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How safe are the safe haven assets?


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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  1. 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.

  2. We choose three-month bond yields so as to obtain the longest sample from Datastream.

  3. 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.

  4. Note that the choice of quantiles is somewhat arbitrary. Our selection was influenced by the work of Baur and McDermott (2010).

  5. 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.

  6. 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.

  7. 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.

  8. Only monthly data are available for real estate and wine, and thus these are excluded from the weekly analyses in Table 4.

  9. We repeat the tests with the VIX outlined in Table 3 at weekly frequency, too. The results are similar to those of Table 3 and are not reported for brevity.

  10. 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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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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Correspondence to John Byong-Tek Lee.

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).

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  • Safe haven assets
  • Global financial crisis
  • Return correlation

JEL Classification

  • G12
  • G15