The predictive power of macroeconomic uncertainty for commodity futures volatility

• Published date: 01 September 2021
• Author: Zhuo Huang,Fang Liang,Chen Tong
• Date: 01 September 2021
• DOI: http://doi.org/10.1111/irfi.12310

ORIGINAL ARTICLE

The predictive power of macroeconomic

uncertainty for commodity futures volatility

Zhuo Huang

1

| Fang Liang

1,2

| Chen Tong

1

1

National School of Development, Peking

University, Beijing, China

2

International School of Business and Finance,

Sun Yat-sen University, China

Correspondence

Chen Tong, National School of Development,

Peking University, Beijing, China.

Email: tongchen@pku.edu.cn

Funding information

National Natural Science Foundation of China,

Grant/Award Number: 71871060, 71671004

Abstract

We investigate whether and to what extent macroeconomic

uncertainty predicts the volatility of commodity futures. By

examining 26 commodities in six categories, we find that

the measure of aggregate macroeconomic uncertainty

based on a large dataset has a significant predictive effect

for commodity volatility. The predictive relationship holds

both in-sample and out-of-sample after controlling for

lagged volatility. The extent of the predictability differs by

commodity category, with energy, precious metals, and

industrial metals futures having the most significant effect.

For all commodities, the predictive power of macroeco-

nomic uncertainty is stronger in more recent data and dur-

ing recessions.

KEYWORDS

commodity futures, macroeconomic uncertainty, realized

volatility, volatility forecasting

JEL CLASSIFICATION

C58; G17; Q02

1|INTRODUCTION

Since the financialization of the commodity markets in the early 2000s, commodity futures have become as a popu-

lar asset class for speculation and asset management (Basak & Pavlova, 2016; Christoffersen, Lunde, &

Olesen, 2014; Tang & Xiong, 2012). This prompts interest in the predictability of commodity futures volatility

because commodity volatility is a determinant of portfolio allocation (Büyüks¸ahin & Robe, 2014; Singleton, 2014),

Received: 23 May 2018 Revised: 20 February 2020 Accepted: 10 March 2020

DOI: 10.1111/irfi.12310

© 2020 International Review of Finance Ltd. 2020

International Review of Finance. 2021;21:989–1012. wileyonlinelibrary.com/journal/irfi 989

risk management (Conover, Jensen, Johnson, & Mercer, 2010), derivatives pricing (Cortazar, Gutierrez, &

Ortega, 2016), and core macroeconomic quantities (Kilian, 2009).

In this article, we investigate whether macroeconomic uncertainty can predict commodity volatility. Macroeco-

nomic uncertainty is a type of economic uncertainty closely related to macroeconomic activity. Understanding the

link between the aggregate economy and commodity volatility is an important empirical question in finance, espe-

cially for risk management and asset pricing.

Our study contributes to two strands of literature. The first strand is the literature on commodity volatility pre-

dictability based on macroeconomic variables. Christiansen, Schmeling, and Schrimpf (2012) show that the interest

rate is a useful predictor when forecasting the volatility of the Standard & Poor's GSCI commodity index.

Hammoudeh and Yuan (2008) find that rising interest rates tend to reduce the volatility of gold, silver, and copper.

Foreign exchange variables also play an important role in influencing commodity volatility. For example, the

U.S. dollar affects gold volatility (Tully & Lucey, 2007), and the exchange rates of small commodity exporters have

predictive content for global commodity prices, both in-sample and out-of-sample (Chen, Rogoff, & Rossi, 2010).

Shang, Yuan, and Huang (2016) claim that foreign exchange rates affect commodity futures and also contain infor-

mation on future movements in commodity markets. Variables relevant to inflation and industrial production also

affect commodity returns and volatility. For example, Bailey and Chan (1993) provide evidence that inflation and

industrial production lead to risk premium in commodity markets. However, macroeconomic uncertainty, which

always plays as the trigger of macroeconomic fundamentals in the business cycle, is far less studied for predicting

commodity volatility. Furthermore, as the risk–return relationship in commodity markets is widely documented (see

Bakshi, Gao, & Rossi, 2019; Basu & Miffre, 2013; Fernandez-Perez, Frijins, Fuertes, & Miffre, 2018; Han, Mo, Zhi, &

Zhu, 2019), our paper can also provide some insights on macro risk factors in commodity futures markets from the

perspective of macroeconomic uncertainty.

Second, our paper enriches the literature on the impact of economic uncertainty on commodity markets. S&P

500 turnover, a common proxy for the dispersion of opinion (Scheinkman & Xiong, 2003) and therefore a potential

indicator of uncertainty about future market valuations, is a useful predictor of commodity volatility (Christiansen

et al., 2012). Liu, Han, and Yin (2018) use news-implied volatility (NVIX) as a proxy for news uncertainty. They show

that NVIX affects the volatility of nonenergy futures and that stock market indexes (SMIs) affects both energy and

nonenergy futures. Gospodinov and Jamali (2018) measure monetary policy uncertainty using the realized volatility

of Eurodollar futures fronts and find that the uncertainty associated with negative monetary policy shocks drives

down commodity prices. The economic policy uncertainty (EPU) index proposed by Baker, Bloom, and Davis (2016)

has a predictive power for commodity returns (Reboredo & Wen, 2015; Wang, Zhang, Diao, & Wu, 2015). Yin and

Han (2014) find that the relationship between the EPU index and commodity prices varies over time, with a large

change occurring after the 2008–2009 global financial crisis. Antonakakis, Chatziantoniou, and Filis (2014) find a

negative relationship between the EPU index and oil price shocks. The GEPU index, proposed by Davis (2016) and

based on the EPU index, measures a GDP-weighted average of national EPU. Fang, Chen, Yu, and Qian (2018) show

that the GEPU index has a significant and positive effect on gold volatility both statistically and economically. Most

studies on the impact of macroeconomic uncertainty on commodity futures markets focus on return predictability

(Tan & Ma, 2017). However, few studies investigate the ability of macroeconomic uncertainty to predict commodity

volatility.

1

In addition, the out-of-sample forecasting performance of macroeconomic uncertainty is generallyignored

in the literature. Our study fills this gap.

There are several channels through which macroeconomic uncertainty can drive commodity volatility. In general,

macroeconomic uncertainty plays an important role in driving business cycles (Bloom, Floetotto, Jaimovich, &

Saporta-Eksten, 2018). It is natural to conjecture that macroeconomic uncertainty has useful information for

predicting the future volatility of commodity futures, because many commodities are closely related to industrial pro-

duction and their prices are vulnerable to macroeconomic variations. The channels of impact vary by commodity cat-

egory. For industrial inputs, price fluctuations are mainly driven by demand shocks triggered by uncertainty about

the future. Take crude oil as an example, precautionary demand shocks caused by uncertainty drive price fluctuations

990 HUANG ET AL.