The market quality of commodity futures markets

• Author: Yuchi Xie,Qingfu Liu,Yiuman Tse,Qian Luo
• Date: 01 November 2020
• Published date: 01 November 2020
• DOI: http://doi.org/10.1002/fut.22115

J Futures Markets. 2020;40:1751–1766. wileyonlinelibrary.com/journal/fut © 2020 Wiley Periodicals, Inc.

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1751

Received: 25 February 2020

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Accepted: 2 March 2020

DOI: 10.1002/fut.22115

RESEARCH ARTICLE

The market quality of commodity futures markets

Qingfu Liu

1

|Qian Luo

1

|Yiuman Tse

2

|Yuchi Xie

3

1

Institute for Financial Studies, Fudan

University, Shanghai, China

2

College of Business Administration,

University of Missouri–St. Louis, St. Louis,

Missouri

3

Fanhai International School of Finance,

Fudan University, Shanghai, China

Correspondence

Yiuman Tse, College of Business

Administration, University of Missouri–St.

Louis, St. Louis 63105, Missouri.

Email: tseyi@umsl.edu

Abstract

To study the market quality of commodity futures markets, we construct a

commodity futures market quality index from the perspective of liquidity,

efficiency, and volatility. Based on the market quality index, the Chinese

commodity futures market operates steadily. The metal futures market is more

efficient and stable than the market for agricultural futures. The Chinese

commodity futures market is less liquid and more volatile than the U.S.

market. We examine the determinants of market quality and find that mac-

roeconomic variables and futures market contracts are significantly related to

the market quality of Chinese commodity futures.

KEYWORDS

commodity futures, efficiency, liquidity, market quality index, volatility

JEL CLASSIFICATION

G1; G12; G14

1|INTRODUCTION

Over the past 20 years, the Chinese commodity futures market has rapidly grown. For example, the trading volume on

the Chinese commodity futures market in 2017 totaled 350 billion contracts. This market plays an important role in

price discovery and hedging the risks of commodities, which advances its development. The Chinese futures market

depends on government support much more than the U.S. futures market. The government implements many policies

to help ensure that the Chinese futures market develops in a healthy way. In 2004 the State Council (Cabinet) issued

guidance on opening up and reform in the financial market. This guidance emphasizes the importance of commodity

futures markets in servicing the real economy and the crucial role of commodity futures markets in establishing a

financial futures market. At the same time, the Chinese central government has issued documents that emphasize the

important role of a commodity futures market in the country's financial system. Supported by these government

policies, more commodities started to be traded on the Chinese commodity futures market.

A commodity futures market quality index is useful for evaluating the market quality of the Chinese commodity

futures market, but to date, few papers have constructed such an index. Silber (2010) evaluates the function of futures

markets with the trading volume and trading duration of futures contracts. Glen (1994) and Kuo and Li (2011) evaluate

futures markets from the perspective of market efficiency, liquidity, trading cost, and volatility. Pagano and Röell

(1996), Boehmer, Saar, and Yu (2005), Bessembinder, Maxwell, and Venkataraman (2006), and Frutos and Manzano

(2014) consider transparency an important indicator of the function of futures markets. In general, market liquidity,

efficiency, volatility, trading cost, and transparency are the crucial factors on which to focus in evaluating the market

quality of commodity futures markets.

Some papers measure market liquidity using market trading information. In a stock market, liquidity is seen as closely

related to asset prices, expected returns, turnover, and trading volume (Amihud, Mendelson & Lauterbach, 1997;

O'Hara, 1995). Haugom and Ray (2017) measure the liquidity of oil futures using the daily trading volume on futures

markets. Kalaitzoglou and Ibrahim (2015) measure the liquidity of the European carbon futures market with the conditional

weighted trading volume. Cai, Hudson, and Keasey (2004), Bortoli, Frino and Jarecic (2010),andLiu,Hua,andAn(2016)

use price, trading volume, and duration to measure liquidity, with both low‐frequency and high‐frequency data. Roll (1984)

measures liquidity using the covariance of price fluctuation. Amihud (2002) measures market liquidity with expected returns

and the ratio of expected returns to the amount traded. In general, the liquidity of futures markets is measured by trading

price and volume.

