Gold

In their February 2017 paper entitled “Bayesian Model Averaging, Ordinary Least Squares and the Price of Gold”, Dirk Baur and Brian Lucey analyze a large set of factors that potentially influence the price of gold via two methods: Ordinary Least Squares (OLS, scatter plot) and Bayesian Model Averaging (BMA, accounting for model uncertainty). They include as potential influencers three other precious metals futures, crude oil spot and futures, two commodity indexes, U.S. and world stock indexes, currency exchange rates, 10-year U.S. Treasury note (T-note) yield, U.S. Federal Funds Rate (FFR), a volatility index (VIX) and U.S. and world consumer price indexes. To test robustness of influencers, they consider: (1) subsamples to test consistency over time; (2) daily and monthly measurements to test consistency across sampling frequencies (except consumer price indexes, available only monthly); and, (3) contemporaneous and one period-lagged (predictive) relationships. Using daily and monthly prices for the specified assets during January 1980 through September 2016, they find that: Keep Reading

Are precious metals effective safe havens, preserving capital when stocks and bonds crash? In their January 2017 paper entitled “Reassessing the Role of Precious Metals as Safe Havens – What Colour is Your Haven and Why?”, Sile Li and Brian Lucey assess whether four precious metals (gold, silver, platinum and palladium) are safe havens relative to stock market indexes and 10-year government bonds across 11 countries. The 11 countries are: U.S., UK, Germany, France, Italy, Switzerland, Canada, Japan, China, India and South Africa. They focus on stock and bond market crashes specified as daily returns in the bottom 5% of respective return distributions over the entire sample period. They define weak and strong safe haven behaviors based on moderate and high statistical confidence in crash protection, respectively. They consider different economic and political causes of stock and bond market crashes. Using daily returns for stock market indexes, 10-year government bond indexes and precious metals spot markets for the 11 countries, all in local currencies, during January 1994 through July 2016, they find that: Keep Reading

Do changes in the dollar-euro exchange rate reliably interact with the U.S. stock market and gold? For example, do declines in the dollar relative to the euro indicate increases in the dollar value of hard assets? Are the interactions coincident or exploitably predictive? To investigate, we relate changes in the dollar-euro exchange rate to returns for U.S. stock indexes and spot gold. Using end-of-month and end-of-week values of the dollar-euro exchange rate, levels of the S&P 500 Index and Russell 2000 Index and spot prices for gold during January 1999 (limited by the exchange rate series) through October 2016, we find that: Keep Reading

“Central Bank Gold Reserves, Gold Lending and Gold Price” finds some conditional relationships between aggregate world central bank gold reserves and gold price. Can investors exploit these relationships? To check, we relate recent quarterly estimates of central bank gold reserves and quarterly spot gold price, noting that there is roughly a one quarter delay in availability of the former. Using these data from the end of the first quarter of 2000 through the end of the first quarter of 2016, we find that: Keep Reading

Do central banks predictably influence the price of gold? In the July 2016 revision of his paper entitled “Central Banks and Gold”, Dirk Baur examines interactions of central bank gold reserves, gold carry trade profitability and gold price. Using monthly data for central bank gold reserves and gold prices since 1968 and for 1-month and 6-month gold lease rates since 1989, as available through 2015, he finds that: Keep Reading

What drives the price of gold: inflation, stock market behavior, public sentiment? To investigate, we relate spot gold price to non-seasonally adjusted Consumer Price Index (CPI), the S&P 500 Index and University of Michigan Consumer Sentiment. We start sampling in 1975 because: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.” We lag CPI and consumer sentiment measurements by one month to ensure they are known to the market when calculating gold returns. Using monthly data from January 1975 (January 1978 for consumer sentiment) through June 2016 (498 or 461 months), we find that: Keep Reading

A reader asked whether the gold-gold miner stocks arbitrage-like argument in Jay Kaeppel’s February 2010 article “Don’t Give Up On Gold Stocks Just Yet” (for which his September 2004 article “Gold Stock and Gold Bullion” is a more robust antecedent) supports frequent timing of these assets. For example, if SPDR Gold Shares (GLD) and Market Vectors Gold Miners GDX) diverge over some recent interval, do they then reliably converge quickly? To check, we examine the relative price behaviors of these funds. Using weekly dividend-adjusted closes for GLD and GDX during late May 2006 (inception for GDX) through mid-May 2016, we find that: Keep Reading

Subscribers have proposed that asset class momentum effects should accelerate (shorter optimal ranking interval) when markets are in turmoil (bear market/high volatility). “Asset Class Momentum Faster During Bear Markets?” addresses this hypothesis in a multi-class, relative momentum environment. Another approach is to evaluate the relationship between time series (intrinsic or absolute) momentum and volatility. Applied to the S&P 500 Index and the S&P 500 Implied Volatility Index (VIX), this alternative offers a longer sample period less dominated by the 2008-2009 equity market crash. Specifically, we examine monthly correlations between S&P 500 Index return over the past 1 to 12 months with next-month return to measure strength of time series momentum (positive correlations) or reversal (negative correlations). We compare correlations by ranked fifth (quintile) of VIX at the end of the past return measurement interval to determine (in-sample) optimal time series momentum measurement intervals for different ranges of VIX. We also test whether: (1) monthly change in VIX affects time series momentum for the S&P 500 Index; and, (2) VIX level affects time series momentum for another asset class (spot gold). Using monthly S&P 500 Index levels and spot gold prices since January 1989 and monthly VIX levels since inception in January 1990, all through April 2016, we find that: Keep Reading

Do simple moving averages (SMA) commonly used to identify stock market bull and bear regimes work similarly for the spot gold market? To investigate, we consider two market regime indicators: the 200-day SMA and a combination of the 50-day and 200-day SMAs. Because trading days for gold and stocks are sometimes different, we also check a 10-month SMA based on monthly closes. Using daily and monthly spot gold prices and S&P 500 Index levels during January 1973 through April 2016, we find that: Keep Reading

Are illiquid assets competitive as investments with liquid financial assets over the long run? In his March 2016 paper entitled “The Long-Term Returns to Durable Assets”, Christophe Spaenjers summarizes long-term returns for three types of illiquid assets since the start of the 20th century:

1. Houses and farmland.
2. Collectibles (art, stamps, wine and violins).
3. Gold, silver and diamonds.

He focuses on capital gains but comments on ancillary costs and potential associated income where relevant. Using available monthly price indexes for these assets from a variety of sources during 1900 through 2014, he finds that: Keep Reading