**1. Introduction and Motivation**

A financial asset is referred to as a "safe haven" asset if it provides hedging benefits during periods of market turbulence. In other words, during periods of market stress, "safe haven" assets are supposed to be uncorrelated, or negatively correlated, with large markets slumps experienced by more traditional financial assets (typically stock or bond prices).

The financial literature identifies various asset classes exhibiting "safe haven" features: gold and other precious metals, the exchange rates of some key international currencies against the US dollar, oil and other important agricultural commodities, and US long-term governmen<sup>t</sup> bonds.

This paper contributes to the existing literature focusing on some of the most representative "safe haven" assets, namely gold, the Swiss Franc/US dollar exchange rate, and oil. The main motivation behind this choice is twofold.

First, empirical research on these assets have attracted major attention in recent years, both from academia and from institutional investors. Second, there are some weaknesses in the applied literature that need to be addressed.

The hedging properties of gold and its monetary role as a store of value are widely documented. Jaffe (1989) and Chua et al. (1990) find that gold yields significant portfolio diversification benefits. Moreover, the "safe haven" properties of gold in volatile market conditions are widely documented: See, among others, Baur and McDermott (2010), Hood and Malik (2013), Reboredo (2013), and Ciner et al. (2013).

The popular views of gold as a store of value and a "safe haven" asset are well described in Baur and McDermott (2010). As reported by these authors, the 17th Century British Mercantilist Sir William Petty described gold as "*wealth at all times and all places*" (Petty 1690). This popular perception of gold

spreads over centuries, reinforced by its historic links to money, and even today gold is described as "*an attractive each way bet*" against risks of financial losses or inflation (Economist 2005, 2009).

Turning to the role of the Swiss Franc as a "safe haven" asset, Ranaldo and Söderlind (2010) documented that the Swiss currency yields substantial hedging benefits against a decrease in US stock prices and an increase in forex volatility. These findings corroborate earlier results (Kugler and Weder 2004; Campbell et al. 2010). More recent research documented that increased risk aversion after the 2008 global financial turmoil strengthened the "safe haven" role of the Swiss currency (Tamakoshi and Hamori 2014).

The oil hedging properties have mostly been underlined in relation to governmen<sup>t</sup> bonds, since oil price increases are usually related to an increase in expected inflation which, in turn, negatively affects bond prices. Recent research provides strong evidence in this direction, confirming that oil qualifies as a "safe haven" financial instrument against governmen<sup>t</sup> bonds on most international financial markets (Nguyen and Liu 2017), particularly under distressed market conditions (Ciner et al. 2013).

Although the recent literature made consistent progresses applying various econometric methodologies, there are still some notable shortcomings in existing applied work.

More specifically: (1) The effects of financial crises on time-varying correlations between these assets have rarely been explored, notwithstanding the occurrence of many crises episodes in the latest years; (2) the effects of macroeconomic and financial variables potentially affecting the degree of agents risk-aversion (and hence dynamic correlation patterns) have likewise been seldom addressed.

The former issue is almost completely neglected in recent contributions (e.g., Ding and Vo 2012; Ciner et al. 2013; Creti et al. 2013; Jain and Biswal 2016; Poshakwale and Mandal 2016; Kang et al. 2016, 2017; Nguyen and Liu 2017). Only a small number of papers explore the impact of crises episodes either on dynamic correlations (Tamakoshi and Hamori 2014) or modeling return co-movements through copula theory (Bedoui et al. 2018); these contributions, moreover, focus exclusively on the 2007/2009 global financial crisis.

A similar weakness is apparent with regards to the impact of macroeconomic and financial variables, since the bulk of applied work neglects their potential effects on dynamic correlations. One relevant exception is represented by Poshakwale and Mandal (2016), which documents a significant impact of macroeconomic, non-macroeconomic, and financial variables on "safe haven" assets co-movements.

A better understanding of factors driving time-varying conditional correlations is important both to assess the effective relevance of the hedging properties of "safe haven" assets and from an optimal asset allocation perspective.

Since "safe haven" assets are presumed to offer protection against market slumps, one would expect, during each financial crisis, a massive portfolio shift towards these assets with consequent increases in their return co-movements. As shown in Bedoui et al. (2018), this was actually the case during the 2007/2009 global financial crisis, since the dependence structures among oil, gold, and Swiss Franc returns significantly rose with respect to untroubled periods. However, how robust is this empirical evidence to financial crises occurring after the 2007/2009 turmoil? In other words, do "safe haven" assets consistently display their hedging properties during all financial crises?

Similar questions arise with regards to the influence of economic and financial variables on dynamic correlations. Poshakwale and Mandal (2016) consider three "safe haven" assets (gold, oil, 10-year US governmen<sup>t</sup> bonds) and document a significant impact of non-macroeconomic variables on their return co-movements. How robust are these results to a change in the bundle of "safe haven" assets? How robust is this empirical evidence to different, and potentially equally relevant, economic and financial variables such as systemic stress indicators, economic policy uncertainty indicators, consumer confidence indicators, or the world equity risk premium? As discussed in Poshakwale and Mandal (2016), the forecasting performance of models explaining asset return co-movements is an important issue in asset allocation problems, and a better knowledge of variables driving correlation patterns is important for international investors and portfolio managers. Asset return co-movements,

in other words, have an economic value when implementing dynamic asset allocation strategies (Guidolin and Timmermann 2007).

In light of the above discussion, this paper takes a first step towards a more accurate investigation of dynamic linkages among three important "safe haven" assets. Since the analysis does not include any traditional financial asset, the purpose of this paper is not to assess the hedging properties of gold, oil, and the Swiss franc but, more simply, to explore the underlying determinants of their correlation patterns during the last two decades.

The structure of the paper is as follows. Section 2 describes the data set and provides some descriptive statistics. Section 3 implements a standard econometric approach, i.e. the Multivariate Garch Dynamic Conditional Correlation (DCC) model of Engle (2002), in order to obtain time-varying conditional correlations estimates. On this basis, Section 4 explores the determinants of dynamic linkages among gold, oil, and the Swiss Franc, focusing on the impact of recent financial crises and of some relevant economic variables. Section 5 concludes.
