Trading activity and price discovery in Bitcoin futures markets

doi:10.1016/j.jempfin.2021.03.001

Journal of Empirical Finance

Volume 62, June 2021, Pages 107-120

https://doi.org/10.1016/j.jempfin.2021.03.001 Get rights and content

Highlights

•
Hedgers promote the market efficiency of Bitcoin futures.
•
Retailers deteriorate the market efficiency of Bitcoin futures.
•
Speculators’ trading has opposite effects on the CME and CBOE Bitcoin futures.
•
Impacts of speculators/retailers on BTC remain constant after the delisting of XBT.
•
CME’s Bitcoin futures exhibit superior price discovery than CBOE’s.

Abstract

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.

Introduction

Cryptocurrency brings an intriguing aspect to financial markets around the globe in the past decade, as a result of the development of the financial technology (FinTech) in general and blockchain in particular. Introduced in 2008, the Bitcoin is currently the most successful/prominent of over 2400 cryptocurrencies, receiving much attention from not only regulators but also academic researchers and market practitioners. Although the Bitcoin’s dominance¹ in the market has dropped from over 90% in the early period to about 65.82% recently, it remains the most popular virtual currency on the cryptocurrency market followed by Ethereum (7.74%) and Ripple (4.23%).²

The Chicago Board Options Exchange (CBOE) and Chicago Mercantile Exchange (CME) successively launched Bitcoin futures in December 2017, which has inspired researchers to investigate the effects of the introduction of Bitcoin futures on spot markets (e.g., Köchling et al., 2019, Kim et al., 2020, Liu et al., 2020), the price discovery leadership between futures and spot markets (Corbet et al., 2018a, Baur and Dimpfl, 2019, Kapar and Olmo, 2019, Akyildirim et al., 2020), hedging properties of Bitcoin futures (Sebastião and Godinho, 2020), and the determinants of price discovery on Bitcoin markets (Entrop et al., 2020). The advent of futures contracts facilitates institutional investors to trade and hedge Bitcoin futures on regulated exchanges for the first time and broadens the base of market participants. Studying trading activities of different types of traders in Bitcoin futures markets, a topic that is yet to be explored, is critical for enhancing our understanding of the price discovery process in this market. Moreover, it is of interest to explore the informational competition of price discovery between the two futures exchanges, which could shed light on the CBOE’s decision to delist Bitcoin futures on March 14, 2019.

We contribute to the extant literature in two major aspects. First, we investigate the impact of trading activities by different types of traders on the price discovery in Bitcoin futures markets using the Commitments of Traders (COT) data from the Commodity Futures Trading Commission (CFTC). We find that trades of hedgers are positively associated with the modified information shares (MIS) in both CME and CBOE Bitcoin futures markets, suggesting that they promote market efficiency. In contrast, retailers’ trading activity that relates negatively to the price discovery of the CME Bitcoin futures destabilizes the market. Speculators, the major participants in the market, exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Second, we examine the informational competition of price discovery between the CME and CBOE Bitcoin futures contracts. Our finding that CME’s contracts exhibit superior competitiveness in price discovery relative to CBOE’s and the observation that trading volume in the CBOE has decreased substantially in early 2019 provide plausible justification for CBOE’s decision in March 2019 to abandon its Bitcoin futures once the last contract expired in June 2019.

The remainder of this paper is organized as follows. Section 2 discusses the related literature and details our research objectives. Section 3 describes the data and methodology. Section 4 presents the empirical results, including the price discovery in Bitcoin futures and spot markets, and the contribution of trading activity by type of traders to the price discovery of Bitcoin futures. Section 5 concludes.

Section snippets

Price discovery and trading activities in futures markets

Price discovery or lead–lag relationship between spot and futures markets is a crucial issue in finance because it relates directly to market efficiency. It has long been recognized in the finance literature that futures markets lead spot markets and contribute more to the price discovery process due to futures markets’ inherent features, such as high leverage, low transaction costs, and few short-sale constraints (e.g., (Kawaller et al., 1987, Stoll and Whaley, 1990, Chan, 1992, Dwyer et al.,

Data description

We use Bitcoin spot trading data (denominated in US dollars) with 1-min frequency from the Bitstamp exchange due to its long trading history, leading role in the cryptocurrency market, and good liquidity. This dataset, which is readily available from the Bitcoincharts.com website, is also adopted by Feng et al. (2018), Urquhart (2018), and Baur and Dimpfl (2019). We obtain the 1-min transaction prices for the CBOE (Ticker: XBT) and CME (Ticker: BTC) futures contract from tickdata.com (according

