The structure of gold and silver spread returns

Abstract

The price dynamics of gold and silver have long been a matter of popular concern and fascination. The objective of this study is to investigate the dynamics of the bivariate relationship between gold and silver prices. First, we investigate the spread, measured as the price difference between gold and silver trading as a futures contract. Then the presence of a fractal structure is measured using statistical techniques based on rescaled range analysis after accommodating short-term autocorrelated innovations in the return process. To highlight the economic consequences of fractality, we apply trading rules based upon the Hurst coefficient to the time series data. Importantly, we find that these rules out-perform simple buy-hold and moving-average strategies over varying holding periods.

Detecting long-range dependence with truncated ratios of periodogram ordinates

“…Recent studies found indications of time-varying long memory in financial time series (Cajueiro and Tabak 2004;Carbone, Castelli, and Stanley 2004;Hull and McGroarty 2014;Auer 2016b). Batten et al (2013) and Auer (2016a) explored possible applications for the development of profitable trading strategies. They used estimates of the Hurst coefficient H, which is related to the memory parameter d via H ¼ d þ 0:5, for the generation of buy and sell signals.…”

“…They used estimates of the Hurst coefficient H, which is related to the memory parameter d via H ¼ d þ 0:5, for the generation of buy and sell signals. Batten et al (2013) used the values -0.1 and 0.1 as thresholds (for estimates of d, i.e., 0.4 and 0.6 for estimates of H) and a rolling window of 22 trading days. In an effort to reduce transactions costs by reducing the trading frequency, Auer (2016a) increased the threshold values to -0.2 and 0.2 and the window size to 240 trading days.…”

The informational content of unconventional monetary policy on precious metal markets

“…Furthermore, Baur and Tran () concluded that there is a close relationship between gold and silver returns in the long term, which is affected significantly during distress periods. Finally, Narayan, Narayan, and Sharma () and Batten, Ciner, Lucey, and Szilagyi () underline the importance of using commodities within profitable trading strategies, though the performance of these strategies could be affected significantly during distressed periods, as was the case within the recent financial crisis.…”

Improved Estimation of the Memory Parameter

“…Studying emerging stock markets, Cajueiro and Tabak [21], Hull and McGroarty [65] and Auer [27] observed time-varying estimates of the Hurst exponent H. Batten et al [25] and Auer [28] took things a step further. Assuming that fractal dynamics does in fact exist in precious metal returns, they explored possible trading strategies that are based on local estimates of H. In contrast, Reschenhofer et al [66] found no evidence of long-range dependence, neither in stock index returns nor in gold returns.…”