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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.

Assessment of Risk Parity Asset Allocation

How does the risk parity asset allocation strategy (equalizing the volatility contributions of portfolio components) fare in comparison to other commonly used strategies? In their March 2012 research note entitled “The Risk Parity Approach to Asset Allocation – Climbing the Wall of Worries?”, Fabian Dori, Frank Haeusler, Manuel Krieger, Urs Schubiger and David Stefanovits contrast three popular allocation strategies: (1) traditional balanced (40% equities, 50% bonds and 10% commodities); (2) minimum variance (the mean-variance optimized portfolio with the lowest variance); and, (3) risk parity. Their asset universe includes equity index futures (FTSE 100, DAX, S&P 500, TOPIX and ASX SPI 200), 10-year government bond futures (UK, German, U.S., Japanese and Australian) and commodity index futures (GSCI agriculture, energy, industrial and precious metals and softs). For each asset allocation strategy, they model daily rebalancing of assets to specified weights. Using daily futures return data during August 1992 through June 2012, they find that: Keep Reading

Common Factor Exposures of Specialized Stock Indexes

How do specialized stock indexes relate to commonly used equity risk factors? In his February 2012 paper entitled “Evaluating Alternative Beta Strategies”, Xiaowei Kang examines risk exposures (betas), construction methodologies and historical performances of alternative stock indexes such as those based on value, low-volatility and diversification strategies. He considers five risk factors: (1) market, representing excess return of the market capitalization-weighted U.S. stock market; (2) size, representing return from a portfolio that is long small-cap stocks and short large-cap stocks; (3) value, representing return from a portfolio that is long high book-to-market stocks and short low book-to-market stocks; (4) momentum, representing return from a portfolio that is long past winning stocks and short past losing stocks; and, (5) volatility, representing return from a portfolio that is long high-volatility stocks and short low-volatility stocks. Using monthly returns for several specialized indexes and the specified risk factors as available through 2011, he finds that: Keep Reading

Diversification Power of Commodities

Are commodities effective diversifiers for stocks and bonds? In his September 2012 paper entitled “Commodity Investments: The Missing Piece of the Portfolio Puzzle?”, Xiaowei Kang examines the diversification properties of commodity indexes relative to stock and bond indexes. He focuses on the widely used S&P GSCI, composed of 24 commodities with liquid futures markets weighted by world production value. He also considers the S&P GSCI Dynamic Roll, designed to suppress negative roll returns by rolling into longer-dated (nearby) futures contracts when a commodity’s term structure is in contango (backwardation). Using monthly levels of these indexes, MSCI World (to represent stocks) and Barclays Global Aggregate Bond Index (to represent bonds), along with contemporaneous U.S. Treasury bill yields to calculate excess returns, from as early as December 1970 through June 2012, he finds that: Keep Reading

Managed Futures as Portfolio Diversifier

Are managed futures programs good portfolio diversifiers? In his September 2012 paper entitled “Revisiting Kat’s Managed Futures and Hedge Funds: A Match Made in Heaven”, Thomas Rollinger updates prior research exploring the diversification effects of adding managed futures to traditional portfolios of stocks and bonds and to portfolios including stocks, bonds and hedge funds. His proxies for the four asset classes are: (1) for stocks, the S&P 500 Total Return Index; (2) for bonds, the Barclays U.S. Aggregate Bond Index; (3) for hedge funds, the HFRI Fund Weighted Composite Index; and, (4) for managed futures programs, the Barclay Systematic Traders Index (focused on systematic trend-following strategies). He assumes monthly (frictionless) portfolio rebalancing. Using monthly returns for the four asset class indexes during June 2001 through December 2011, he finds that: Keep Reading

Gold as Diversifier Versus Safe Haven

Has increasing use of gold as a portfolio diversifier changed the response of its price to crises? In their August 2012 paper entitled “The Destruction of a Safe Haven Asset?”, Dirk Baur and Kristoffer Glover examine the potential of investor behavior to extinguish the safe haven property of gold. Specifically, they consider how widespread inclusion of gold as a diversifier in investment portfolios affects gold price behavior in times of crisis. Based on theoretical conjecture and price data for gold during major financial market crises, they conclude that: Keep Reading

Tests of Strategic Allocations Based on Risk Metrics

Risk-focused asset allocation strategies derive from evidence that forecasting asset return volatility is easier than forecasting average return. Is there a best risk-focused strategy? In his September 2012 paper entitled “A Small Survey of Quantitative Models that Discard Estimation of Expected Returns for Portfolio Construction”, Stefano Colucci compares asset allocation strategies that rely on forecasted asset risk metrics but not on forecasted average returns. Specifically, he compares future gross annualized return-risk ratios, Ulcer indexes, one-month maximum drawdowns and average monthly portfolio turnovers for the following asset allocation strategies:

  1. Minimum Variance (least volatile, or left-most, efficient portfolio per Modern Portfolio Theory).
  2. Minimum Expected Shortfall with weightings estimated by Monte Carlo simulation.
  3. Equal Risk Contribution (each asset weighted by the inverse of its forecasted maximum expected shortfall).
  4. Maximum Diversification (related to expected shortfall with weightings again estimated by numerical simulation).
  5. Risk Parity (each asset weighted by the inverse of its portfolio volatility contribution).
  6. Equal Weighting (requiring neither average return nor volatility forecasts) as a benchmark.

