Bitcoin Futures Research Articles

Portfolio Diversification, Hedge and Safe-Haven Properties in Cryptocurrency Investments and Financial Economics: A Systematic Literature Review

Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge, and elaborated a systematic literature review along with a bibliometric analysis. Our results indicate a fast-growing literature evidencing cryptocurrencies’ ability to hedge against stocks, fiat currencies, geopolitical risks, and Economic Policy Uncertainty (EPU) risk; also, that cryptocurrencies present diversification and safe-haven properties; that stablecoins reveal unstable peg with the US dollar; that uncertainty is a determinant for cryptocurrency returns. Additionally, we show that investors should consider Gold, along with the European carbon market, CBOE Bitcoin futures, and crude oil to hedge against unexpected movements in the cryptocurrency market.

Using Crypto-Asset Pricing Methods to Build Technical Oscillators for Short-Term Bitcoin Trading

This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.

Infectious Diseases-Related Uncertainty and the Predictability of Foreign Exchange and Bitcoin Futures Realized Volatility

This paper examines the forecasting power of daily infectious disease-related uncertainty in predicting the realized volatility of nine foreign exchange futures and the Bitcoin futures series using the heterogeneous autoregressive realized variance model. Our results indicate that the infectious diseases-related uncertainty index plays a crucial role in predicting the future path of foreign exchange and Bitcoin futures realized volatility in all the selected time intervals. These findings have important implications for portfolio managers and investors during periods of high levels of uncertainty associated with infectious diseases.

Who trades bitcoin futures and why?

Using regulatory data with identifiers, we analyze the traders active in the Bit- coin futures (BTC) contracts traded on the Chicago Mercantile Exchange (CME). We find two primary trader types, those who hold almost exclusively BTC (con- centrated traders) and those who hold BTC to diversify a broader futures portfolio (diversified traders). The prevalence of these two types changes over time. We also study how BTC markets are connected to other futures markets through common holdings of BTC traders. Finally, we analyze the micro BTC contract and find that the trader composition is different than that of the full-size contract.

Bitcoin futures risk premia

AbstractThis paper presents an empirical analysis of bitcoin futures risk premia. Based on the relevant theories and empirical findings of commodity futures risk premia, we study a battery of predictors, including position‐based measures, market microstructure factors, and macroeconomic variables. We find that trading activity and extreme sentiment of speculators and retailers present significant predicting power on the subsequent bitcoin futures price changes over different time horizons. We also find evidence that the lower transaction cost, the higher bitcoin futures risk premiums. Regarding macroeconomic variables, financial conditions index, TED spread, US M2 money stock, and funding cost of financial institutions could predict bitcoin futures returns. The return impact of net position changes of hedgers is likely to be affected by extremes in macroeconomic variables. Speculators behave like negative feedback traders, while retailers are positive feedback traders. This detailed analysis of the risk premia of this emerging derivatives market provides critical implications.

Hedging with automatic liquidation and leverage selection on bitcoin futures

Bitcoin derivatives positions are maintained with a self-selected margin, which is often too low to avoid automatic liquidation by the exchange, without notice, especially during periods of excessive volatility. Indeed, according to CryptoQuant, almost $80 billion of positions on centralised exchanges were liquidated during 2021, that is an average of over $200 million per day. So hedgers of bitcoin price risk should account for the possibility of automatic liquidation when taking positions on bitcoin futures. We derive a semi-closed form for an optimal hedging strategy with dual objectives – to minimize both the variance of the hedged portfolio and the probability of liquidation due to insufficient collateral. The solution depends on the statistical characteristics of the spot and futures extreme returns, and other parameters that characterize the hedger by choice of leverage, loss aversion and collateral management. An empirical analysis based on minute-level data compares the performance of the major direct and inverse bitcoin hedging instruments traded on five major exchanges.

Price discovery in bitcoin spot or futures during the Covid-19 pandemic? Evidence from the time-varying parameter vector autoregressive model with stochastic volatility

ABSTRACT This paper examines price discovery between bitcoin spot and futures using static measures, namely information share (IS), component share (CS), modified information share (MIS), information leadership share (ILS), impulse response, and a time-varying parameter vector autoregressive (TVP-VAR) model with stochastic volatility and Markov Chain Monte Carlo (MCMC) sampling algorithm. Our one-minute and daily datasets cover 16 months before and 16 months during the Covid-19 pandemic (November 2018 to June 2021). Our IS, CS, MIS and impulse response results indicate a stronger bitcoin spot leadership, whereas our ILS results point to a weaker bitcoin futures dominance, during the Covid-19 pandemic. We construe this, as far as microstructure noise is concerned, as meaning that the bitcoin price is discovered in the spot market, and its dominance appears to have strengthened during the pandemic. However, as far as ‘pure speed’ is concerned, price discovery takes place in the bitcoin futures market, and its leadership seems to have weakened during the Covid-19 pandemic. The results of the time-varying measure (TVP-VAR) imply that, before the pandemic, price discovery took place within bitcoin futures but, during the pandemic, price discovery leadership has changed course, to occur within bitcoin spot.

