The study investigates the no-arbitrage parity conditions in Bitcoin spot and futures markets, focusing on the efficiency of the spot-futures (SFP) and futures spread parity (FSP) models in estimating the Bitcoin futures prices. Utilizing data from the Chicago Mercantile Exchange (CME) and Binance exchange, the research analyzes the relationship between spot and futures prices of Bitcoin, moreover, examines the relationship between intramarket Bitcoin futures contracts. The study finds that the mean pricing error of SFP is greater than FSP, indicating the greater efficiency of FSP in pricing Bitcoin futures. It also explores arbitrage opportunities by testing the equality of means of the bid-ask spread and mispricing, revealing that arbitrage opportunities are not consistently present. Few exploitable arbitrage opportunities in bullish markets are found, but overall, the arbitrage profit is not feasible when considering the costs such as bid-ask spread.
This paper studies two cryptocurrencies and finds that their prices can be estimated or forecasted better than their returns because returns being ratios of prices, do not always exhibit the economic relationship that may exist between two price series. However, average returns use multiple prices in their ratios that capture the economic behavior of the price series. Further, the forecasting performance of traditional preceding return models are compared with those of preceding average return models and the latter are found to generally give better results in terms of Root Mean Square Error (RMSE) and average return on investments (ARoIs).
ABSTRACT Recent literature explores the profitability of various cryptocurrency momentum trading strategies and proposes cryptocurrency momentum as a pricing factor (Liu et al.). How risky is this factor‐based investment strategy for crypto‐investments? We answer this question by examining the distributional characteristics (hence, riskiness) of six cryptocurrency momentum trading strategies. The empirical evidence suggests that the realised variances of cryptocurrency momentum strategies are governed by power laws. The statistical tests derived from block bootstraps indicate that the population mean and variance of the momentum factor realised variances are statistically not defined. Contrary to the belief that cryptocurrency momentum trading strategies produce generous payoffs, our results imply that, in real life, we might not be able to realise these risk premiums. We conclude that the performance metrics evaluating the profitability of cryptocurrency momentum strategies, using variance as an input, are not informative. We also find cross‐sectional dependence amongst the tail risk of momentum strategies based on different formation periods.
This paper demonstrates that the Millennial generation exhibits unique personal traits that have implications for their portfolio choice and, hence, for the stock market. Specifically, Millennials display greater propensity to participate in the stock market, exhibit more confidence (as they trade more frequently), and more diversification (invest in greater number of stocks and foreign assets). At the macro level, we find that the Millennials influence the stock market to behave differently surrounding holidays, and the statistical significance of key financial anomalies is disrupted. Despite the Millennials’ proficiency in using internet, they utilize more social methods (friends/relatives) when they invest than previous generations. Collectively, we infer that the financial market is not only exposed to business cycles, but also to generation cycles.
Virtual assets and currency sector are becoming increasingly intertwined.According to new IMF research, the correlation of crypto assets with traditional holdings like equities has increased dramatically as usage has grown, limiting their risk perception investment opportunities, and raising the danger of spillover across financial markets.Theoretical and empirical findings concerning cryptocurrencies and stock market behaviour have been misleading thereby putting policy makers at a crossroads.This paper therefore examines the response of stock market to investment in cryptocurrencies in the US stock market.Monthly data covering the period between February 2016 to February 2022 was used.The answer was achieved using novel dynamic autoregressive-distributed lag (ARDL) simulation techniques along with the Breitung and Candelon causality test.Findings revealed that cryptocurrencies impacted positively on the US stock market.Secondly, investment in Bitcoin and Ethereum is a good predictor of stock market while no evidence of causality between investment in ripple and stock market indices in the US stock market.Thirdly, a long-run relationship exists between investment in cryptocurrencies and behaviour of stock market indices in the United State, and that investment in cryptocurrencies has a significant long-run increasing effect on stock prices in United State.
Abstract This study investigates how exposure to local prices changes the transaction utility of international tourists, and the role of purchasing power parity (PPP) and the use of cryptocurrency in these changes. Findings indicate that tourists’ transaction utility did not vary all that much when they visited a country with comparable PPP to their own. Meanwhile, when traveling to countries with a lower PPP, tourists enjoy a heightened transaction utility. Furthermore, using Bitcoin results in greater transaction utility than using fiat currency.
As an investor, volatility plays an important role in decision making. It is defined as the rate at which a security’s price increases or decreases, i.e., shows pricing behavior during a definite span of time. A high volatility will lead to high risk. Thus, it becomes critical to determine the volatility and the risk-return trade-off among investments. This paper tries to document the volatility and risk-return trade-off of four prominent crypto-currencies (Bitcoin, Ethereum, Binance and Ripple), based on market-capitalization. For analysis, closing prices of cryptocurrencies has been accumulated through secondary method for 365 days, starting from 1st March 2022 and ending on 28th February 2023. Standard Deviation and Kurtosis, used together for volatility and risk assessment, documented that Bitcoin has the highest volatility and risk associated with expected returns. Regression, for assessing the impact of volatility in BTC price on others, derived that ETH has a strong, but not very strong, bivariate relationship with BTC, among all the pairs. Durbin Watson (DW) test concluded that there was no auto-correlation in the prices of crypto-currencies, i.e., previous day’s price does not play significant role in today’s price. For risk-return trade-off, Coefficient of Variation (CoV) has been applied. It determined that Ethereum has the highest ratio indicating its non-suitability to a conservative investor because of having the lowest returns as compared to risks involved; while Binance has the lowest Coefficient of Variation (CoV) depicting lower risk and maximum return among all.
