Bitcoin Futures Research Articles

Are bitcoin futures contracts for hedging or speculation?

The emerging interest in Bitcoin futures market has led to questions on its trading form and contribution to risk minimization. These questions are important for market participants, including hedgers and speculators. This paper addresses the possible trading motive in Bitcoin futures market in being speculation or hedging. The author first tests a model relating Bitcoin futures returns with trading volume and conditional volatility, estimated with a GJR-GARCH specification, on a full sample of daily futures prices. A robustness check is then conducted by investigating the hedging effectiveness of Bitcoin futures and the speculation-hedging ratios on individual Bitcoin futures contracts. The estimation results on Bitcoin futures contracts, spanning from December 2017 to February 2020, show a significant positive relationship between futures returns and lagged volume. The speculation-hedging measures used for Bitcoin futures contracts maturing in March, June, September, and December reveal an increasing demand for speculation. Also, the Bitcoin spot’s full-hedge and OLS-hedge strategies with Bitcoin futures provide no gain over a no-hedge strategy. The results reveal strong evidence that traders in the Bitcoin futures market are motivated by speculation rather than hedging. This further puts in evidence the existence of asymmetric information within informed traders in Bitcoin futures market, and therefore market participants would not insure their positions against Bitcoin price movements.

Diversifying with Cryptocurrencies during COVID-19

Literature suggests assets become more correlated during economic down- turns. The current COVID-19 crisis provides an unprecedented opportunity to investigate this considerably further. Further, whether cryptocurrencies provide a diversification for equities is still an unsettled issue. Additionally, the question of whether cryptocurrency futures are safe havens has received very little attention. We employ several econometric procedures, including wavelet coherence, copula principal component, and neural network analyses to rigorously examine the role of COVID-19 on the paired co-movements of six cryptocurrencies, as well as bitcoin futures, with fourteen equity indices and the VIX. We nd co-movements between cryptocurrencies and equity indices gradually increased as COVID-19 progressed. However, most of these co-movements are positively correlated, suggesting that cryptocurrencies do not provide a diversification benefit during downturns. Exceptions, however, are the co-movements of bitcoin futures and tether being negative with equities. Results are consistent with investment vehicles that attract either more informed or more speculative investors dierentiating themselves as safe havens.

Cryptocurrency Returns before and after the Introduction of Bitcoin Futures

This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and post periods. Besides the neighbourhood of the break time, the current period is also investigated as returns start to recover after some time. Search intensity is observed to be the most important variable for Bitcoin for all periods, whereas for the other cryptocurrencies there are other variables that seem more important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest that search intensity increases the volatility of Bitcoin returns more in the post period than it does in the pre period. Our empirical findings suggest that the top five cryptocurrencies are substitutes before the launch of Bitcoin futures. However, this effect is lost, and moreover, there are spillover effects on altcoins during both the post and the recovery period. We find a spillover effect of the introduction of bitcoin futures on altcoins and this effect seems to persist during the recovery period.

Impact of Twitter Sentiment Related to Bitcoin on Stock Price Returns

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.

Gold, platinum, and expected Bitcoin returns

This study examines the prediction power of the ratio of gold to platinum prices (GP) on Bitcoin. By using different models and data sources for Bitcoin, we find that GP predicts future Bitcoin return. When the price of gold relative to platinum increases, Bitcoin return also goes up. This finding is consistent with previous studies and contributes to the ongoing discussion whether GP can be used as an indicator for aggregate market risk. Using variance decomposition, we also find that volatility in the gold and platinum market can influence Bitcoin volatility and that this relationship shows time-varying dependency. Finally, our results are robust to including new year effects and data of different frequency. Hence, this paper contributes to the current literature of Bitcoin by showing that GP provides an important proxy of risk in the economy.

Price Clustering After the Introduction of Bitcoin Futures

Economic theory suggests that introduction of derivative contracts can improve the informational efficiency of the underlying asset prices (Danthine, 1978). In this study, we examine the impact of the introduction of Bitcoin futures on price clustering in Bitcoin. Our findings suggest that price clustering in Bitcoin meaningfully decreases post the introduction of its futures contracts.