Loaded for bear: Bitcoin private wallets, exchange reserves and prices

This study highlights a special feature of cryptocurrency trading and offers information about investor behavior that cannot be observed in traditional financial markets. We find that investors prefer holding bitcoin off exchanges in private wallets and primarily use exchanges to trade. Consequently, bitcoin exchange reserve changes resulting from the movement of bitcoin between private wallets and exchange accounts are negatively related to contemporaneous and future bitcoin returns. Specifically, the transfer of bitcoin on exchanges implies increased selling pressure and the transfer off exchanges implies decreased selling pressure. In turn, flows onto exchanges are triggered by large negative shocks and increased volatility.

Modeling the volatility of Bitcoin returns using Nonparametric GARCH models

Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models.

Informativeness of CME Micro Bitcoin Futures in Pricing of Bitcoin: Intraday Evidence

This study is the first to measure the informativeness of Chicago Mercantile Exchange(CME)-traded Micro Bitcoin futures (MBT) by exploring its contribution made to the price discovery process and realized volatility spillover of CME Bitcoin futures (BTC) and underlying Bitcoin spot markets. Hasbrouck's information share, and Lien and Shrestha's modified information share suggest that MBT contributes approximately equal or slightly greater proportion to price discovery with BTC. The dominance of MBT is marginally higher in Bitstamp and itBit compared to Coinbase and Kraken. The Diebold and Yilmaz index suggests bi-directional realized volatility spillover between CME futures and between Bitcoin and MBT.

Underreaction and overreaction in Bitcoin market

ABSTRACT Bitcoin market had a significant momentum phenomenon before the launch of Futures, and then it turned into an insignificant reversal effect. After Covid-19 appeared, the momentum effect and reversal effect disappeared. The advent of bitcoin futures has increased how investors respond to information. With the outbreak of COVID-19, investor interest in Bitcoin as a safe-haven asset has increased the effectiveness of the price. We estimate the speed of signal diffusion in the bitcoin market, and the results support that effective response to information is the essential mechanism for the disappearance of momentum effect.

Investigating the Efficiency of Bitcoin Futures in Price Discovery

The present study investigates the efficiency of the Bitcoin futures in the price discovery process by assessing the lead-lag relationship between the futures and spot prices of Bitcoin. The study tests whether the Bitcoin futures market is leading the price discovery mechanism for the Bitcoin spot market. The study considers daily closing prices of both Bitcoin spot and future indices from December 12, 2017 to December 31, 2020. The stationarity of the two time-series variables is tested using Augmented Dickey-Fuller test while the long-run co-integrating relationship is tested using Johansen Co-integration test. To test the long-run causality, the Error Correction Mechanism framework (ECM) is used while the Wald test is applied to assess the short-run causality between the Bitcoin future and spot prices. The results of trace and max-eigen statistics indicate that there is long term co-integrating relationship between Bitcoin futures and Bitcoin spot markets. The negative significant coefficient of error correction term indicates that there is long-run causality from the Bitcoin futures towards the Bitcoin spot market. The significant Chi-square test statistics of the Wald test suggest that there is short-run causality from the Bitcoin futures towards the Bitcoin spot market. This shows that the Bitcoin futures market is acting as a leading indicator and the Bitcoin spot market as a lagging indicator. Thus, it is concluded that the price discovery is taking place between Bitcoin futures and the Bitcoin spot market. With the entrance of the new information in the cryptocurrency market, it is first observed in the Bitcoin futures followed by the Bitcoin spot prices.

Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions

This paper investigates the directional causal relationship and information transmission among the returns of West Texas Intermediate (WTI), Brent, major cryptocurrencies, and stablecoins by drawing on daily data from July 2019 to July 2020. Applying effective transfer entropy, a non-parametric statistic, the results show that the direction of the causal relationship and the nature of information spillovers changed after the COVID-19 pandemic. More precisely, our findings reveal that WTI and Brent are leading the prices of Bitcoin and Bitcoin Cash. Conversely, Bitcoin futures and stablecoins (TrueUSD and USD Coin) are leading WTI and Brent prices. In addition, the stablecoin Tether became a leader against Brent prices after the pandemic, although it is still following WTI prices. Moreover, Ethereum and USD coin preserved their position as leaders against Brent prices. Interestingly, our results also reveal that Ethereum, Litecoin, and Ripple preserved their position as leaders of WTI prices. The change in the nature of directional causality and the spillover effect after the COVID-19 crisis provide valuable information for practitioners, investors, and policymakers on how the ongoing pandemic influences the connection and network correlation among the energy, cryptocurrency, and stablecoin markets.