Currently the most liquidly traded options on the crypto underlying are the so-called inverse options. An inverse option contract is quoted and traded in the units of the underlying cryptocurrency. The main economic reason for popularity of inverse contracts in the crypto exchanges (such as Deribit) is that inverse contracts enable to operate without maintaining fiat cash accounts. For the theoretical part, we show that inverse options are just regular vanilla options considered under the martingale measure using the forward of the underlying as the numéraire. This measure requires an adjustment to option delta. For the empirical part, we use Deribit options data of past four years to backtest delta-hedged option strategies. We introduce USD and Coin accounting of trading Profit&Loss (P&L) which is important for designing strategies in crypto options. We show empirically that USD and Coin accounting rules are equivalent when performance is measured is Coin and USD units, respectively. We establish that the risk-premia observed in options on Deribit is negative and significant so that strategies selling volatility are expected to generate positive risk-adjusted performance in the long-term.
Jan 1, 2023·Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Hangzhou, China
Quantitative trading plays a pivotal role in financial markets. Over the past decade, quantitative trading has made remarkable improvements. Due to instability and nonlinearity in financial markets, it is still challenging to formulate high-return trading strategies to address the problem of long-t
Abstract Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.
This study will investigate the liquidity spillover effects of five cryptocurrencies: Bitcoin, Ether, Binance-coin, Ripple, and Tether. Firstly, the researcher utilizes the Amihud illiquidity ratio to quantify the liquidity performance of the five currencies, which we treat as weekly for the purposes of our study due to data collecting constraints. Secondly, to quantify the liquidity spillover effect in the cryptocurrency market over the period of 2017-2022, the researcher employs Diebold and Yilmaz's spillover index. The results identify the senders and receivers of liquidity spillovers on an individual and pairwise basis for the five major currencies and demonstrate the presence of time variation. Additionally, this paper evaluates the news report-based cryptocurrency uncertainty index (UCRY). This includes the price of cryptocurrencies (UCRY price) and the uncertainty surrounding cryptocurrency policy (UCRY policy). Considering the constructed index follows the same path as the largest cryptocurrency, Bitcoin, it is therefore recommended that the Bitcoin price can be used to forecast the cryptocurrency uncertainty index. Overall, this study has filled a gap in the literature by conducting research on liquidity spillovers in cryptocurrency markets, and it presents some preliminary conclusions. However, in order to verify the validity of our findings and to provide more meaningful results, additional research is required over a longer time horizon and with additional cryptocurrency types.
This paper examines the impact of Bitcoin futures introduction on the crash risk of spot Bitcoin prices. Using both time-series regression with a time dummy and a difference-in-differences (DID) framework, we find that crash risk, proxied by the negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL) of 5-minute intraday Bitcoin returns, declines significantly after the launch of Bitcoin futures. Robustness checks confirm that the findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period. Furthermore, we explore the moderating roles of market liquidity and investor attention. The crash-mitigating effect of Bitcoin futures is significantly more pronounced in periods of low liquidity and limited investor attention, suggesting that futures markets play a stronger role in enhancing information efficiency under such conditions.HighlightsThis paper examines whether Bitcoin futures introduction increases or decreases Bitcoin price crash risk.The price crash risk of Bitcoin, measured by NCSKEW and DUVOL from high-frequency intraday data, decreases significantly after futures introduction.The main findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period.The crash-mitigating effect is more pronounced in periods of low liquidity and limited investor attention.
Jan 1, 2023·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Research suggests that a significant number of those investing in cryptocurrencies do not follow what we might call rational, profit-maximizing behavior. We also know that with the progressive lowering of entry barriers to online trading platforms, an increasing number of inexperienced investors are investing in cryptocurrencies. Increasingly, the behavior of investors contradicts the predictions made by traditional financial models and challenges the assumptions on which such models have previously relied when anticipating returns on cryptocurrency investments. To overcome this issue we develop a random forest model which we train with features stemming from a sentiment analysis performed on data generated by cryptocurrency enthusiasts using Twitter, Google Trends, and Reddit. Our findings show that such features have an important role to play in capturing the behavior of cryptocurrency investors and increase our model’s ability to anticipate regime changes in the cryptocurrency market. Our model outperforms the predictive ability of the Log-Periodic Power Law model—currently, the model most widely-used to predict regime changes in financial markets. These results imply that scholars and practitioners aiming to understand and predict the development of cryptocurrency markets stand to benefit from analyzing social media data generated by cryptocurrency enthusiasts.