Abstract
The aim of this paper is to examine which of the assets commonly believed to be safe havens do, in fact, protect investors during periods of severe financial instability. Using a broad dataset of 32 assets over the period of 1964–2014, we examine the relationship of these assets with the US equity market during financial crises to determine which of them are safe havens for US investors, hedges, or speculations. We find that the US Treasuries and Japanese yen are the strongest safe haven investments in months characterized by large declines in market value or excessive volatility. We also document that the recent global financial crisis had significantly negative ramifications on the safe haven properties of many of these assets. Our out-of-sample analyses show that while, in general, predictive market exposures are negatively correlated with asset returns in strong market downturns, those of even the strongest safe haven assets are often statistically insignificant.
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Notes
An earlier version of the study examined a wider range of assets, including international equity indices and other sovereign bonds and currencies. As one would expect, all the equity indices were positively correlated with US market returns and other bonds and currencies yielded results similar to those of their peers in the current version and thus were dropped to conserve space.
We choose three-month bond yields so as to obtain the longest sample from Datastream.
The chosen return frequency is somewhat arbitrary. The monthly return period is chosen, first, based on data availability and, second, because this frequency is likely to be of most interest to investors. We also conduct analyses on weekly returns as a robustness check.
Note that the choice of quantiles is somewhat arbitrary. Our selection was influenced by the work of Baur and McDermott (2010).
To account for potential heteroskedasticity in the data, we also specified a corresponding GARCH(1,1) equation (\(h_t =\pi +\alpha e_{t-1}^2 +\beta h_{t-1})\) and estimated this full model simultaneously using maximum likelihood. However, the results obtained from the GARCH tests did not differ significantly from the OLS estimates. Therefore, we report only the results of the OLS analysis for brevity.
The Swiss franc had periods of set minimum rates against other currencies—in 1978 against the German mark and from September 2011 against the euro. The results excluding these periods are similar. We thank the anonymous referee for pointing this out.
We also employ realized volatility computed from the daily S&P 500 returns within each month as another measure of uncertainty. The results (not reported) are qualitatively similar to those with the VIX presented in Table 3. We thank the anonymous referee for the suggestion.
Only monthly data are available for real estate and wine, and thus these are excluded from the weekly analyses in Table 4.
The GFC results are slightly different from those in Table 6 as the crisis dummy was set with the observations leading up to the GFC only.
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Acknowledgements
The initial version of the paper was Kopyl’s Honours dissertation at the University of Auckland, which was completed prior to her employment by the Reserve Bank of Australia. The views expressed in this paper are solely those of the authors and not necessarily those of the Reserve Bank of Australia. This paper benefited substantially from the comments and suggestions of the anonymous referee.
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Appendix: Description of data
Appendix: Description of data
This table describes the data used in this paper. The test assets, shown in Panels A–D, are grouped by primary asset class. Panel E displays the two independent variables, which are used as proxies to identify periods of financial instability. The table displays the name of the asset, as used throughout the paper; the source of the data with code when applicable; and the month in which the price series for each asset began in the data (this is the latest of December 1963 and the first available data point for the given asset from the identified source).
Asset | Data source | Start |
|---|---|---|
Panel A: 10-year government bond indices | ||
Australia | Datastream: BMAU10Y | Feb 1987 |
Canada | Datastream: BMCN10Y | Dec 1984 |
European Union | Datastream: BMEN10Y | Jan 1999 |
France | Datastream: BMFR10Y | Jan 1985 |
Germany | Datastream: BMBD10Y | Dec 1979 |
Italy | Datastream: BMIT10Y | Mar 1991 |
Japan | Datastream: BMJP10Y | Dec 1983 |
Netherlands | Datastream: BMNL10Y | Dec 1987 |
New Zealand | Datastream: BMNZ10Y | Mar 1991 |
Spain | Datastream: MBES10Y | Nov 1990 |
Sweden | Datastream: BMSW10Y | Dec 1980 |
United Kingdom | Datastream: BMUK10Y | Dec 1979 |
United States | Datastream: BMUS10Y | Dec 1979 |
Panel B: Currencies | ||
Australian dollar | Datastream: USDAUSP | Dec 1983 |
Brazilian real | Datastream: BRUSDSP | Oct 1994 |
British pound | Datastream: USDOLLR | Dec 1963 |
Canadian dollar | Datastream: CNDOLLR | Dec 1964 |
German mark | Datasteram: DMARKER | Dec 1963 |
Japanese yen | Datastream: JPUSBOE | Jan 1975 |
Norwegian krone | Datastream: NORKRON | Dec 1964 |
Swedish krona | Datastream: SWEDRON | Dec 1964 |
Swiss franc | Datastream: SWISSFR | Dec 1964 |
Panel C: Commodities | ||
Copper | Datastream: LCPCASH | Dec 1963 |
Crude oil | Datastream: CRUDOIL | Jan 1986 |
Gold | Datasteram: GOLDBLN | Jan 1968 |
Natural gas | Datastream: NATLGAS | Jan 1993 |
Platinum | Datastream: PLATFRE | Jan 1976 |
Silver | Datastream: SILVUSL | Jan 1968 |
Palladium | Datastream: PALLADM | Jan 1987 |
Diamonds | Datastream: DIA1DFL | Jan 2002 |
Panel D: Alternative assets | ||
Real estate | S&P/Case-Shiller | Jan 1987 |
Wine | Liv-Ex 100 | Jul 2001 |
Panel E: Financial stability indicators | ||
S&P500 | Datastream: S&PCOMP | Dec 1963 |
VIX | Datastream: CBOEVIX | Jan 1990 |
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Kopyl, K.A., Lee, J.BT. How safe are the safe haven assets?. Financ Mark Portf Manag 30, 453–482 (2016). https://doi.org/10.1007/s11408-016-0277-5
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DOI: https://doi.org/10.1007/s11408-016-0277-5