Short-term and long-term dependencies of the S&P 500 index and commodity prices

Abstract

We utilize wavelet coherency methodology with simulated confidence bounds to examine the short-term and long-term dependencies of the returns for S&P 500 and the S&P GSCI ® commodity index. Our results indicate no evidence of co-movement between S&P 500 total return and the S&P GSCI ® commodity index total return in the short term, thereby suggesting diversification gains for equity investors. Importantly, this finding encompasses the onset of the current financial crisis. However, long-term diversification benefits, particularly after the onset of the recent financial crisis, are limited. We find, moreover, no consistent evidence of co-movements between S&P 500 and 10 individual sub-indexes of the S&P GSCI ® commodity index. Of particular importance, we report weak co-movement of returns between S&P 500 and S&P GSCI ® Precious Metals total return and S&P 500 and S&P GSCI ® Softs at all frequencies, implying significant diversification gains both for short-term and long-term investors.

Gold, oil, and stocks: Dynamic correlations

“…Gençay et al (2001) and Ramsey (2002) provide ample exposition on the use and versatility of wavelet techniques in economics and finance. During the past decade the methodology gained currency and relevant applications of wavelets include analyses of stocks (Fernandez, 2006(Fernandez, , 2008In and Kim, 2006;Rua and Nunes, 2009), commodities (Vacha and Barunik, 2012;Graham et al, 2013;Reboredo and Rivera-Castro, 2014a), exchange rates (Nekhili et al, 2002;Karuppiah and Los, 2005;Nikkinen et al, 2011), and other financial and economic variables or their interactions In, 2005, 2007;Faÿ et al, 2009;Rua, 2010;Aguiar-Conraria and Soares, 2011;Gallegati et al 2011;Aguiar-Conraria et al, 2012;Reboredo and Rivera-Castro, 2014b). By using wavelets we are able to test the hypothesis on the existence of homogeneity in dynamic correlations across various investment horizons among assets, an issue that so far has been largely overlooked in the literature.…”

Integrating High Volume Financial Datasets to Achieve Profitable and Interpretable Short Term Trading with the FTSE100 Index

“…However, the reality is very different. As expected several articles indicated dependencies between the FTSE100 index and several other financial indices [6,7] and these dependencies have not yet been studied thoroughly. Thus, in order to achieve optimal prediction and trading results using the FTSE100 index, several inputs from other financial indices should be utilized alongside with the traditional autoregressive and technical indicator inputs.…”

Long‐term dynamics of the VIX index and its tradable counterpart VXX

“…Aguiar‐Conraria and Soares () analyzed the time–frequency relationship between oil prices and macroeconomic variables. Graham, Kiviaho, and Nikkinen () studied the time–frequency relationship between the returns of S&P 500 and S&P GSCI commodity index. Vacha and Barunik () examined the relationship between commodities (crude oil, gasoline, heating oil, and natural gas).…”