# Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war

Alhonita Yatie

<figure><img src="image_1.png" /></figure>

## To cite this version:

Alhonita Yatie. Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war. 2022. [https://doi.org/10.36251/hal-03625196](https://doi.org/10.36251/hal-03625196)

HAL Id: hal-03625196

https://hal.science/hal-03625196

Preprint submitted on 30 Mar 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war

Alhonita Yatié

Univ. Bordeaux, CNRS, BSE, UMR 6060, F-33600 Pessac, France

<figure><img src="image_3.png" /></figure>

Bordeaux Sciences Economiques
Bordeaux School of Economics

BSE UMR CNRS 6060

Université de Bordeaux
Avenue Léon Duguit, Bât. H
33608 Pessac - France

Tel : +33 (0)5.56.84.25.75

http://bse.u-bordeaux.fr/
**Abstract**

This paper studies the impact of fear, uncertainty and market volatility caused by the Ukraine-Russia war on crypto-assets returns (Bitcoin and Ethereum) and Gold returns. We use the searches on Wikipedia trends as proxies of uncertainty and fear and two volatility indices: S&P500 VIX and the Russian VIX (RVIX).

The results show that Bitcoin, Ethereum and Gold failed as safe havens during this war.

**Keywords:** War, Russia, Ukraine, crypto-assets, Gold, Safe haven.

**JEL:** H56, G32, G12, G15.

**To cite this paper:** Yatié Alhonita (2022), Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war, Bordeaux Economics Working Papers, BxWP2022-07
https://ideas.repec.org/p/grt/bdxewp/2022-07.html

<figure><img src="image_4.png" /></figure>
# Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war

Alhonita YATIE<fnref n="1" />

March, 2022

## Abstract

This paper studies the impact of fear, uncertainty and market volatility caused by the Ukraine-Russia war on crypto-assets returns (Bitcoin and Ethereum) and Gold returns. We use the searches on Wikipedia trends as proxies of uncertainty and fear and two volatility indices: S&P500 VIX and the Russian VIX (RVIX). The results show that Bitcoin, Ethereum and Gold failed as safe havens during this war.

JEL Code: H56, G32, G12, G15.

Keywords: War, Russia, Ukraine, crypto-assets, Gold, Safe haven.

## 1. Introduction

> "A safe haven asset holds its value in 'stormy weather' or adverse market conditions. Such an asset offers investors the opportunity to protect wealth in the event of negative market conditions"

Baur and McDermott (2010).

After quite a few months of encampment near the Ukrainian border, on February 24, 2022, Russian troops attacked Ukraine. This rather "unexpected" attack, despite the ambient tensions since December 2021, will create a lot of volatility on the Russian financial market<fnref n="2" /> and the decline of many indices<fnref n="3" />. The European Union will not be slow to react by inflicting financial sanctions on Russia. To avoid capital flight, the Russian Central Bank will request "the closing of the stock market", so the IMOEX index will no longer be listed from February 25.

This crisis is causing significant financial stress that the ECB will highlight on March 14, 2022 with the publication of the CISS graph, the systemic stress indicator in Europe. All these events revive the debate on the search for effective safe haven assets: assets capable of reacting positively to increased stress and volatility in the financial markets. We selected some assets that have been described several times in the literature as safe haven assets: **Cryptoassets and Gold**. Our study is therefore in line with the studies of Baur and Lucey (2010), Stensås et al. (2019), Urquhart and Zhang (2019), Luc Duc Huynh (2020), Ding et al (2022), Su et al. (2022) etc. who analyze the characteristics of these assets in times of crisis.

<footnote n="1">¹ Univ. Bordeaux, BSE, F-33600 Pessac, France. E-mail address: alhonita.yatie@u-bordeaux.fr.</footnote>

<footnote n="2">² Russian VIX (+40.31%).</footnote>

<footnote n="3">³ IMOEX (-33.28%), CSI 1000 (2.19%), CROBEX (-6.48%), PX (-4.77%).</footnote>
Our analysis shows that Bitcoin, Ethereum and Gold are not safe havens during this crisis as they react negatively to stress and volatility indicators. The remainder of the paper is organized as follows. Section 2 describes the data and the model. Section 3 presents the results. Section 4 concludes.

