Are Gold and Government Bond Safe‐Haven Assets? An Extremal Quantile Regression Analysis

Abstract

This study reexamines gold and government bonds as potential safe-haven assets (SHAs) during market turmoil from daily data in 16 international markets over the past 20 years. We apply the extremal quantile regression model by Chernozhukov and Chernozhukov and Fernandez-Val for empirical investigation. The outcomes indicate that a government bond is more likely to be qualified an active SHA, which can increase in value during market turmoil. Gold can be generally evaluated as a passive SHA, which is uncorrelated with market slumps. However, at the extremal 0.001 quantile level, neither asset can be qualified as a SHA. Since both assets exhibit a similar number of cases of being qualified as SHAs, we cannot significantly differentiate the "flightto-liquidity" and "flight-to-quality" hypotheses. In terms of market selection, United States and Singapore are the top two choices while France and Hungary are the least commended markets to invest their local gold market as SHA.

Is Gold a Safe Haven? International Evidence Revisited

“…One explanation would be that, due to the easy monetary policies of the advanced nations, the massive influx of money into the emerging and developing countries might have worried the recipient governments about a sudden reversal in their currencies. (2015), and Liu (2019) confirmed the safe-haven feature of gold for the advanced economies. However, when it comes to equity markets in the emerging and developing countries, there are mixed results.…”

Is gold a safe haven? The international evidence revisited

“…Baur and Lucey (2010) find that, except for Australia, Canada, and Japan, gold is a safe haven for major European stock markets and the US. Ciner et al (2013), Baur and McDermott (2010) and Liu (2019) confirm the safe haven feature of gold for advanced economies 3 . However, when it comes to equity markets in emerging and developing countries, there are mixed results.…”

Overnight returns of industry exchange‐traded funds, investor sentiment, and futures market returns

“…where ( ) Avg are defined as mentioned previously. Following Koenker and Hallock (2001) and Liu (2020), this paper uses quantile levels of 0.15 and 0.85 to estimate quantile regressions. Then, SEN m RCM is calculated as follows:…”