Bonds

A subscriber suggested testing the diversification power of SPDR Barclays International Treasury Bonds (BWX) as a distinct asset class. To check, we add BWX to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for BWX and the average pairwise correlation of BWX monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without BWX. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for BWX and the above nine asset class proxies from November 2007 (first return available for BWX) through April 2013 (66 monthly returns), we find that: Keep Reading

It is plausible that high-yield corporate bonds have return characteristics substantially different from those of other asset classes, and therefore represent a good diversification opprotunity. To check, we add iShares iBoxx $ High-Yield Corporate Bond (HYG) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for HYG and the average pairwise correlation of HYG monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without HYG. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for HYG and the above nine asset class proxies from May 2007 (first return available for HYG) through April 2013 (72 monthly returns), we find that: Keep Reading

Treasury Inflation-Protected Securities (TIPS), offering an explicit inflation hedge, may be an attractive asset for strategic diversification. To check, we add iShares Barclays TIPS Bond Fund (TIP) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for TIP and the average pairwise correlation of TIP monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without TIP. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for TIP and the above nine asset class proxies as available from January 2004 (first available for TIP) through April 2013 (112 monthly returns), we find that: Keep Reading

It is plausible that crude oil as a dominant energy commodity has return characteristics substantially different from those of other commodities and asset classes, and therefore represent a good diversification opprotunity. To check, we add iShares Barclays 7-10 Year Treasury (IEF) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for IEF and the average pairwise correlation of IEF monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without IEF. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for IEF and the above nine asset class proxies as available from January 2003 (the start of the “Simple Asset Class ETF Strategy”) through April 2013 (124 monthly returns), we find that: Keep Reading

What pitfalls face forecasters trying to predict financial markets with economic data series? In their November 2012 preliminary paper entitled “Forecasting through the Rear-View Mirror: Data Revisions and Bond Return Predictability”, Eric Ghysels, Casidhe Horan and Emanuel Moench examine the predictive power of economic data to predict annual returns for U.S. Treasury notes (T-note) with constant maturities of two, three, four and five years. They focus on the effects of publication delays and use of as-released (real-time) versus revised data. They consider a set of 68 monthly economic variables for which initial release (as well as revised) values and release dates are available since the early 1980s. Using annual T-note returns and both as-released and revised data for these 68 economic variables (such as industrial production, employment, housing indicators, personal income, price indexes and money stock) from ArchivaL Federal Reserve Economic Data) during March 1982 through December 2011, they find that: Keep Reading

Which asset class best hedges inflation? In the September 2012 draft of his book chapter entitled “‘Real’ Assets”, Andrew Ang examines the behaviors of the following assets commonly thought to hold their value during times of high inflation (“real” assets): inflation-linked bonds, commodities, real estate and U.S. Treasury bills (T-bill). He focuses on inflation as year-over-year change in the U.S. Consumer Price Index for all urban consumers and all items, but considers also inflation rates for medical care and higher education. He distinguishes inflation hedging (measured by correlation of returns and inflation) from long-run asset class performance. Using asset class proxy returns and U.S. inflation rates as available through 2011, he finds that: Keep Reading

What does price momentum of stocks, whether total or risk-adjusted, imply about future returns of associated corporate bonds? In their August 2012 paper entitled “Residual Equity Momentum for Corporate Bonds”, Daniel Haesen, Patrick Houweling and Jeroen Van Zundert compare the predictive powers of total stock price momentum and risk-adjusted (residual) stock price momentum to predict returns of same-firm bonds. To focus on firm effects, they remove the influence of interest rates by measuring bond returns in excess of duration-matched U.S. Treasury instruments. They form (overlapping) bond portfolios monthly by: (1) ranking firms into fifths (quintiles) based on cumulative stock returns in excess of the risk-free rate over a past interval (base case, six months); (2) skipping a month; and, (3) forming a hedge portfolio that is long (short) for the next 1, 3, 6 or 12 months the equally weighted bonds of firms in the quintile with the highest (lowest) past stock returns. They calculate residual stock returns via 36-month lagged rolling regressions of excess stock returns versus the Fama-French model risk factors (market, size, book-to-market). Using monthly returns for U.S. investment grade and high-yield corporate bonds and associated stocks (2,442 firms), and for duration-matched U.S. Treasury instruments and the three equity risk factors, during January 1994 through September 2011, they find that: Keep Reading

How do general economic conditions and economy-wide default risk shocks affect corporate bond returns, especially past winners and losers? In the May 2012 draft of their paper entitled “Sources of Momentum in Bonds”, Hwagyun Kim, Arvind Mahajan and Alex Petkevich investigate the relationship between U.S. corporate bond momentum portfolio returns and U.S. aggregate default risk. They measure the momentum effect as average monthly gross returns of overlapping hedge portfolios formed each month by buying (selling) the equally weighted tenth of bonds with the highest (lowest) total cumulative returns over the past six months and holding for six months, with a skip-month between ranking and holding intervals. They measure aggregate default risk as the prior-month yield spread between the Moody’s CCC corporate bond index and the 10-year U.S. Treasury note. They define default risk shocks as deviations from the linear relationships between default risk this month and its values the prior two months. They define high (low) default risk shock conditions as those above (below) the inception-to-date median value of the series. Using price and yield data for all listed U.S. corporate bonds (excluding convertible bonds, asset-backed securities and bonds with very low capitalization) during January 1995 (101 bonds) through December 2010 (2,513 bonds), they find that: Keep Reading

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

How does the effectiveness of safe havens vary over time? In the February 2012 draft of their paper entitled “Safe Haven Assets and Investor Behaviour under Uncertainty”, Dirk Baur and Thomas McDermott examine the roles of gold and U.S. Treasury instruments as safe haven assets during times of financial markets uncertainty. They define a safe haven asset as an asset that is either uncorrelated or negatively correlated with other assets when those other assets are in distress. They focus on the effects of changes in uncertainty (shocks) on asset values and on the pairwise relationships between stocks, bonds and gold. Using daily returns in U.S. dollars for a global stock market index, U.S. Treasuries (2-year, 10-year and 30-year) and gold bullion (spot and futures) from 1980 through 2010 (more than 8,000 daily returns over 31 years), they find that: Keep Reading