Trading behavior in bitcoin futures: Following the “smart money”

AbstractThe Bitcoin futures market has grown rapidly since its 2017 introduction. Along with enabling institutional traders to access a regulated cryptocurrency product, futures provide a means to improve market efficiency by shorting Bitcoin. We examine trading behavior in Bitcoin futures utilizing the Commodity Futures Trading Commission Commitment of Traders report. Leveraged money traders tend to hold the largest positions, be net short, and their trading behavior plays a key role in the Bitcoin futures market. Our empirical results show that leveraged money traders display market timing ability, largely by adjusting their short positions. It seems that other trader types follow this “smart money” in adjusting their own positions in subsequent periods. We also demonstrate that it is possible to construct profitable trading strategies based on observed variations in leveraged money positions.

The profitability of Bollinger Bands trading bitcoin futures

ABSTRACT We explore whether investors would receive excess profits by round-turn trading (hereafter referred to as trading) Bitcoin futures based on Bollinger Bands trading strategy (BBTS). Since investors are suggested to first buy (then sell) Bitcoin futures as oversold (overbought) signals emitted by the BBTS (i.e. penetrating lower (upper) Bollinger Bands regarded as a buying (selling) signal), we aim to explore whether investors would have better returns by trading such futures according to the BBTS. Results show that the average holding period return (AHPR) is over 20% for trading Bitcoin futures following the BBTS. Furthermore, after we adjust the 60-day moving average (MA) instead of the 20-day MA for the BBTS, the AHPR is above 50%. It is noted that if the margin could be deemed as an investment amount, its rate of return would be much higher than the 50% for trading Bitcoin futures.

Does Bitcoin futures trading reduce the normal and jump volatility in the spot market? Evidence from GARCH-jump models

This paper investigates how Bitcoin futures introduction affects Bitcoin's normal and jump volatility over time. Using GARCH-jump models, we find Bitcoin's normal and jump volatility increase in the short run, move in opposing directions in the mid run, and decrease in the long run. Besides, we examine whether futures trading activity, proxied by unexpected trading volume and open interest, is associated with Bitcoin volatility. We document that Bitcoin's normal and jump volatility covary positively with unexpected trading volume in the short and mid run. Meanwhile, both volatility covary positively (negatively) with unexpected open interest in the short (mid) run.

Arbitrage, contract design, and market structure in Bitcoin futures markets

AbstractPerpetual futures, first proposed by Shiller (1993), have only seen wide use in cryptocurrency markets. We examine the contract design and market microstructure differences for the behavior of Bitcoin quarterly and perpetual futures prices and assess the implications for market participants and policymakers. We find perpetual futures exhibit multiple “u‐shaped” curves, seasonal effects, and opening effects despite lacking opening and closing hours. There is suggestive evidence of spillover effects between perpetual and quarterly futures contracts. We find quarterly futures offer cash‐and‐carry arbitrage opportunities, but similar to Hattori and Ishida (2021) these opportunities primarily exist during market dislocations.

The Covid 19 Crisis and The Future of Bitcoin in E-Commerce

The spread of the coronavirus has devalued stock indices, lowered commodity prices such as oil, and chaos in financial markets around the world. Fiat currencies have plummeted as central banks injected too much money into affected economies, and an unprecedented recession has hit the world that central banks and financial institutions are unable to cope with. Bitcoin is a virtual currency whose production is the result of a time-consuming computational activity called exploration. This internati

Modelling and forecasting the volatility of bitcoin futures: the role of distributional assumption in GARCH models

<abstract><p>The Bitcoin futures market is growing and, as such, becoming more sophisticated. A small change in price may therefore have a large impact on the market. This paper investigates the propensity of 18 different competing GARCH family models and error distributions to model and forecast the volatility of Bitcoin futures returns. The study employs two different time periods (from January 2, 2018 to June 14, 2021; and March 11, 2020 to June 14, 2021). From the results, iGARCH(1, 1)-Students't-distribution (STD) is selected as the best performing model among the constructed models for the first period. By fitting the best three models from the first period to the second period, the iGARCH(1, 1)-STD is again selected as the optimal model. However, the iGARCH(1, 1)-normal inverse Gaussian (NIG) provides a significant variance forecast when used for in-sample and out-of-sample forecasts before the financial crisis and during the financial crisis, respectively. Our results indicate the impacts of past squared shocks on squared returns of Bitcoin futures and the ability of iGARCH(1, 1)-STD to capture such innovations and the propensity of iGARCH(1, 1)-NIG to optimally forecast the variance of Bitcoin futures returns.</p></abstract>

Uncovering the impact of regulated Bitcoin futures on volatility and volume

Uncovering the impact of regulated Bitcoin futures on volatility and volume