## 2. Data and Model

Our analysis is based on daily data from 1 November 2021 to 15 March 2022. The data about Bitcoin and Ethereum prices was extracted from CoinGecko, the data on Gold price from Banque de France, data on S&P VIX are from S&P Global website, data on Russian VIX (RVIX) are from www.investing.com and the volume of searches about Ukraine- Russia war are from Wikipedia Trends. We use a DCC-GARCH on the log-returns of our variables<fnref n="4" />. The aim is to capture the dynamic nature of Gold, Bitcoin and Ethereum as safe havens during this war. DCC-GARCH captures the interactions among assets by allowing the correlations to change over the time.

The model is defined as:

$$r_t = \mu_t + \varepsilon_t, \quad \varepsilon_t | E(\varepsilon_t) = 0, \text{Cov}(\varepsilon_t) = H_t \quad (1)$$

$$\varepsilon_t = \sqrt{H_t} u_t, \quad u_t \sim N(0, I) \quad (2)$$

$$H_t = D_t R_t D_t \quad (3)$$

Where $r_t$, $\mu_t$, $\varepsilon_t$ and $u_t$ are $N \times 1$ dimensional vectors representing respectively log returns of n assets at time t, expected value of the conditional $r_t$, mean-corrected returns of n assets at time t and iid errors.

$H_t$, $R_t$ and $D_t$ are $N \times N$ dimensional matrices illustrating respectively time-varying matrix of conditional variances of $\varepsilon_t$, time-varying conditional correlation matrix of $\varepsilon_t$ and time-varying diagonal matrix of conditional standard deviations of $\varepsilon_t$.

The DCC-GARCH<fnref n="5" /> (1,1) equation is then given by $Q_t$:

$$Q_t = (1 - \alpha - \beta)\bar{Q} + a\varphi_{t-1}\varphi'_{t-1} + \beta Q_{t-1} \quad \text{with} \quad \varphi_t = D_t^{-1}\varepsilon_t \quad (4)$$

Where $\varphi_t$ is a vector of standardized residuals from the first-step estimation of the GARCH (1,1) process, $Q_t$ is the time-varying unconditional correlation matrix of $\varphi_t$ and $\bar{Q}$ is a $N \times N$ dimensional positive-definite matrix which represents the unconditional covariance matrix of $\varphi_t$.

$\alpha$ and $\beta$ satisfy $\alpha + \beta < 1$. As long as $\alpha + \beta < 1$ is fulfilled.

As a robustness test, we run OLS regressions with Prais-Winstern robust estimator, as presented in equations 5 and 6:

<footnote n="4">⁴ We use a Dickey-Fuller test to verify the stationarity of the variables. All the variables are stationary.</footnote>

<footnote n="5">⁵ The numerical results of the DCC-GARCHs are available upon request. We also confirm the presence of autocorrelation and volatility clustering in all the return series. Finally there are the presence of ARCH effect and GARCH effect.</footnote>
$$ \text{Coin}_t = \gamma + \beta_1 \text{Coin}_{t-1} + \beta_2 \text{Wikipedia}_t + \beta_3 \text{Ukrainewar} * \text{Wikipedia}_t + \beta_4 \text{VIX}_t + \beta_5 \text{RVIX}_t + \\ \beta_6 \text{Ukrainewar} * \text{VIX}_t + \beta_7 \text{Ukrainewar} * \text{RVIX}_t + \varepsilon_t \quad (5) $$

$$ \text{Gold}_t = \gamma + \beta_1 \text{Gold}_{t-1} + \beta_2 \text{Wikipedia}_t + \beta_3 \text{Ukrainewar} * \text{Wikipedia}_t + \beta_4 \text{VIX}_t + \beta_5 \text{RVIX}_t + \\ \beta_6 \text{Ukrainewar} * \text{VIX}_t + \beta_7 \text{Ukrainewar} * \text{RVIX}_t + \varepsilon_t \quad (6) $$