Prediction of Bitcoin using Recurrent Neural Network

For the past couple of years, Machine learning and trading helped by artificial intelligence has drawn growing interest. Here, the approach is used to test the hypothesis that the inefficiency of cryptocurrency industry can be exploited in order to produce anomalous revenue. For the duration between Nov. 2015 and Apr. 2018, daily data for 1, 681 crypto currencies were analyzed. Simple trade techniques supported by state-of -the-art machine learning algorithms are seen to outperform the traditional benchmarks. The results obtained imply that non-trivial, but fundamentally simple, algorithmic processes will help to predict the short-term future of the cryptocurrency market. The popularity of cryptocurrencies had skyrocketed in 2017 due to several consecutive months of super-exponential growth of market capitalization. There are over 1,500 currently recorded cryptocurrencies actively trading today with the cryptocurrencies sitting on more than $300 billion [2], and a total market capitalization of over $800 billion in January 2018. According to a recent survey, between 2.9 and 5.8 million privates as well as institutional investors are in the numerous investment networks and access to markets has become easier over time. In a number of online markets, major crypto currencies can be purchased using fiat currency, and then used in order to purchase less known crypto currencies. The average trading amount is globally exceeding $15bn. About 170 money market funds had been invested in cryptocurrencies since 2017, and Bitcoin futures are launched in order to satisfy the Bitcoin trading and hedging demand for the market. The main objective of the work is to predict the Bitcoin prices, one of the most popular and widely used cryptocurrency which is a source of attraction for many investors as a source of profit or investment. But the market for the cryptocurrencies been volatile since the day it was first introduced. So, the approach towards the survey is to use LSTM RNN and use the available dataset and train the model to give the highest possible accuracy and to provide a real-time price of the Bitcoin for the following days.

Forecasting Bitcoin Volatility: Evidence from the Options Market

This paper studies a large number of Bitcoin options traded on the options exchange Deribit. We use the trades to calculate implied volatility and analyze if volatility forecasts can be improved using such information. Implied volatility is less accurate than ARMA or HAR model forecasts in predicting short-term future bitcoin volatility (1 day ahead), but superior in predicting long-term volatility (7, 10, 15 days ahead). Further, a combination of implied volatility and model-based forecasts provides the highest accuracy for all forecasting horizons revealing that the bitcoin options market contains unique information.

Future of bitcoin in India: Issues and challenges

Purpose - The main purpose of the study is to classify the different barriers in adoptions of Bitcoins and provide the appropriate elucidations for the problems faced by Bitcoin users in India. Design/ methodology/ approach - The study reviews researches on the adoption of Bitcoin as a medium of transactions and investments. A review of literature has been carried out to explore various existing Bitcoin. A case study has been conducted on Bitcoin and its adoption by individuals in different countries. AHP has been used to propose the model and for insights. Originality/value - Bitcoin adoption is still an unexplored area of research. This research has attempted to explore the root causes of the non-adoption of Bitcoin in different countries.

What factors drive returns on initial coin offerings?

In recent years, initial coin offerings (ICOs) have received considerable attention as a new form of crowdfunding. Because of the rapid growth in their popularity, ICOs have become a prominent research topic. However, the lack of knowledge about the nature of this kind of financial operation inevitably raises several important unanswered questions. This study starts by providing a comprehensive description of the characteristics, benefits, and risks associated with ICOs. Then, using a panel data set comprising daily information for 125 ICOs (44,217 observations gathered from December 2017 to December 2018), this study explores the influence of the ICO presale period, ICO category, Bitcoin spot returns, Bitcoin futures returns, and sentiment extracted from social media on ICO returns. The results suggest that while Bitcoin spot and Bitcoin futures returns exert a positive influence on ICO returns, the existence of a presale period exerts a negative influence, and ICO category seems to be non-significant. Sentiment extracted from social networks positively influences ICO returns. Further conclusions of this study are presented in the last section.

