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Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

Two Biggest Mistakes of Long-term Investors

How can long-term investors maximize their edge of strategic patience? In their November 2011 paper entitled “Investing for the Long Run”, Andrew Ang and Knut Kjaer offer advice on successful long-term investing (such as by pension funds).  They define a long-term investor as one having no material short-term liabilities or liquidity demands. Using the California Public Employee’s Retirement System and other large institutions as examples, they conclude that: Keep Reading

Translating Risk Strategies into Common Factors

Do somewhat abstract risk-based portfolio strategies translate to familiar stock/firm characteristic tilts? In their September 2011 paper entitled “Demystifying Equity Risk-Based Strategies: A Simple Alpha plus Beta Description”, Raul Leote de Carvalho, Xiao Lu and Pierre Moulin investigate how the following five risk-based equity allocation strategies relate to four common portfolio factors.

  1. Equal Weight – long only, same portfolio weight for each stock.
  2. Equal Risk Budget – long only, same portfolio weight times volatility for each stock.
  3. Equal Risk Contribution – long only, similar to equal-risk budget, but tilted toward stocks with low market correlations (low beta).
  4. Minimum Variance – long only or long-short, limited to stocks with the extreme volatilities and market correlations.
  5. Maximum Diversification – long only or long-short, limited to stocks with extreme market correlations.

They map these five strategies to market capitalization (size), book-to-market ratio (value), market beta and residual (idiosyncratic) volatility factors based on quarterly rebalancing for empirical tests. Using weekly total returns for the stocks in the MSCI World Index of developed countries and the one-month U.S. Treasury bill yield during January 1997 through December 2010, they find that: Keep Reading

Harvesting Equity Market Premiums

Should investors strategically diversify across widely known equity market anomalies? In the October 2011 version of his paper entitled “Strategic Allocation to Premiums in the Equity Market”, David Blitz investigates whether investors should treat anomaly portfolios (size, value, momentum and low-volatility) as diversifying asset classes and how they can implement such a strategy.  To ensure implementation is practicable, he focuses on long-only, big-cap portfolios. To account for the trading frictions associated with anomaly portfolio maintenance and for time variation of anomaly premiums, he assumes future (expected) market and anomaly premiums lower than historical values, as follows: 3% equity market premium; 0% expected incremental size and low-volatility premiums; and, 1% expected incremental value and momentum premiums. He assumes future volatilities, correlations and market betas as observed in historical data and constrains weights of all anomaly portfolios to a maximum 40%. He considers both equal-weighted and value-weighted individual anomaly portfolios, and both mean-variance optimized and equal-weighted combinations of market and anomaly portfolios. Using portfolios constructed by Kenneth French to quantify equity market/anomaly premiums during July 1963 through December 2009 (consisting of approximately 800 of largest, most liquid U.S. stocks), he finds that: Keep Reading

Asset Allocation Strategy Horse Race

Do sophisticated asset allocation strategies beat simple ones? In the December 2010 version of their paper entitled “Risk Parity Portfolio vs. Other Asset Allocation Heuristic Portfolios”, Denis Chaves, Jason Hsu Feifei Li and Omid Shakernia conduct a horse race among six asset allocation weighting strategies applied to nine asset classes:

  1. 60/40: 60% S&P 500 Index/40% Barclays Capital U.S. Aggregate Bond Index.
  2. U.S. Pension Model: 55% stocks (80% U.S. and 20% international); 35% bonds (60% U.S. Long Treasury, 20% investment-grade corporate and 20% global); and, 10% alternative investments (2.5% each commodities, REITs, emerging market equities and high-yield bonds).
  3. Equal: simple equal weight for all nine asset classes.
  4. Risk Parity: equal volatility contribution for all nine asset classes (asset classes contribute equally to expected portfolio fluctuations).
  5. Mean-Variancemean-variance optimization based on a five-year rolling return history.
  6. Minimum-Variance: like mean-variance optimization but using only variances and ignoring returns, based on a five-year rolling return history.

The nine asset classes proxies are: Barclay’s Capital U.S Long Treasury Index; Barclay’s Capital U.S. Investment Grade Corporate Bond Index; J.P. Morgan Global Goernment Bond Index; Barclay’s Capital U.S. High Yield Corporate Bond Index; S&P 500 Index; MSCI EAFE Index; MSCI Emerging Market Index; Dow Jones UBS Commodity Index; and, FTSE NAREIT US Real Estate Index. Using annual and monthly data as available for these indexes over the period January 1980 through June 2010, they find that: Keep Reading

Alternative Global Equity Diversification Approaches

What approaches to global diversification work best? In their July 2011 paper entitled “What Matters in International Equity Diversification? “, Chun-hung Chen, Tom Goodwin, and Wenling Lin use mean-variance spanning and optimization tests of indexes to compare benefits of alternative approaches to global diversification of the equity portion of an investor’s portfolio. Specifically, they investigate potential contributions to global diversification from: (1) American Depository Receipts (ADR); (2) multinational firms; (3) global versus country-specific delineation of large capitalization versus small capitalization; and, (4) non-U.S. developed small and micro capitalizations, emerging market small and micro capitalizations and frontier markets. Using monthly returns for the Russell 1000 and Russell 2000 indexes for a benchmark U.S. portfolio, and for various global and country indexes as available through February 2011, they find that: Keep Reading

Diversifying with Equity Volatility Exposure?