Where $\text{Coin}_{t-1}$ is the Crypto-asset returns at day t-1, $\text{Gold}_{t-1}$ is Gold return at day t-1, $\text{Wikipedia}_t$ is the volume of searches related to Ukraine-Russia war<fnref n="6" /> at day-t and $\text{Ukrainewar}$ is a dummy variable that equals one if day-t is on the beginning of the war or the subsequent days and 0 otherwise. **If a Crypto-asset or Gold serves as a safe haven asset during the war, then the coefficients related to Wikipedia trends and the volatility indices are expected to be positive and significant.**

### 3. Results

As a proxy of uncertainty and fear we use Wikipedia trends. The searches volumes on Wikipedia Trends indicate people's interest on a subject (Kristoufek, 2013, Dastgir et al., 2019) and are used by Zhang et al. (2018b) as an “online sentiment proxy” which could have an impact on financial market for instance (Zhang et al., 2018a; Ruan et al., 2018).

First, Figure 1 presents the heatmap of the correlation between the series. A dark red color indicates that the respective two variables are highly negatively correlated, while dark blue indicates a highly positive correlation. As we can notice, the correlations between Gold, Bitcoin or Ethereum and our indicators of fear/uncertainty are globally negative. It is the same case for all the volatility indices. These results can give us a hint about the failure of Gold and Crypto-assets as safe havens during this crisis.

In Figure 2 the correlations between Bitcoin returns and our indicators of uncertainty or fear are negative. The correlations between Bitcoin returns and volatility indicators are also negative at the beginning of the crisis and a few weeks after, showing once again the absence of safe haven properties for Bitcoin. When we take a look to the volatility indices only, Bitcoin cannot be considered at all as a safe haven during the study period. These results are confirmed by the robustness test in Table 1, Bitcoin returns have a negative and significant relationship with: the volume of searches about the keyword “Ukraine war” (and its dummy variable) and S&P500 VIX (and its dummy variable). These results are in line with those from Su et al. (2022) and Choi and Shin (2021) who show that Bitcoin prices decrease significantly in response to financial uncertainty shocks measured by the VIX, suggesting that Bitcoin is not a safe haven asset.

In Figure 3, we see the same results for Ethereum. The correlations between Ethereum returns and our various fear and volatility indicators are negative, showing that Ethereum could not serve as a safe haven asset during the crisis. The robustness test in Table 1 shows that Ethereum returns have a negative and significant relationship with: the dummies variables of the searches about the keyword “Ukraine war” and Vladimir Putin, and S&P500 VIX (and its dummy variable).

Figure 4 also shows that Gold is negatively correlated with fear and volatility indicators. However, we note a resumption of its status as a safe haven/hedge asset for the S&P500 VIX. The robustness test in Table 1 shows that Gold returns have a negative and significant relationship with: the dummy variable of the searches about the keyword “Vladimir Putin” and S&P500 VIX (and its dummy variable). These results are in line with those from Hood and Malick (2013) who find that Gold cannot act as a safe haven in times

<footnote n="6">⁶ The words include in Wikipedia trends are: Vladimir Putin, Ukraine-Russia, Ukraine war).</footnote>
of high market volatility. Ding et al. (2022) show also that Gold can act only as a weak-hedge during political risk.

<figure><img src="image_5.png" /><figcaption>Figure 1: Heatmap of the correlation between the series</figcaption></figure>

Note: A dark red color indicates that the respective two variables are highly negatively correlated, while dark blue indicates a highly positive correlation.

<figure><img src="image_6.png" /><figcaption>Figure 2: Correlations between Bitcoin returns and the uncertainty/volatility series</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 4: Correlations between Gold returns and the uncertainty/volatility series</figcaption></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>Table 1: Robustness test</figcaption></figure>

