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1,505 results · page 36 of 63
Masaaki Fukasawa, Basile Maire, Marcus Wunsch
Impermanent Loss in Decentralized Finance can be hedged with weighted variance swaps
Vesa Pursiainen, Jan Toczynski
No abstract is available for this record.
Carol Alexander, Arben Imeraj
We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data on hourly historical bitcoin option prices reveals that bitcoin implied volatility curves behave very differently from those of equity index options. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument because the basis risk for the perpetual is very much smaller than it is for calendar futures. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black–Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30% for out-of-the-money puts, and reach an average of 15% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.
Kanis Saengchote
The rapid rise of cryptocurrency prices led to concerns (e.g. the Financial Stability Board) that this wealth accumulation could detrimentally spill over into other parts of the economy, but evidence is limited. We exploit the tendency for metaverses to issue their own cryptocurrencies along with non-fungible tokens (NFTs) representing virtual real estate ownership (LAND) to provide evidence of the wealth effect. Cryptocurrency prices and their corresponding real estate prices are highly correlated (more than 0.96), and cryptocurrency prices Granger cause LAND prices. This metaverse bubble reminisces the 1920s American real estate bubble that preceded the 1929 stock market crash.
Mieszko Mazur, Miguel Vega
This article investigates the emerging segment of the cryptocurrency market related to football fan tokens (FFTs)—digital assets used for engagement with professional football clubs around the world. More specifically, the authors study the investability of FFTs from the perspective of risk and return. They find that FFTs generate a whopping 150% return on the first trading day. This return is significantly larger if the FFT market cap is higher, the FFT offer price is lower, the football team displays better historical performance, and the team is located in a relatively small metropolitan area with a high GDP per capita. They also find that in the long run, FFTs severely underperform all major crypto benchmarks, including NFT, DeFi, Meme, and bitcoin. Moreover, the returns to FFTs tend to be highly volatile (160% annualized). Intriguingly, they show that the real-life performance of football teams does not affect the contemporaneous market performance of their FFTs.
Nusret Cakici, Syed Jawad Hussain Shahzad, Barbara Będowska-Sójka, Adam Zaremba
No abstract is available for this record.
Stefan Scharnowski, Hossein Jahanshahloo
No abstract is available for this record.
Sang Rae Kim
No abstract is available for this record.
Peng‐Fei Dai, John W. Goodell, Luu Duc Toan Huynh, Zhifeng Liu · 5 authors
Abstract We evidence that cryptocurrencies have a higher probability of crashes than equity indices, although such crashes are of shorter duration. Commonality of crash risk between cryptocurrency and equity markets occur in approximately 80% of the periods examined. Further, recently evolved cryptocurrency uncertainty indices are more relevant for predicting co‐crash behavior than economic policy uncertainty. Results are consistent with cryptocurrencies being a growing source of financial instability.
Kanis Saengchote, Tālis J. Putniņš, Krislert Samphantharak
Decentralized Finance (DeFi) is built on a fundamentally different paradigm: rather than having to trust individuals and institutions, participants in DeFi potentially only have to trust computer code that is enforced by a decentralized network of computers. We examine a natural experiment that exogenously stress tests this alternative paradigm by revealing the identities of individuals associated with a DeFi protocol, including a convicted criminal. We find that, in practice, DeFi does not (yet) fully remove the need for trust in individuals. Our findings suggest that that because smart contracts are incomplete, they are subject to run risk (Allen and Gale, 2004) and personal character and trust of individuals are still relevant in this alternative financial system.
Yu Wang
No abstract is available for this record.
Joshua T. White, Sean Wilkoff, Serhat Yildiz
No abstract is available for this record.
Sean Wilkoff, Serhat Yildiz
No abstract is available for this record.
Raj Parekh, Nisarg Patel, Nihar Thakkar, Rajesh Gupta · 8 authors
Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework,DL-Gues, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction ofDashcarried out using price history and tweets ofDash,Litecoin, andBitcoinfor various loss functions for validation. Further, to check the usability ofDL-GuesSon other cryptocurrencies, we have also inferred results for price prediction ofBitcoin-Cashwith the price history and tweets ofBitcoin-Cash,Litecoin, andBitcoin.
