Growth Index and S&P/Barra Value Index to separate the S&P 500 into value stocks and growth stocks. The design of the indexes is an outgrowth of research into investment styles in the U.S. equity market performed in conjunction with 1990 Nobel Laureate William F. Sharpe. The indexes serve as benchmarks for institutional investors who wish to more closely track these two dominant investment styles in the market.

Publication:
Authors: BUCKLEY Oliver

Barra has traditionally seen the market place as multifaceted, and we have captured  those facets of the market through factors such as SIZE, VOLATILITY, SUCCESS, VALUE, GROWTH, and the like. The opposite viewpoint is the old, one-dimensional separation of stocks into large and small. Style indices represent something of a midpoint between Barra's multifaceted view of the market and the one-dimensional  view. To create style indices we look along the axis of value and growth and end up with a matrix stratification into categories such as large-value, large-growth, small-value, small-growth, and so on.  Style indices are similar to factors in that it is possible to define a style portfolio as a  list of stock holdings. Style index portfolios have the added benefit of being a capitalization-weighted list of assets; that is, the critical item in the style portfolio is membership in the list; weighting is always in proportion to capitalization. Every asset in the market gets included in only one of the style portfolios, so we can consider the  style portfolios as pieces of a mosaic; put all the pieces together and you get the market.!

Publication:
Authors: DIVECHA Arjun, GRINOLD Richard

Market-neutral hedge funds are becoming increasingly popular in the institutional investment world; managers apparently find the advantages more compelling than the disadvantages. The major advantage to these funds is their flexibility, which can magnify the manager's alpha. How do these hedge funds work?

Publication:
Authors: GOTTIPALLI Praveen

It is common practice these days for large pension funds to allocate assets among multiple investment managers who specialize in investment areas. It is the sponsor's job to combine these specialized managers into an aggregate pool that is consistent with the plan's overall objectives, both for the entire fund and for each asset class. Plan objectives are often stated in terms of a target portfolio. Even when assets are allocated among multiple investment managers, matching the target is not an easy task. We will examine the problems of matching the target and present potential ways of addressing these problems in this asset reallocation case study for the fund sponsor.

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Authors: NEWHOUSE Beth

In some respects, the most difficult part of passive management comes before any money is actually managed. Choosing which index to track from the many available has significant effects on the running and performance of an index fund. In this article, we argue that while the S&P 500 is clearly the most popular index and undoubtedly the easiest to track, other indices are superior for many investment objectives.

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Authors: Source: Barra Newsletter

The material in this article is taken from a presentation at the annual research seminar at Pebble Beach, June 4-8, 1989. Covariance is a measure of the linear relation between two random variables. On average, if the monthly returns in 1988 from holding IBM stock were positive (negative) when the returns from holding Southwest Gas were negative (positive), then the returns on these two stocks are said to exhibit negative covariance. What is the appropriate frequency to measure covariance? Is it reasonable to assume that covariance is constant over time? Is the covariance between two stock returns for the next month predictable?

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Authors: Source: Barra Newsletter

If you have two or more forecasts of asset return, what is the best way for you to make use of the information? In our analysis of combined forecasts, we analyze forecasts by turning them into portfolios to produce a combined forecast with the highest possible information ratio.

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Authors: Source: Barra Newsletter

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Authors: Source: Barra Newsletter