Measurement of price discovery in Bitcoin futures markets

Before estimating MIS of spot and futures, we consider the issue of sampling frequency because market microstructure noise might likely impede the robustness of price discovery measurement. Table 3 reports the autocorrelation functions (ACF) of Bitcoin futures returns at various sampling frequencies. For both BTC and XBT return series, the ACF( $\cdot$ ) in absolute value based on 15-min frequency is the lowest for most cases, which justifies our use of the 15-min frequency data to analyze the

Conclusions

Because of the uncertainty of global stock markets, many funds and asset managers turn to safe haven or alternative assets, including Bitcoin, to form diversified portfolios. The strong demand may have caused the recent rebound in the spot price of Bitcoin. If investors concur that Bitcoin could be a hedge asset against stocks and increase their holding positions, it is of importance to understand whether the Bitcoin futures markets can attract institutional/informed investors and to analyze

CRediT authorship contribution statement

Jui-Cheng Hung: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing - original draft. Hung-Chun Liu: Conceptualization, Data curation, Formal analysis, Investigation, Validation, Writing - original draft. J. Jimmy Yang: Conceptualization, Formal analysis, Methodology, Supervision, Validation, Writing - review & editing.

Acknowledgment

We would like to thank Rossen Valkanov (the editor) and an anonymous referee for insightful comments and suggestions.

References (57)

AkyildirimE. et al.
The development of bitcoin futures: Exploring the interactions between cryptocurrency derivatives
Finance Res. Lett.
(2020)
BalcilarM. et al.
Can volume predict bitcoin returns and volatility? A quantiles-based approach
Econ. Model.
(2017)
BaurD.G. et al.
Bitcoin: Medium of exchange or speculative assets?
J. Int. Finan. Mark. Inst. Money
(2018)
BouriE. et al.
Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles
Q. Rev. Econ. Finance
(2018)
BouriE. et al.
Trading volume and the predictability of return and volatility in the cryptocurrency market
Finance Res. Lett.
(2019)
BouriE. et al.
Cryptocurrencies and the downside risk in equity investments
Finance Res. Lett.
(2020)
BouriE. et al.
On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier?
Finance Res. Lett.
(2017)
ChangY.K. et al.
The effectiveness of position limits: Evidence from the foreign exchange futures markets
J. Bank. Financ.
(2013)
ChenY.L. et al.
Investor structure and the informational efficiency of commodity futures prices
Int. Rev. Financ. Anal.
(2015)
ChenY.L. et al.
News announcements and price discovery in foreign exchange spot and futures markets
J. Bank. Financ.
(2010)

Cited by (24)

