Strategic Allocation

Are implied volatility futures good diversifiers of underlying indexes? Do implied volatility futures for different indexes represent a reliable pair trading opportunity? In their November 2013 paper entitled “Investment Strategies with VIX and VSTOXX Futures”, Silvia Stanescu and Radu Tunaru update the case for hedging conventional stock and stock-bond portfolios with near-term implied volatility futures for the S&P 500 Index (VIX) and the Euro STOXX 50 Index (VSTOXX). For this analysis, they use data for the U.S. and European stock market indexes, associated implied volatility futures and U.S. and European aggregate bond indexes from March 2004 for U.S. assets (VIX futures inception) and from May 2009 for European assets (VSTOXX futures inception), both through February 2012. They also investigate a statistical arbitrage (pair trading) strategy exploiting a regression-based prediction of the trend in the gap between VIX and VSTOXX during the last six months of 2012. Using daily data for the specified indexes and implied volatility futures contracts, they find that: Keep Reading

Have investors overwhelmed commercial traders in commodity futures markets, thereby depressing the value of commodity futures as a diversifier of stocks and bonds? In his November 2013 papers entitled “Implications of Financialization for Commodity Investors: The Case of Roll Yields” and “Implications of Financialization for Strategic Asset Allocation: The Case of Correlations”, Adam Zaremba examines the effects of commodity futures market financialization on the potential diversification benefit of a passive allocation to commodities. He quantifies financialization as the share of open interest in commodity futures contracts held by non-commercial traders per Commitments of Traders reports of the Commodity Futures Trading Commission. He investigates specifically the effects of financialization on: (1) roll return, the return from continually shifting from expiring to longer-term commodity futures contracts to maintain a position; and, (2) the correlations of commodity futures returns with those of stocks and bonds. Then, in a mean-variance optimization framework from the perspective of a U.S. investor, he examines how these effects alter the diversification benefit of adding a commodity futures position to stocks and bonds. Using monthly returns of index proxies for the broad U.S. stock market, U.S. government bonds and a broad set of commodity futures from the end of 1991 through 2012, he finds that: Keep Reading

How can retirees optimally segregate reliable income from risky growth? In their November 2011 paper entitled “The Floor-Leverage Rule for Retirement”, flagged by a subscriber, Jason Scott and John Watson examine a retirement allocation strategy that strictly segregates safe income-generating assets (“riskless” bonds) from potentially income-boosting risky assets (stocks). They designate the safe allocation as the floor portfolio, funded to guarantee a real income level in perpetuity. They designate the risky allocation as the surplus portfolio, which invests all remaining funds to capture a risk premium. If the risky assets perform well, the retiree periodically moves funds from the surplus portfolio to the floor portfolio and thereby increases guaranteed income. In assessing this floor-surplus approach, the authors assume that the riskless bonds generate a steady 2% annual real return and that the risky assets offer a 6% annual risk premium with 18% annual volatility (like the U.S. equity markets over the long run). Based on analysis of several case studies using these return assumptions, they conclude that: Keep Reading

Has Modern Portfolio Theory failed to deliver over the past decade because users employ long-term averages for expected returns, volatilities and correlations that do not respond to changing market environments? Do short-term estimates of these key inputs work better? In their May 2012 paper entitled “Adaptive Asset Allocation: A Primer”, Adam Butler, Michael Philbrick and Rodrigo Gordillo backtest a progression of strategies culminating in an Adaptive Asset Allocation (AAA) strategy that incorporates return predictability from relative momentum (last 120 trading days, about six months), volatility predictability from recent volatility (last 60 trading days) and pairwise correlation predictability from recent correlations (last 250 trading days). Their tests employ nine asset class indexes (U.S. stocks, European stocks, Japanese stocks, U.S. real estate investment trusts (REIT), International REITs, intermediate-term U.S. Treasuries, long-term U.S. Treasuries and commodities) and a spot gold price series. They reform portfolios monthly based on evolving return, volatility and correlation forecasts. They ignore trading frictions as negligible for “intelligent retail or institutional investors” via mutual funds or Exchange Traded Funds. Using daily returns for the nine indexes and spot gold) to test six strategies during January 1995 through March 2012, they find that: Keep Reading