New Insights from the Bitcoin Futures Market

Recently, cryptocurrencies have drawn considerable attention from investors around the world. Such digital assets have also raised numerous hot issues in academic fields. Among them, Bitcoin is the most well-known and most notorious. After it was created in 2009, Bitcoin kept rising in price and reached its peak in late 2017. After that, it plunged dramatically. Coincidentally, Bitcoin futures also launched in December 2017. We are curious about the role Bitcoin futures play in the Bitcoin market. In this study, we investigate the relationship between Bitcoin and Bitcoin futures. First, we compare the optimal hedge ratios using three different hedge strategies, the na?ve hedge, the ordinary least squares (OLS) method, and dynamic hedging with the bivariate BEKK-GJR-GARCH model. Dynamic hedging is the most effective of the three methods; the level of risk reduction is around 59%. Then we test whether the volatility of Bitcoin would be significantly different before and after Bitcoin futures (BTC) launched. Our results support the hypothesis.

Стан та перспективи використання похідних фінансових інструментів на криптовалюти

The emergence and rapid development of the cryptocurrency market necessitated its organization and legal regulation. Today in Ukraine, businesses are allowed to record cryptocurrency as a financial asset (financial instrument / intangible asset), so cryptocurrency can be used by businesses and individuals as an investment. In developed countries, where the legal framework for the operation of cryptocurrencies has been created, new derivative financial instruments are emerging: Bitcoin futures and options on Bitcoin futures. The purpose of the article is to study the features of derivative financial instruments for cryptocurrencies and prospects for their use in Ukraine. The authors analyzed the peculiarities of the functioning of Bitcoin derivatives on Chicago Mercantile Exchange (CME). It has been established that both Bitcoin futures and options on Bitcoin futures are settlement contracts without the actual delivery of the underlying asset, and their value is formed depending on the spot prices for bitcoin. According to the results of the study, it can be argued that derivatives based on cryptocurrencies (bitcoin) are used mainly for speculative purposes, are highly volatile and high risk, require significant investment to participate in trading (compared to derivatives on traditional financial instruments) and do not involve any transactions with direct cryptocurrencies. Domestic legislation does not explicitly prohibit investments in cryptocurrencies and financial instruments derived from them, but does not determine the legal status of cryptocurrencies. National financial market regulators do not provide any guidance on valuation, accounting and cryptocurrency transactions to businesses, but only warn of the high risks of investing in cryptocurrencies.

Futures-Trading Activity and Jump Risk: Evidence From the Bitcoin Market

This paper examines the effects of futures trading on the jump risk in the Bitcoin market. We use a nonparametric method to detect Levy-type jumps in Bitcoin prices and obtain jump risk measures (jump intensity and jump size) of big and small jumps by using 5-minute high-frequency data, and document that Bitcoin has obvious features of Levy jumps, i.e., prices include both big jumps and small jumps, and the jump risk is time-varying. We then investigate the changes of these jump risk measures before and after the introduction of the Bitcoin futures, and we find that the jump size of big and small jumps decreases after the introduction of Bitcoin futures, while the big jump intensity increases. Further, we examine whether greater futures-trading activity (volume and open interest) is associated with greater Bitcoin jump risk and document that, on the one hand, there exists bidirectional causality between unexpected volume and jump intensity and size of Bitcoin and the impulse response function analysis show that the shocks have temporary increasing effects, and the speculative trading in the futures market will increase the jump risk. On the other hand, unexpected open interest Granger causes jump risk but the inverse is not true, indicating that investors are unlikely to hedge jump risk by using Bitcoin futures. These findings disclose the fact that the hedging function of the Bitcoin futures does not work well and the functions of Bitcoin futures need to be further improved.

The role of investor sentiment in the valuation of bitcoin and bitcoin derivatives

Bitcoin is the currency of the blockchain, which promises cost reductions for businesses. This paper develops models to value bitcoin, bitcoin futures, and bitcoin options. It provides the theoretical basis for bitcoin pricing. Optimal bitcoin prices are derived at the intersection of an aberrancy utility function, a hyperbolic cosine utility function, and a Bessel utility function with price distributions. Rational investors value bitcoin on the basis of blockchain applications, while irrational investors' value bitcoin based on personal recommendations.