The Bitcoin volume-volatility relationship: A high frequency analysis of futures and spot exchanges
2024, International Review of Financial Analysis
We examine the volume-volatility relationship across Bitcoin futures and spot markets, using daily realised volatility measures estimated from high frequency intraday data. We estimate realised spot volatility across five major exchanges using both the standard volume weighted price and using a new approach, inspired by the CME Bitcoin Reference Rate methodology. We find that unexpected trading volume is the most important explanatory variable for BRR spot volatility, explaining 20% of variation in price volatility at exchange level. Conversely, we find that both expected and unexpected CME Bitcoin futures volumes play a very limited or even calming role in systemic volatility. Our findings suggest that CME Bitcoin futures are not independently contributing to systemic risk in Bitcoin over the period studied.
Arbitrage opportunities and feedback trading in regulated bitcoin futures market: An intraday analysis
2024, International Review of Economics and Finance
The study uses 5-min intraday Bitcoin futures and spot prices to investigate the additive and multiplicative impact of arbitrage opportunities on feedback trading in the Bitcoin futures market. The basis, equal to the difference between Bitcoin spot and futures prices, is used to proxy arbitrage opportunities. The study uses quantile regressions to capture different market conditions. The study documents episodic cases of positive and negative feedback trading. The resulting evidence reveals that the intensity and type of feedback trading largely depend on market conditions and the degree of arbitrage opportunities. From the multiplicative model, the impact of arbitrage opportunities on positive feedback trading is strongest in the extreme bear market, lowest in the regular market conditions, and absent in the bull market conditions. Channel “breakouts” analysis indicates positive feedback traders dominate during bear and normal market conditions. When the basis declines by varying magnitudes across quantiles, positive feedback trading shifts to negative feedback trading. The findings suggest widening basis intensifies negative feedback trading during bull market conditions. However, when the basis decreases by varying scales across upper quantiles, negative feedback trading switches to positive feedback trading. By observing the trading signals, investors can adjust their portfolio positions to maximize returns and minimize risk.
Futures trading activity and the jump risk of spot market: Evidence from the bitcoin market
2023, Pacific Basin Finance Journal
This paper examines the impacts of Bitcoin futures trading on the jump risk of spot market. Based on 5-min high-frequency data, we use a nonparametric method to detect Lévy-type jumps in Bitcoin and document that Bitcoin prices are subject to both big and small jumps, and the jump risk – captured by jump intensity and jump size– is time varying. We then investigate the changes of the jump risk before and after the Bitcoin futures introduction and find that both the jump intensity and jump size of big and small jumps have decreased, yet the change of the latter is insignificant. Furthermore, we examine whether greater futures trading activity, proxied by unexpected trading volume and open interest, is associated with greater jump risk in the spot market. We document that jump risk Granger-causes futures speculative trading activity while the reverse is not true, and there is no causality between futures hedging activity and jump risk.
The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns
2023, International Review of Financial Analysis
The impact of Bitcoin futures introduction on the underlying Bitcoin volatility has been a controversial topic. Conflicting results had been obtained from different sample periods and methodologies. To address this debate, this study examines the impact of futures trading on volatility and volatility asymmetry of Bitcoin returns in the short and long run. Using exponential GARCH models, we introduce a dummy in the variance equation to capture the changes in the volatility after the introduction of Bitcoin futures. We find that after the introduction, spot return volatility decreases in the short run, but increases in the long run. Besides, in the short run, there exists an inverse leverage effect before and after the introduction; in the long run, the inverse leverage effect before the introduction changes to a usual level effect after the introduction. Finally, we examine whether greater futures trading activity, proxied by trading volume and open interest, is associated with greater Bitcoin volatility. To do so, we decompose each proxy into expected and unexpected components and document that, in the long run, Bitcoin volatility covaries positively with unexpected futures trading volume, but negatively with unexpected futures open interest.
Do connections pay off in the bitcoin market?
2022, Journal of Empirical Finance
Citation Excerpt :
This result aligns with the survey result conducted by Mahomed et al. (2018) in South Africa. Hung et al. (2021) show that different kinds of investors may exert opposite effects on price discovery in the bitcoin future market. Using the Mt. Gox exchange trading data, Gandal et al. (2018) and Chen et al. (2019) demonstrate that there are market manipulations inside the Mt. Gox exchange which cause unprecedented spikes in bitcoin price.
This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and 2020. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most-connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.
Price discovery in fiat currency and cryptocurrency markets
2022, Finance Research Letters
We study the dynamic relations between the direct exchange rate of fiat currencies and the rate implied by bitcoin prices. The empirical results show the deviation between actual and implied rates of EUR/USD, GBP/USD, and JPY/USD affects the movements of actual and BTC-implied rates. We observe that implied rates contribute more to price discovery than actual rates before 2019 for EUR/USD and before 2018 for JPY/USD. Triangular arbitrage opportunities arise when VIX is high. The arbitrage opportunities are also related to the market capitalization of bitcoin and the trading of bitcoin futures on CBOE and CME.

View all citing articles on Scopus

View full text

Trading activity and price discovery in Bitcoin futures markets

Highlights

Abstract

Introduction

Section snippets

Price discovery and trading activities in futures markets

Data description

Measurement of price discovery in Bitcoin futures markets

Conclusions

CRediT authorship contribution statement

Acknowledgment

Finance Res. Lett.

Econ. Model.

J. Int. Finan. Mark. Inst. Money

Q. Rev. Econ. Finance

Finance Res. Lett.

Finance Res. Lett.

Finance Res. Lett.

J. Bank. Financ.

Int. Rev. Financ. Anal.

J. Bank. Financ.

J. Empir. Financ.

Econom. Lett.

Econom. Lett.

Finance Res. Lett.

J. Commod. Mark.

Econom. Lett.

Finance Res. Lett.

Res. Int. Bus. Finance

J. Empir. Financ.

Int. Rev. Financ. Anal.

Econom. Lett.

Finance Res. Lett.

Finance Res. Lett.

J. Bank. Financ.

Finance Res. Lett.

J. Bank. Financ.

Energy Econ.

Finance Res. Lett.