<table><thead><tr><th>Variables</th><th>Bitcoin</th><th>Ethereum</th><th>Gold</th></tr></thead><tbody><tr><td>Constant</td><td>-0.232</td><td>-0.180</td><td>0.069</td></tr><tr><td>Coin<sub>t-1</sub></td><td>-0.016*</td><td>-0.014*</td><td></td></tr><tr><td>Gold<sub>t-1</sub></td><td></td><td></td><td>0.382***</td></tr><tr><td>Vladimir</td><td>0.0101</td><td>0.014</td><td>6.9210<sup>-05</sup></td></tr><tr><td>Ukraine – Russia</td><td>0.004</td><td>-0.001</td><td>-0.001</td></tr><tr><td>Ukraine – war</td><td>-0.001**</td><td>-0.010</td><td>-0.003</td></tr><tr><td>VIX<sub>t</sub></td><td>-0.066*</td><td>-0.110***</td><td>-0.006*</td></tr><tr><td>RVIX<sub>t</sub></td><td>-0.021</td><td>-0.031</td><td>0.002</td></tr><tr><td>Ukrainewar * Vladimir</td><td>-0.083</td><td>-0.109*</td><td>-0.022*</td></tr><tr><td>Ukrainewar * Ukraine – Russia</td><td>0.084</td><td>0.078</td><td>0.017</td></tr><tr><td>Ukrainewar * Ukraine – war</td><td>-0.077*</td><td>-0.046*</td><td>0.004</td></tr><tr><td>Ukrainewar * VIX<sub>t</sub></td><td>-0.257*</td><td>-0.267**</td><td>-0.107***</td></tr><tr><td>Ukrainewar * RVIX<sub>t</sub></td><td>0.005</td><td>-0.009</td><td>0.028</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.096</td><td>0.121</td><td>0.289</td></tr></tbody></table>

Note: Regression (OLS with Prais-Winsten robust estimator) results analyzing Crypto-assets and Gold as safe-havens based on Equations 5 and 6. Where $Coin_{t-1}$ is the Crypto-asset returns at day t-1, $Gold_{t-1}$ is Gold returns at day t-1, Wikipedia<sub>t</sub> is the volume of searches related to Ukraine-Russia war at day-t and Ukrainewar is a dummy variable that equals one if day-t is on the beginning of the war or the subsequent days and 0 otherwise. Levels of significance: *10%, **5%, **1%.

4. Conclusion

This article has empirically shown the impact of uncertainty, fear and financial market volatility induced by the in Ukraine on Bitcoin, Ethereum and Gold. It emerged from our analysis that these assets cannot be used as safe haven assets during this crisis. They are negatively and significantly correlated with war stress. This results could be useful for investors and policymakers during this crisis.
References

Baur, D. G., & Lucey, B. M. 2010. Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold. *Financial Review*, 45(2), 217–229.

Baur, D. G., & McDermott, T. K. 2010. Is gold a safe haven? International evidence. *Journal of Banking & Finance*, 34(8), 1886–1898.

Choi, S., & Shin, J. 2021. Bitcoin: An inflation hedge but not a safe haven. *Finance Research Letters*, 102379.

Dastgir, S., Demir, E., Downing, G., Gozgor, G., & Lau, C. K. M. 2019. The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test. *Finance Research Letters*, 28, 160–164.

Ding, Q., Huang, J., Gao, W., & Zhang, H. 2022. Does political risk matter for gold market fluctuations? A structural VAR analysis. *Research in International Business and Finance*, 60, 101618.

Kristoufek, L. 2013. BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. *Scientific Reports*, 3(1), 3415.

Luu Duc Huynh, T. 2020. The effect of uncertainty on the precious metals market: New insights from Transfer Entropy and Neural Network VAR. *Resources Policy*, 66, 101623.

Ruan, Q., Yang, H., Lv, D., & Zhang, S. 2018. Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA. *Physica A: Statistical Mechanics and Its Applications*, 503, 243–256.

Stensås, A., Nygaard, M. F., Kyaw, K., & Treepongkaruna, S. 2019. Can Bitcoin be a diversifier, hedge or safe haven tool? *Cogent Economics & Finance*, 7(1), 1593072.
Su, C.-W., Xi, Y., Tao, R., & Umar, M. 2022. Can Bitcoin be a safe haven in fear sentiment?
*Technological and Economic Development of Economy*, 28(2), 268–289.

Urquhart, A., & Zhang, H. 2019. Is Bitcoin a hedge or safe haven for currencies? An intraday
analysis. *International Review of Financial Analysis*, 63, 49–57.

