Manoel Fernando Alonso Gadi, Maximilian Schmidt, Noah Ruemmele, MiguelβΓngel Sicilia
Stock market indices are pivotal tools for establishing market benchmarks, enabling investors to navigate risk and volatility while capitalizing on the stock market's prospects through index funds. For participants in decentralized finance (DeFi), the formulation of a token index emerges as a vital resource. Nevertheless, this endeavor is complex, encompassing challenges such as transaction fees and the variable availability of tokens, attributed to their brief history or limited liquidity. This research introduces an index tailored for the Ethereum ecosystem, the leading smart contract platform, and conducts a comparative analysis of capitalization-weighted (CW) and equal-weighted (EW) index performances. The article delineates exhaustive criteria for token eligibility, intending to serve as a comprehensive guide for fellow researchers. The results indicate a consistent superior performance of CW indices over EW indices in terms of return and risk metrics, with a 30-constituent CW index outshining its counterparts with varied constituent numbers. The recommended CW30 index demonstrates substantial advantages in comparison to established benchmarks, including prominent indices like DeFi Pulse Index (DPI) and CRypto IndeX (CRIX). Additionally, the article explores the practicality of implementing the CW30 in Layer 2 networks of the Ethereum Ecosystem, advocating for the Arbitrum infrastructure as the optimal choice for the decentralized crypto index protocol herein referred to as the Ethereum Ecosystem Index (EEI). The study's insights aspire to enrich the DeFi ecosystem, offering a nuanced understanding of network selection and a strategic framework for implementation. This research significantly enhances the existing literature on index construction and performance within the Ethereum ecosystem. To our knowledge, it represents a pioneering comprehensive analysis of an index that accurately mirrors the Ethereum market, advancing our comprehension of its intricacies and wider ramifications. Moreover, this study stands as one of the initial thorough examinations of index construction methodologies within the nascent asset class of crypto. The insights gleaned provide a pragmatic approach to index construction and introduce an index poised to serve as a benchmark for index products. In illuminating the unique facets of the Ethereum ecosystem, this research makes a substantial contribution to the current discourse on crypto, offering valuable perspectives for investors, market stakeholders, and the ongoing exploration of digital assets.
Bitcoin inverse futures are dominant derivative contracts traded in the cryptocurrency market. We aim to understand the mean-variance tradeoff of such contracts through quantitative studies. To this purpose, we derive explicit representations for the expectation and variance of the returns on Bitcoin inverse futures and obtain their first-order approximations. The empirical findings show that Bitcoin inverse futures are more (resp. less) risky than standard futures when the market is in backwardation (resp. contango). We further find that Bitcoin inverse futures bear higher downside risk, as measured by semi-deviation, than standard futures.
Abstract. This article presents an analysis of the historical dynamics of the cryptocurrency market based on time series data using the OHLCV dataset. The study presents the results of calculations of the main cryptocurrency market momentum technical indicators. Using intelligent computational methods, the paper assesses patterns and trends in the data of major cryptocurrencies. The study emphasizes the importance of technical analysis in understanding the volatile landscape of digital currencies. Key words: time-series, data analysis, cryptocurrency market, momentum indicators, technical analysis indicators, OHLCV.
The main theme of this thesis lies in our attempt to contribute to explaining the Home Bias Puzzle (HBP) observed in international financial markets in the presence of Cryptocurrencies, within the framework of FinTech and more specifically in the context of Decentralized Finance (DeFi). Through a meticulous review of recent works on the question of international portfolio diversification, encompassing physico-financial assets such as Cryptocurrencies, technology stocks, classical stocks, currencies, commodities, and oil, we examined the issues of arbitrage and the strategy of choosing investment in domestic assets and/or choosing investment in international portfolio diversification. To empirically test our central issue, we validated four essays formulated as hypotheses: In the first essay on Efficiency and Volatility, we examined, through time series modeling, the impact of integrating cryptocurrencies into the investor's portfolio to verify our first hypothesis, namely the transmission of volatility shocks induced by this asset. The use of ARCH and GARCH modeling shows that the coefficients associated with them are close to unity, thus indicating a permanent effect of shocks on conditional variance. However, during the COVID-19 pandemic, Bitcoin was considered a safe haven asset. Also, relying on the econometric results of EGARCH and TARCH models, similar to Wang (2021), we show the existence of an excessive leverage effect on the volatility of future returns for Bitcoin (+26.50%) and Dogecoin (+65.07%). Furthermore, our study shows the absence of leverage for the other cryptocurrencies in the sample. Our second essay aims to validate the second hypothesis borrowed from industrial economics on the Integration-Segmentation-Diversification (ISD) triptych of asset portfolios, i.e., the relationship between goods and services markets and the capital market. To verify this hypothesis, we used a VAR (Vector AutoRegressive) modeling to analyze the causal time relationship between economic variables (real sphere) and financial variables (financial sphere) through standard tests (AIC) in the first stage and (SC) in the second stage. The results obtained show that price variations in the developed markets of the sample do not follow a common long-term trend. In this context, there would likely be an opportunity for diversification among developed markets, a product of financial liberalization (Attig.N. and al. (2023)). Thirdly, the empirical test of the existence of a Home Bias, our third essay and hypothesis were conducted over the period 2006-2021, with 640 observations. The determinants of the Home Bias Puzzle (HBP) were divided into seven panels: governance variables, macroeconomic variables, market size and microstructure variables, information asymmetry, familiarity and geography, Foreign Trade, and finally geopolitical variables. The econometric results we obtained are consistent with previous findings (Garg, Karmakar, M. and Paul, S., (2023); Lee, J. Lee, K. and Oh, F.D. (2023)). Finally, the last essay, reflecting hypothesis four on the relationship between Cryptocurrencies, Portfolio Diversification, and Behavioral Finance, highlights the superiority of the W. Sharpe (1964) performance index compared to other naive portfolio diversification strategies derived from the Mean-Variance approach by H. Markowitz (1952). Our results corroborate those obtained by Hachicha F., and al. (2023).