Lennart Ante
This paper investigates the market reaction to large positive or negative liquidity shocks on the value of tokens traded on decentralized exchanges (DEXes) on the Ethereum blockchain. Automated market makers (AMMs) and constant product markets provide transparent and decentralized ways to directly swap two blockchain tokens for each other via the use of liquidity pools. Using trade-by-trade data of 2.77 million swaps of 14 different tokens traded on Uniswap v2, v3 and SushiSwap, we find that the size of sell orders significantly correlates with negative future token returns, while buy size positively correlates with future token returns. Using an event study approach, we quantify the market reaction of unusually large sell and buy orders (top 1% percentile) and identify that the market reaction outweighs the economic value of the event by a factor of -7.4 for sell orders and +4.4 for buy orders over a short-span trading window. In the case of sell orders, a high proportion of the abnormal return is already realized before the event, which indicates informed trading in the form of arbitrage or frontrunning via Miner Extractable Value (MEV). Looking at individual crypto assets, we find a mean reassessment of token value following short sales of up to 0.79% within just one follow-up trade (buy orders up to 0.50%). The findings indicate that price shocks may have a signaling effect but also that market capitalization may be an insufficient metric for assessing the liquidity and valuation of (inefficient) crypto assets. The results suggest multiple challenges for investor protection in decentralized finance (DeFi) markets.
Andrés García-Medina, Ester Aguayo-Moreno
No abstract is available for this record.
Huaigang Long, Ender Demir, Barbara Będowska-Sójka, Adam Zaremba · 5 authors
We examine the role of geopolitical risk in the cross-sectional pricing of cryptocurrencies. We calculate cryptocurrency exposure to changes in the geopolitical risk index and document that coins with the lowest geopolitical beta outperform those with high geopolitical beta. Our findings suggest that risk-averse investors require additional compensation as motivation to hold cryptocurrencies with low and negative geopolitical betas, and they are willing to pay a premium for assets with high and positive geopolitical betas. The effect cannot be explained by known return predictors and is robust to many considerations.
Mei Luo, Shuangchen Yu
Abstract This study compares and contrasts US and international accounting and financial reporting practices for cryptocurrency. We analyze the financial statements of 40 global companies that have exposure to cryptocurrencies, including cryptocurrency purchases, mining, payments, trading, and investments in ICOs and early-stage blockchain ventures. We document inconsistency between Generally Accepted Accounting Principles (GAAP) and International Financial Reporting Standards (IFRS), as well as distortions that can mislead users in assessing asset value, liquidity, profitability, and cash-generating abilities across firms. In particular, firms receiving cryptocurrencies in revenue-generating activities account for cryptocurrencies as intangibles using different measurement bases and classify the associated cash inflows differently. Some firms place cryptocurrencies in the usual long-term location of intangibles, while others consider intangibles as liquid, short-term assets. Limited guidance about crypto-assets from both IFRS and GAAP lets companies choose which existing standard to apply and how to apply it. Understanding the financial and valuation implications of these new virtual assets is vital for future accounting research and professional practice.
Samet Günay, Kerem Kaşkaloğlu
The majority of NFTs utilize the Ethereum blockchain platform to facilitate smart contracts. In this paper, we execute various econometric analyses to determine if this technical dependence induces a financial linkage to the risk, return, and prices of assets. For robustness, we also test the same relation between Bitcoin and NFTs. Empirical analyses are conducted through SADF bubbles test, DCC-GARCH time-varying correlation analysis, Bootstrap causality tests and spillover analysis. According to the results of various price, return, and volatility analyses, we find that NFTs do not demonstrate idiosyncratic features in their price developments and thus they cannot be considered as a separate asset class. Additionally, NFTs do not possess a specific financial linkage with Ethereum from using its infrastructure. Finally, we suggest NFT investors use alternative financial instruments, rather than Ether and Bitcoin in portfolio diversification, due to the presence of significant time-varying relationships and interactions.