Can diversification via allocations to volatility-related securities enhance the absolute and risk-adjusted returns of equity portfolios? In other words, can investors construct useful asset classes from equity volatility? In their early 2010 paper entitled “Volatility Exposure for Strategic Asset Allocation”, Ombretta Signori, Marie Briere and Alexandre Burgues investigate potential benefits to long-term U.S. equity investors of including two volatility-related assets: (1) a rolling dynamic long position in VIX futures that is bigger when VIX is relatively low and smaller when it is relatively high; and, (2) a rolling short position in one-month variance swap contracts to exploit the tendency of option-implied volatility to exceed realized volatility (volatility risk premium). The former lowers the downside risk of holding equities, and the latter offers returns from selling “insurance” against volatility. Because the return distributions of such volatility investments are clearly non-normal, the authors employ a risk-return optimization approach that takes distribution skewness and kurtosis into account. Using S&P 500 Index, VIX, VIX futures and S&P 500 Index variance swap contract data as available over the period February 1990 through August 2008, they find that: Keep Reading

Overview of Research on Asset Allocation in the Face of Disaster

Has the academic community made practical progress in specifying asset allocation approaches that mitigate adverse impacts of multi-market crises (systemic risk) on diversified portfolios? Two recent papers address this question in complementary top-down and bottom-up ways. The February 2011 version of “Asset Allocation and Asset Pricing in the Face of Systemic Risk: A Literature Overview and Assessment” by Christoph Meinerding assesses recent research linking systemic risk with asset pricing and asset allocation, with systemic risk essentially characterized by the empirical properties of contagion. The 2011 paper “Fat-Tailed Models for Risk Estimation” by Stoyan Stoyanov, Svetlozar Rachev, Boryana Racheva-Iotova and Frank Fabozzi reviews mathematical approaches for modeling return distributions that match empirical data. Based on the relevant bodies of research, these papers conclude that: Keep Reading

Combining Tail Risk Management and Modern Portfolio Theory

Does combining avoidance of fat tail losses with a traditional portfolio optimization strategy enhance performance? In her January 2011 paper entitled “The Economic Value of Controlling for Large Losses in Portfolio Selection”, Alexandra Dias investigates the effectiveness of combining tail loss risk management with minimum variance efficiency. This approach essentially seeks to add avoidance of Black Swans to the benefit of diversification. The investigation consists of testing four long-only strategies using 224 months of rolling historical returns on all possible combinations of three Dow Jones Industrial Average (DJIA) stocks by choosing each month: (1) the minimum variance portfolio with the smallest variance (benchmark strategy); (2) the minimum variance portfolio with the smallest probability of a large loss; (3) the minimum variance portfolio with the thinnest losses tail; and, (4) the minimum Value at Risk (VaR) portfolio with the smallest VaR. Strategies (2), (3) and (4) are alternatives for managing return distribution tail risk. Using monthly returns for the 24 DJIA stocks for which which prices are available during February 1973 through June 2010 (allowing 2,024 combinations of three stocks), she finds that: Keep Reading

Feasibility of Diversifying Equities with Volatility Futures

Can investors straightforwardly diversify equity portfolios with volatility futures? In the January 2011 draft of their paper entitled “The Hazards of Volatility Diversification”, Carol Alexander and Dimitris Korovilas explore the potential benefits and costs of combining ‘buy-and-hold’ positions in volatility futures with a long-term equity portfolio. Specifically, they examine diversification of long exposure to the S&P 500 Index via S&P Depository Receipts (SPY) with a rolling long position in VIX futures. They distinguish “diversification” from “hedging” based on permanence of positions. They consider three VIX futures strategies using one-month-to-expiration, three-month-to-expiration or longest-maturity-available series, with rollover either at or five business days before expiration. Using daily trading data for VIX futures, SPY and the 1-month Treasury bills from March 26, 2004 through December 31, 2010 (about 69 months), they find that: Keep Reading

Best Global Equity Diversification Approach?

What approach to diversifying equity holdings across industries and global geographies is most sensible? In the October 2010 version of his paper entitled “Assessing Alternative Global Equity Investment Frameworks”, Xi Li compares the feasibility and optimality of eight possible approaches for grouping of stocks by geography and industry/sector. He compares feasibility of diversification grouping approaches based on a requirement that each group has at least 15 stocks, such that groups exhibit reasonably reliable behavior. He compares optimality of  diversification grouping approaches based on ability to explain the variation in returns across groups (effectiveness in grouping similar stocks). Using monthly returns for mostly large and medium capitalization stocks of 23 developed country markets assigned to three regions (Americas, Asia Pacific and Europe), 10 sectors and 24 industries over the period July 1989 through March 2010 fewer, he finds that: Keep Reading

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