The work carried out a comparative analysis of scientific publications regarding the possibility of predicting the direction of the cryptocurrency exchange rate using the data of open numerical indicators, based on the results of which it can be concluded that due to the volatility of the cryptocurrency market and the need for accurate forecasting, there is a need to create an aggregated indicator that will take into account the current price exchange rate asset, parameters of simple indicators, trading volume, etc. In addition, this indicator will be a parameter for the application of a multi-criteria analysis model in the process of supporting decision-making for cryptocurrency trading. A software decision support system for cryptocurrency traders on the Trading View platform has also been developed, which allows the cryptocurrency trader to get the value of the current situation of the cryptocurrency market in the form of a value using the method of weighting coefficients and selected indicators. Among the selected indicators: RSI, MA, CCI, Stochastic Oscillator, OBV, ADX, CMF to determine the moment of opening a position, and Fibonacci Retracement, Ichimoku Cloud to determine the closing of positions. Taking into account all the indicators and the coefficients determined for them, the obtained range of values is from 0 % to 100 %. If the value of the indicator exceeds the threshold of 20 %, it means that it is necessary to inform the trader about a possible entry point. That is, a value of 20 % to 40 % is weak performance, 40 % to 60 % is medium performance, 60 % to 80 % is strong performance, and a value greater than 80 % will not be overlapped by new pyramiding values for a better overall indicator success rate. The value of the indicator determines the potential effectiveness of opening positions, and thanks to the RSI indicator, the direction of opening positions is determined. The direction of the position is divided into long and short. An indicator has been developed for the TradingView platform, which, unlike existing simple indicators, collects data from open access and calculates a potential point for opening a position. Obtaining the numerical value of a single indicator saves the trader time to review and analyze a collection of indicators and time to decide on opening a position, as the cryptocurrency market is known for its sudden volatility, where a decision must be made quickly.
This paper addresses the imperative task of assessing and ranking cryptocurrencies, particularly pertinent in the context of the burgeoning popularity of public blockchains. The proliferation of available options necessitates a rigorous evaluation, prompting the formulation of a novel model grounded in both objective and subjective criteria. To contend with the challenge posed by the expanding landscape of public blockchains, ten discerning criteria are delineated, encompassing facets such as Technology, TPS, Market capitalization, GitHub fork, GitHub stars, Twitter followers, Twitter hashtags, trading volume, sentiment score, and the price range differential. Leveraging expert opinions, the pairwise impact of these criteria is ascertained, and the DEMATEL method is judiciously employed to derive their respective weights. Subsequently, the PROMETHEE method is harnessed to effectuate the ranking of 20 cryptocurrencies predicated on the identified criteria. Furthermore, the integration of LSTM enables the prediction of values for four predictable criteria, seamlessly incorporated into the PROMETHEE model to furnish rankings across diverse temporal intervals. The proposed model, thus, presents a holistic and pragmatic approach to inform investment decision-making within the dynamic cryptocurrency market. By embracing a comprehensive set of criteria and integrating predictive analytics, this model stands as a valuable contribution to the field, offering nuanced insights to stakeholders navigating the complexities of cryptocurrency investment.
In recent years, with the rapid development of science and technology today (especially blockchain techniques), a special currency cryptocurrency (also relatively known as digital currencies) was born in 2008 and has received a lot of attention. On account of its special nature and intrinsic, a large amount of investors often combine digital currencies and quantitative trading to make profits and earn extra returns based on the concepts. With this in mind, this study mainly describes the definition of digital currency and its development process and compares digital currency with ordinary currency to highlight its advantages and disadvantages. Subsequently, this research introduces the combined application of digital currency and quantitative transaction in general with some of the backtesting results. According to the analysis, this paper puts forward suggestions on the existing problems of digital currency and promotes its further development in the future. Overall, these results shed light on guiding further exploration of quantitative strategy designs for cryptocurrency.