Zhang, Z., Zhang, Y., Shen, D., & Zhang, W. 2018a. The Dynamic Cross-Correlations between Mass
Media News, New Media News, and Stock Returns. *Complexity*, 2018. Scopus.

Zhang, Z., Zhang, Y., Shen, D., & Zhang, W. 2018b. The cross-correlations between online sentiment
proxies: Evidence from Google Trends and Twitter. *Physica A: Statistical Mechanics and Its
Applications*, 508, 67–75.
# Appendix

<figure><img src="image_10.png" /><figcaption>Table 1: Descriptive statistics</figcaption></figure>

<table><thead><tr><th></th><th>BITCOIN</th><th>ETHEREUM</th><th>GOLD</th><th>UKRAIN WAR</th><th>UKRAIN- RUSSIA</th><th>VLADIMIR</th><th>Russian VIX</th><th>VIX</th></tr></thead><tbody><tr><td>Mean</td><td>47284.87</td><td>3520.942</td><td>1835.820</td><td>657.298</td><td>3582.269</td><td>31046.51</td><td>52.040</td><td>23.305</td></tr><tr><td>Median</td><td>44421.74</td><td>3397.431</td><td>1816.950</td><td>236</td><td>581</td><td>5160</td><td>36.405</td><td>21.580</td></tr><tr><td>Std. Dev.</td><td>8632.891</td><td>747.3142</td><td>59.075</td><td>856.8434</td><td>8730.055</td><td>63706.91</td><td>31.842</td><td>5.722</td></tr><tr><td>Skewness</td><td>0.727</td><td>0.082754</td><td>1.481</td><td>2.685268</td><td>5.877740</td><td>3.142</td><td>1.537</td><td>0.596</td></tr><tr><td>Kurtosis</td><td>2.402</td><td>1.547726</td><td>4.503</td><td>12.39373</td><td>46.88807</td><td>13.805</td><td>3.968</td><td>2.256</td></tr><tr><td>Jarque-Bera</td><td>13.809</td><td>11.92876</td><td>44.146</td><td>653.7231</td><td>11525.98</td><td>872.355</td><td>41.55399</td><td>7.900</td></tr><tr><td>Probability</td><td>0.001</td><td>0.002</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.0192</td></tr><tr><td>Observations</td><td>135</td><td>135</td><td>96</td><td>135</td><td>135</td><td>135</td><td>96</td><td>96</td></tr></tbody></table>
Université de
Bordeaux
Avenue Léon
Duguit, Bât.
H 33608
Pessac,
France

Tel : +33 (0)5.56.84.25.75

http://bse.u-bordeaux.fr/

Derniers numéros – Last issues

- 2022-06 **The impact of the Ukraine-Russia war on world stock market returns** by Whelsy BOUNGOU & Albonita YATIE

- 2022-05 Instability of preferences due to Covid-19 Crisis and emotions: a natural experiment from urban Burkina Faso by Delphine Boutin & Larène Petifour Haris MEGRAZI

- 2022-04 Intensification or Diversification: Responses by Anti Health-PassEntrepreneurs to French Government Announcements by Christophe LEVEQUE & Haris MEGRAZI

- 2022-03 De l'homo oeconomicus empathique à l'homo sympathicus Les apports de la sympathie smithienne à la compréhension des comportements prosociaux by Vanessa MICHEL (OLTRA)

- 2022-02 What drives the risk of European banks during crises? New evidence and insights by Ion LAPTEACRU

- 2022-01 The Impact of Technical Barriers to Trade and Sanitary and Phytosanitary Measures on Trade in the Forest-Wood-Paper Sector by Bossoma Doriane N'DOUA

- 2021-24 Viable and ecosystem-based management for tropical small-scale fisheries facing climate change by Helene GOMES & Luc DOYEN & Fabian BLANCHARD & Adrien LAGARDE

- 2021-23 Non-Practicing Entities in Europe: an Empirical Analysis of Patent Acquisitions at the European Patent Office by Valerio STERZI, Cecilia MARONERO, Gianluca ORSATTI, Andrea VEZZULLI

Ernest MIGUELEZ is the scientific coordinators of the Bordeaux Economics Working
Papers. The layout and distribution are provided by Cyril MESMER.