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.
1 Abstract This thesis examines the price impact of order book events in the Bitcoin mar- ket. Using the data obtained from Binance exchange, the thesis shows that short-term price changes can be explained by high-frequency demand-supply interaction depicted in the Limit Order Book (LOB). The thesis demonstrates that the instantaneous price impact function has a non-linear shape, indicating that small and large orders have di↵erent e↵ects on price, potentially leading to opportunities for price manipulation and quasi-arbitrage. Additionally, the analysis confirms the inverse relation between the price impact coe cient and market depth. Furthermore, the thesis observes that there are no clear intraday patterns for the price impact coe cient. These findings provide valuable insights into the understanding of Bitcoin's price dynamics, benefiting traders, investors, and policymakers seeking to understand the complexities of the cryptocurrency market. 1
This thesis investigates the potential of cumulative prospect theory to ex- plain future cryptocurrencies' returns. Moreover, the study aims to determine whether the predictive power of cumulative prospect theory value persists when cumulative prospect theory value is computed by plugging the percentage form of return (for instance, 5%) instead of the decimal form (for instance, 0.05). Using a rolling sample of 200 cryptocurrencies with the highest market capitali- sation for each month from March 2017 to March 2023, we found that regardless of using returns in percentage or decimal form, the cumulative prospect theory value function produces comparative abnormal portfolio returns and confirms the hypothesis that cryptocurrencies with high (low) cumulative prospect the- ory value earn low (high) subsequent returns. JEL Classification G11, G12, G41 Keywords Prospect theory, Cumulative Prospect Theory, Cryptocurrency, Behavioural Economics Title Prospect Theory in the Cryptocurrency Market Author's e-mail 31078966@fsv.cuni.cz Supervisor's e-mail jiri.kukacka@fsv.cuni.cz
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).
In this research paper, we'll talk about cryptocurrency with the help and development of deep learning, and AI-assisted trading has gained immense popularity. To regulate the splendid engrossment from the part of cryptology we take the assistance of retailing (Deep learning & AI-support). A specific period of data has been stored on daily bases to receive the outcomes in a company of the help of ultra-modern algos. With the references to various papers, I found out the pros and cons of cryptocurrency price prediction. Some simple algorithms & architectures helped to grow the cryptocurrency market. Crypto trading became popular in 2017 and now more than 1500 cryptocurrencies are proactively trading. Crypto currencies can be smoothly created and used for online settlement. Bitcoin is also known as cryptocurrency and its values keep varying every second. Hence for predicting the rate of bitcoin cost I will use the infrastructure of LSTM. This infrastructure will help us in proving that LSTM will provide more accuracy. RNN is a category of ANN and connectivity for this type of network is made through nodes from the direct nodes along with a time-related progressions. LSTM is a RNN infrastructure which is a part of DL which handles the entire data as well as single data points.
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.