The market efficiency of futures markets is widely examined in many papers, in three strands of the literature. One

strand tests the random‐walk hypothesis of futures prices. Previous studies reject this hypothesis and find that the series of

commodity futures prices move in a systematic, as opposed to random, manner. For example, Wei and Leuthold (2000)find

long memory structures in the agricultural futures prices. The second strand of the literature tests whether hedging

opportunities exist on futures markets. For example, Crowder and Hamid (1993) test hedging opportunities on oil futures

markets using cointegration analysis and find no hedging opportunities. Therefore, they conclude that futures markets are

efficient. The third strand of the literature tests the linkage between futures and spot markets. For example, Lean, McAleer,

and Wong (2010) use mean variance and random dominance to test the linkage between American oil futures and spot

markets. They find no linkage between them, supporting the market efficiency hypothesis.

Many papers examine volatility on futures markets as part of evaluating their market quality. Volatility is used to

reflect the frequency and extent of asset price fluctuation. Compared to stock markets, futures markets have relatively

high volatility. Christiansen, Schmeling, and Schrimpf (2012) attribute the high volatility on futures markets to high

fluctuation, whereas Haugom and Ray (2017) attribute the high volatility on futures markets to a high leverage ratio.

Andersen, Bollerslev, Diebold, and Labys (2003) employ high‐frequency data to construct volatility based on in-

formation from a historical data set and take autocorrelation and heterogeneity into account.

The transaction cost is examined in many papers to evaluate the market quality of futures markets. Glen (1994)

defines transaction cost using fixed costs and the bid‐ask spread. Perold (1988) argues that the bid‐ask spread is a

hidden cost for market participants, which is the difference between the executed price and the equilibrium price. In

practice, the transaction cost includes the bid‐ask spread and the commission. Roll (1984) argues that moment esti-

mation can be used to measure the bid‐ask spread. Hasbrouck (2009) employs the Gibbs random sampling technology

to measure the transaction cost. Bleaney and Li (2016) use serial correlation of prices to measure the transaction cost.

Market transparency is also studied in many papers to evaluate the market quality of futures markets. O'Hara (1995)

argues that market transparency reveals transaction information, which includes the transaction price, volume, ask

price, order flow, and market participants. Chowdhry and Nanda (1991) argue that market makers are willing to reveal

information to stop inside trading and encourage outside trading. Transparency is more difficult to measure than

liquidity, efficiency, volatility, and transaction cost because of the difficulty in quantifying it. Bloomfield and O'Hara

(2000) simulate real transactions to test how transparency affects market transactions. Hendershott and Jones (2005)

study the impact of a suspension of information announcements on transactions by regulatory authorities. Boehmer

et al. (2005) study the impact of the OpenBook service on the New York Stock Exchange on announcements of

information. Unlike the measurement of stock market transparency, measuring the transparency of futures markets is

the topic of few papers. Bohl, Salm, and Schuppli (2011) report that futures markets provide information on the spot

market only when market participants are dominated by institutional investors.

Many papers focus on one specific indicator to evaluate the market quality of futures markets. No papers to date have

discussed how to construct an index on futures market quality that takes into account different scenarios to evaluate the market

quality of the Chinese and U.S. futures markets. This study of the market quality of the commodity futures markets deepens

our understanding of the Chinese futures market and enables it to be comparable to futures markets in other countries.

To construct such an index, we take liquidity, efficiency, and volatility into account to evaluate the market quality of

commodity futures markets. We analyze the Chinese and U.S. commodity futures markets using this index. Finally, we

conduct an empirical test to examine the determinants of the market quality of the commodity futures markets. The

results show that the market quality of Chinese commodity futures market is stable during the sample period of

May 2012–June 2018. The market quality of metal futures is moderately better and more stable than that of agricultural

futures. The results also show that the U.S. commodity futures market has higher market quality and is less volatile

than the Chinese market. The empirical results show that macroeconomic variables and futures contracts are important

determinants of the market quality of commodity futures.

This paper is organized as follows: Section 2defines and measures a futures market quality index from the

perspective of liquidity, efficiency, and volatility. Section 3describes the data and analyzes the Chinese and U.S.

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