The role of investor sentiment in the valuation of bitcoin and bitcoin derivatives

Bitcoin is the currency of the blockchain, which promises cost reductions for businesses. This paper develops models to value bitcoin, bitcoin futures, and bitcoin options. It provides the theoretical basis for bitcoin pricing. Optimal bitcoin prices are derived at the intersection of an aberrancy utility function, a hyperbolic cosine utility function, and a Bessel utility function with price distributions. Rational investors value bitcoin on the basis of blockchain applications, while irrational investors' value bitcoin based on personal recommendations.

Price Discovery in Bitcoin: The Impact of Unregulated Markets

We analyse minute-level multi-dimensional information flows within and between bitcoin spot and derivatives. We show that perpetual swaps and futures traded on the unregulated exchanges Huobi, OKEx and BitMEX are much the strongest instruments for bitcoin price discovery and we examine potential determinants of their leadership strength. Prices on the regulated CME bitcoin futures and the US-based spot exchanges react to, rather than lead, price movements on the unregulated exchanges and they may do so relatively slowly. In a multi-dimensional setting including the main price leaders within futures, perpetuals and spot markets, the CME futures have a very minor effect on price discovery, even less than the spot exchanges Bitfinex, Bitstamp and Coinbase. Our findings highlight the persistent problems stemming from inconsistent regulation in bitcoin spot and derivatives markets, including insufficient price stability and lack of resistance to manipulative trading. We conclude that the SEC are correct to maintain such issues as their main concern for bitcoin ETF applications.

Bitcoin Futures and Option Markets: Searching for Completeness

Cryptocurrencies have emerged in the last decade as a new asset class unlikely to disappear despite its extraordinary volatility. Futures contracts on Bitcoins were introduced in December 2017 by the Chicago Mercantile Exchange and options are being traded on crypto exchanges. Our goal in this paper is threefold: i) present the main features of cryptocurrency spot and derivative markets; ii) argue that storability of Bitcoins implies the existence of a convenience yield and infer from traded Future prices the risk neutral drift of the Bitcoin price in the continuous time Black-Scholes setting quite appropriate to continuously traded bitcoins. We use the prices of options traded on the Deribit Exchange to build the volatility smiles and skews observed at different dates of 2019 for short and long dated maturities and compare them to forward curves. Lastly, the robustness of the Black-Scholes formula allows capturing in our approach the stochastic volatility displayed by price trajectories and provides some answers to the incompleteness of the bitcoin market.

Risk Structure and Optimal Hedging of Bitcoin Inverse Futures

In the Bitcoin futures markets, the dominating contracts are inverse contracts. Unlike standard futures, Bitcoin inverse futures have a non-linear payoff structure, are settled in Bitcoin instead of the fiat currency, and require Bitcoins to be deposited into the margin account during trading. We characterize the unique high-order risk factors, asymmetry effect and (de)leverage effect of Bitcoin inverse futures, and obtain optimal hedging strategies in closed forms for both short and long hedges under the minimum-variance framework. We use the market data of Bitcoin spot and futures to conduct empirical studies. Our findings show that the optimal hedging strategies of Bitcoin inverse futures achieve superior hedging performance across exchanges.

Is the introduction of futures responsible for the crash of Bitcoin?

The price of Bitcoin reached its peak a mere few days after the introduction of Bitcoin futures and suffered an 80% loss in the following year. In this paper, we find a significant and negative relationship between the introduction of Bitcoin futures and Bitcoin returns, and an insignificant or positive relationship for other 7 major non-Bitcoin cryptocurrencies. Within the first 45 days after the futures launch, Bitcoin suffered a –26.50% loss, while other cryptocurrencies could still provide positive returns. We presume that the launch of Bitcoin futures was to an extent responsible for the crash of Bitcoin.

Bitcoin futures: trade it or ban it?

This paper examines the impact of South Korea’s ban on Bitcoin futures on intraday spot volatility, liquidity and volatility–volume relationship. The results show that while reducing the permanent component of intraday spot volatility, the imposition of a ban on Bitcoin futures trading increases the transitory component. For intraday spot liquidity, different liquidity proxies indicate heterogeneous results. Moreover, we identify a positive and unidirectional effect of intraday spot volume on volatility. This effect appears to be stronger in the post-ban period. Overall, over the past few months, South Korea’s Bitcoin futures ban generally has had a significant impact on the intraday dynamics of the Bitcoin spot market.