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Review of Quantitative Finance and Accounting

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20-09-2017 | Original Research

Is gold a hedge against inflation? A wavelet time-scale perspective

Authors: Thomas Conlon, Brian M. Lucey, Gazi Salah Uddin

Published in: Review of Quantitative Finance and Accounting | Issue 2/2018

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Abstract

Among the many presumed characteristics of gold, the ability to act as an enduring store of value is frequently noted. In this paper, the ability of gold to dynamically hedge against inflation is examined for various holding periods using the continuous wavelet transformation. Gold is first established as both a short- and long-term hedge against realized inflation for a number of developed economies. Dynamic analysis demonstrates that these hedging properties are not limited to a single historical cohort. Next, gold is shown to comove with unexpected inflation across all countries examined, albeit with some variation in the direction of the relationship. Finally, the capacity of both gold futures and gold stocks to act as a hedge against inflation is demonstrated.

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For a detailed review of the role of gold in Financial Economics, see Batten et al. (2015).

The relationship between inflation and commodities more generally has also been considered; see for example Gospodinov and Ng (2013) and Chen (2009).

Of particular relevance to this study, the wavelet transformation has been applied the estimation of the optimal futures hedge ratio, with increasing hedging performance detailed at long horizons (see for example, Conlon et al. 2016; Conlon and Cotter 2012; Fernandez 2008), reduced downside risk (Conlon and Cotter 2013) and to understand short- and long-term relationships between real, inflation adjusted, interest rates (Shrestha and Hui 2005). See Gençay et al. (2001) and references therein for detailed treatise of the wavelet transformation. Of relevance to this paper, Michis (2014) uses the discrete wavelet transformation to evaluate how gold contributes, again over different horizons, to portfolio risk.

Other applications of the CWT include macroeconomic characterisation, such as the relation between the yield curve and macroeconomic variables simultaneously on the time and scale domains (Aguiar-Conraria et al. 2012) and the scale-dependent time-varying synchronization between Euro area business cycles (Bekiros et al. 2015).

The wavelet transformation relates to techniques which attempt to split time series into trend and fluctuations, for example Craven and Islam (2015).

See for example Bekiros et al. (2015), Bredin et al. (2015), Aguiar-Conraria and Soares (2014), Tiwari et al. (2015), Michis (2014), Graham et al. (2013), Aguiar-Conraria et al. (2012), Vacha and Barunik (2012) and Rua and Nunes (2009).

Alternative wavelet forms were additionally tested, but resulted in no qualitative changes to findings.

The literature on wavelets equivalently refers to the scale, period and horizon associated with the decomposition, each of which may be interpreted as the wavelength. The inverse of the wavelength is the frequency, measuring the number of cycles per period.

Another alternative, not considered here as data is only available over a short window, is to use Treasury Inflation Protected Securities (TIPS) as a measure of expected inflation.

The methodology is not detailed here, for brevity, but can be found in a very large variety of articles. For example, see Engle (2002) and subsequent contributions.

Note Negative unexpected inflation does not represent disinflation or deflation, instead capturing inflation expectations which are larger than that realized—it is akin to a negative surprise in macroeconomic forecasting.

Quarterly data is chosen here for two reasons; first, seasonally adjusted data is unavailable at a monthly interval and is most appropriate for the time series models. Second, the ability of time series models to forecast deteriorates for longer prediction intervals and a 12 month forecast would have been required for monthly data.

For each of UK, Switzerland and Japan, consistent survey based inflation expectations were not available for an extended period. For this reason, a time series model is employed to generate inflation expectations using quarterly seasonally adjusted CPI. Quarterly adjusted CPI data begins in 1970 in Switzerland, but not until 1986 for the UK and Japan.

The level of unexpected inflation is not shown for brevity, but will be described in the text.

One might also speculate that gold futures might be subject to political risk in a similar way to physical gold. For instance, limits could be introduced with regards to the number of futures contracts a private investor is allowed to hold. Some evidence of the relative importance of financial versus ’real’ economic risk, including political, has been documented (Lucey et al. 2013, 2014).

We are grateful to an anonymous referee for suggesting this alternative methodology.

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Title: Is gold a hedge against inflation? A wavelet time-scale perspective
Authors: Thomas Conlon
Brian M. Lucey
Gazi Salah Uddin
Publication date: 20-09-2017
Publisher: Springer US
Published in: Review of Quantitative Finance and Accounting / Issue 2/2018
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-017-0672-7

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