Does the conventional wisdom of a declining allocation to stocks throughout retirement really work best? In their September 2013 paper entitled “Reducing Retirement Risk with a Rising Equity Glidepath”, Wade Pfau and Michael Kitces explore alternative stocks-bonds allocations during retirement. They consider retirees planning for annual withdrawals of an inflation-adjusted 4% or 5% of retirement date assets over 20, 30 or 40 years. They consider three scenarios for future stocks/bonds return statistics (see the table below): (1) assumptions prepared for the MoneyGuidePro financial planning software as of July 2013; (2) a pessimistic scenario more closely calibrated to the current low-interest rate environment, but with an historical equity risk premium; and, (3) an optimistic scenario with stock and bond returns based on historical averages for 1926 through 2011. They assume year-end withdrawals and rebalancings of residual assets to target allocations, with withdrawals covering tax obligations. If a withdrawal pushes the account balance to zero, the portfolio fails. They also consider both the potential failure magnitude and upside potential. They consider 11 at-retirement equity allocations ranging from 0% to 100% in 10% increments gliding linearly to each of 11 at-horizon equity allocations ranging from 0% to 100% in 10% increments (a total of 121 glidepaths). Using outputs from 10,000 Monte Carlo simulations for each of the 121 glidepaths for each combination of withdrawal rate, retirement horizon and future return scenario, they find that: Keep Reading

Are stocks so attractive over the long run that they crowd bonds and cash out of the optimal portfolio? In their September 2013 paper entitled “Optimal Portfolios for the Long Run”, David Blanchett, Michael Finke and Wade Pfau relate optimal portfolio equity allocation to investment horizon worldwide to determine whether stocks universally exhibit time diversification (whereby mean reversion of returns causes equity risk to decrease as investment horizon lengthens). In calculating optimal equity allocation, they employ a utility function to model how investors feel about the risk of good and bad outcomes (not volatility as measured by standard deviation of returns). They consider different levels of investor risk aversion on a scale of 1 to 20, with 20 extremely risk averse. They measure returns for both overlapping and independent investment intervals of 1 to 20 years. They constrain portfolios to long-only positions in three assets: government bills (cash), government bonds and stock indexes. Using annual real returns to local investors in bills, bonds and stock indexes for 20 countries during 1900 through 2012, they find that: Keep Reading

Should long-term investors focus on terminal wealth and ignore interim volatility? In his August 2013 paper entitled “Rethinking Risk”, Javier Estrada compares distributions of terminal wealths for $100 initial investments in stocks or bonds over investment horizons of 10, 20 or 30 years. He utilizes mean, median, tail (extreme 1%, 5% and 10%) and risk-adjusted performance metrics. He employs real returns for 19 country markets adjusted by local inflation and in local currency for individual country markets, and adjusted by U.S. inflation and in dollars for the (capitalization-weighted) World market. Using real annual total returns for indexes of stocks and government bonds in each country during 1900 through 2009 (101, 91, and 81 overlapping intervals of 10, 20, and 30 years), he finds that: Keep Reading

Does trading based on simple moving average crossings reliably improve the performance of a portfolio diversified across asset classes? In the February 2013 update of his paper entitled “A Quantitative Approach to Tactical Asset Allocation”, Mebane Faber examines the effects of applying a 10-month simple moving average (SMA10) timing rule separately to each of the following five total return indexes a part of an equally weighted, monthly rebalanced portfolio: (1) S&P 500 Index; (2) 10-Year Treasury note constant duration index; (3) MSCI EAFE international developed markets index; (4) Goldman Sachs Commodity Index (GSCI); and, (5) National Association of Real Estate Investment Trusts index. Specifically, at the end of each month, he enters from cash (exits to cash) any index crossing above (below) its SMA10. Entry and exit dates are the same a signal dates (requiring some anticipation of signals before the close). The return on cash is the 90-day Treasury bill (T-bill) yield. Calculations ignore trading frictions and tax implications. Using monthly total return series for selected indexes mostly during 1972 through 2012, he finds that: Keep Reading

Do the relationships among returns for stocks and the most heavily traded commodities (gold and crude oil) consistently offer risk diversification? In their July 2013 paper entitled “Gold, Oil, and Stocks”, Jozef Baruník, Evzen Kocenda and Lukas Vacha analyze the return relationships among stocks ( the S&P 500 Index), gold and oil (light crude) over the past 26 years. Specifically, they test the degrees to which their prices: (1) co-move; (2) reliably lead one another; and, share any long-term relationships (such as ratios to which they revert). They seek robustness of findings by employing a variety of methods, data sampling frequencies and investment horizons. Using intraday and daily prices of the most active rolling futures contracts for the S&P 500 Index, gold and light crude oil during 1987 through 2012, they find that: Keep Reading

How can investors capture returns from widely accepted risk factors associated with asset classes and subclasses? In the June 2013 version of his book chapter entitled “Factor Investing”, Andrew Ang provides advice on capturing risk premiums associated with factors such as value, momentum, illiquidity, credit risk and volatility risk. Based on the body of research, he concludes that: Keep Reading