He reforms portfolios every 20 trading days (approximately monthly) and estimates future risk metrics based on a rolling historical window of 500 trading days (approximately two years). Using daily returns over recent periods for stock and bond indexes and individual stocks segregated into several asset selection universes, he finds that: Keep Reading

Mean-Variance Optimizations Versus Equal Weight

Does mean-variance optimization reliably beat simple equal weighting? In his August 2012 paper entitled “The Efficiency of Mean-Variance Optimization with In-depth Covariance Matrix Estimation and Portfolio Rebalancing”, Joonas Hämäläinen tests how many of 96 different mean-variance optimization implementations based on daily data outperform simple equal weighting after accounting for trading frictions. He considers three methods of determining weights for minimum variance portfolios. For each method, he considers three historical intervals for estimating optimal portfolio weights (20, 60 and 250 trading days). He considers three fixed-interval (5, 20 and 60 trading days) and one threshold-based rebalancing rules. His benchmark strategy is equal weight, rebalanced weekly (EW). He tests strategy combinations on four sets of asset returns in euros constructed from 23 MSCI country indexes: 11 European Monetary Union markets during June 2002 through May 2006 (EMU1) and during June 2006 through May 2010 (EMU2); and, 12 big emerging markets during June 2002 through May 2006 (BEM1) and during June 2006 through May 2010 (BEM2). He assumes constant trading frictions of 0.2% (0.4%) of traded value for EMU (BEM) data sets. He focuses on annualized net Sharpe ratio (with risk-free rate zero) and portfolio turnover as critical evaluation metrics. Using daily country total return index levels during June 2001 through May 2010, with out-of-sample tests commencing June 2002, he finds that: Keep Reading

Mean-Variance Investing Basics

How and how well does mean-variance investing work? In his August 2012 draft book chapter entitled “Mean‐Variance Investing”, Andrew Ang compares outcomes for complex asset allocation strategies based on forecasted return statistics to those for very simple strategies such as equal weighting. He illustrates with a horse race among allocation strategies applied to four asset classes (U.S. government bonds, U.S. corporate bonds, U.S. stocks and international stocks), with portfolios reformed monthly based on return statistics estimated from five-year lagged rolling intervals and shorting constrained to no more than -100% for each asset. Using mathematical derivations and monthly return data for the example asset classes during 1973 through 2011, and contemporaneous one-month Treasury bill yields as the risk-free rate, he concludes that: Keep Reading

Fundamentals of Portfolio Weights and Rebalancing

What are the fundamental considerations for portfolio weights and rebalancing rules over the long run? In the July 2012 version of his book excerpt entitled “Dynamic Portfolio Choice”, Andrew Ang elaborates these considerations as derived from two precepts: (1) periodic or conditional rebalancing of the components of a diversified portfolio is foundational to long-term investing; and, (2) target weights for portfolio components can vary for each rebalancing interval as investment opportunities change and investor liabilities, income and risk tolerance evolve. Using mostly theory and some simple example portfolios composed of U.S. stocks and Treasuries during 1926-1940 and 1990-2011, he concludes that: Keep Reading

Mean-Variance Optimization Versus Equal Weight

Is equal weighting of diversified portfolio assets good enough, or are mean-variance optimized allocation strategies constructed from asset return and variance forecasts worth the complexities of implementation? In the June 2012 draft of their paper entitled “Market Volatility, Optimal Portfolios and Naive Asset Allocations”, Massimiliano Caporin and Loriana Pelizzon investigate the conditions under which mean-variance optimized portfolios outperform an equal-weight portfolio. They consider four optimized allocation strategies (mean-variance, and three variations of global minimum variance with no shorting). They apply these strategies to five sets of equity assets diversified across different U.S. stock anomalies according to: (1) industry; (2) size/value; (3) size/short-term reversal; (4) size/momentum; and, (5) size/long-term reversal. They consider four methods for forecasting returns and volatilities for these assets (lagged rolling values, regression on explanatory variables and two technical autoregressions that detect mean reversion), all based on a 60-month or 120-month rolling historical window. They use Sharpe ratio to compare portfolio performances. Using monthly data for the five selected sets of equity assets from Kenneth French’s library spanning April 1953 through December 2010 (693 months), they find that: Keep Reading

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