Hatice Yildirim, Ayben Koy
Purpose- The investment instinct that has come from the past years and the developing technology provides new opportunities. Thetransaction volumes of cryptocurrencies, especially Bitcoin, which have entered our lives since 2008, have increased significantly in the lastfew years. The high volatility, high return, and high risk in cryptocurrencies attract some investors, although it is attractive to some. Theprimary purpose of this study is to investigate the usability of cryptocurrencies in portfolio management and how they will affect portfolioreturns.Methodology- Daily data from January 1, 2019, to December 31, 2020, were used in the study. The return, risk, and Sharpe ratios of portfolioscreated with stocks and new portfolios created by adding crypto currencies compared.Findings- The analysis reveals that portfolios including cryptocurrencies have both higher risk rates and higher returns. It was concluded thatthe Sharpe ratios of the portfolios created in 2019 were high, and the portfolio performance was good.Conclusion- According to the study, the returns of portfolios that include cryptocurrencies are higher than portfolios that are not included.Moreover, due to the volatility of the cryptocurrency market, the investor should consider the possible risks.
Nicholas Apergis
No abstract is available for this record.
Sarafatema Peerzade, Dnyaneshwari Wayal, Gauri Kale
The proposed project work is totally supported and easy yet effective strategy named as Martingale. An automatic system which only requires only some pre-coded instructions to execute trades on variety of market variables starting from asset price to trading volume. The strategy along with each cryptocurrency, the benchmark against which the algorithm is tested is that the market’s performance. Returns are compared with the buying and so multiplying the trade volume at each loss and different scenarios are analysed to work out the chance related to the buying compared with an algorithmic strategy. Results are going to be in love with the market’s actual trends and also with some alternate possible trends to check all market scenarios. An internet interface will accompany the presentation allowing the users to check the strategies by entering their parameters and instantly seeing the results
Tobias Kellner, Dominik Maltritz
Purpose The purpose of this study is to analyze market inefficiencies in the market for cryptocurrencies by providing a comprehensive analysis of short-term (over)reactions that follow significant price changes of such currencies. Design/methodology/approach This study identifies and analyzes overreactions and mispricing in markets for cryptocurrencies by applying a broad set of thresholds that depend on market-specific dynamics and volatilities. This study also analyzes the returns on days following abnormal returns and identifies significant differences from normal returns using the t -test and the Mann–Whitney U -test. The researchers further complement the literature by using end-of-the-day returns in addition to high-low returns. Additionally, this study considers a broad sample of 50 cryptocurrencies for an expanded time span (2015–2020) that includes the big currencies as well as smaller currencies. Findings Findings detect the existence of overreactions and, thus, market inefficiencies in crypto markets. The findings for different methodological approaches are similar, which underpins the robustness of the findings. By considering a broad sample that includes small and big currencies, we can show the existence of a market size effect. By considering a broad set of thresholds, the authors further found evidence for a magnitude effect, which means that higher initial abnormal returns are related to higher inefficiencies. Practical implications This paper has practical implications. Market inefficiencies were detected, which can be used in practical trading to obtain excess returns. In fact, methodological approach of this study and its results can be used to derive a strategy for trading in cryptocurrencies that can be easily implemented. Based on the study’s findings, the authors can expect positive access returns by applying this trading strategy. Originality/value The authors complement the literature on market inefficiencies and mispricing in crypto markets by analyzing price patterns after initial abnormal returns. Researchers contribute by applying different methodological approaches in addition to the approaches used so far, by considering a set of different thresholds and by applying a much broader data set that enables the study to analyze additional aspects.
Andrew Falcon, Tianshu Lyu
We execute a comparative analysis of machine learning models for the time-series forecasting of the sign of next-day cryptocurrency returns. We begin by compiling a proprietary dataset that encompasses a wide array of potential cryptocurrency valuation factors (price trends, liquidity, volatility, network, production, investor attention), subsequently identifying and evaluating the most significant factors. We apply eight machine learning models to the dataset, utilizing them as classifiers to predict the sign of next day price returns for the three largest cryptocurrencies by market capitalization: bitcoin, ethereum, and ripple. We show that the most significant valuation factors for cryptocurrency returns are price trend variables, seven and thirty-day reversal, to be specific. We conclude that support vector machines result in the most accurate classifications for all three cryptocurrencies. Additionally, we find that boosted models like AdaBoost and XGBoost have the poorest classification accuracy. At length, we construct a probability-based trading strategy that secures either a daily long or short position on one of the three examined cryptocurrencies. Ultimately, the strategy yields a Sharpe of 2.8 and a cumulative log return of 3.72. On average, the strategy’s log returns outperformed standalone investments in all three cryptocurrencies by a factor of 5.64, and Sharpe ratios more than threefold.