Russia massively invaded Ukraine on February 24, 2022, unavoidably having an effect on the world economy and finance. This paper uses the event study to research the short-term response of the February 2022 top 5 variable-price cryptocurrencies (BTC, ETH, BNB, XRP, SOL) to the Russia-Ukrainian war under the constant mean model. The cryptocurrency volatility was dramatic during the event window, and cryptocurrencies did not show the characteristics of safe haven. Overall, the result of the effect of the Russia-Ukraine war on the cryptocurrency market was negative, with the least negative impact on SOL and the most negative impact on BNB, XRP. Finally, Using the different event window analysis, it shows the cryptocurrency market return volatility rebounded, but it does not sufficiently indicate there is a positive trend in the cryptocurrency market after the event. The analysis of this paper can provide some help for cryptocurrency investors in the event of unforeseen circumstances. And in the data selection, this paper doesn’t consider stablecoins.
<p>The popularity of cryptocurrencies has grown significantly in recent years, and they have become an important asset for internet trading. One of the main drawbacks of cryptocurrencies is the high volatility and fluctuation in value. The value of cryptocurrencies can change rapidly and dramatically, making them a risky investment. Cryptocurrencies are largely unregulated, which can exacerbate their volatility. The high volatility of cryptocurrencies has also led to a speculative bubble, with many investors buying and selling cryptocurrencies based on short-term price fluctuations rather than their underlying values. Therefore, how to reduce the fluctuation risk introduced by exchanges, transform uncertain prices to deterministic value, and promote the benefits of decentralized finance are critical for the future development of cryptos and Web 3.0. </p> <p>To address the issues, this paper proposes a novel theory as Automatic Increase Market Systems (AIMS) for cryptos, which could potentially be designed to automatically adjust the value of a cryptocurrency helping to stabilize the price and increase its value over time in a deterministic manner. We build a crypto, WISH (https://wishbank.wtf), based on AIMS in order to demonstrate how the automatic increase market system would work in practice, and how it would influence the supply of the cryptocurrency in response to market demand and finally make itself to be a stable medium of exchange, ensuring that the AIMS is fair and transparent.</p>
Following a significant increase in media attention with the exorbitant rise in Bitcoin prices in 2017, initial coin offerings (ICOs) were introduced as a new way for organizations and companies to fund their businesses, presenting retail investors with a new opportunity to invest in young projects and companies. Little is known about who these investors are, why and how they invest in ICOs, and how they evaluate their investments afterward. This chapter investigates how investment behavior and investment satisfaction are influenced by behavioral biases and personality traits of retail investors in ICOs. We analyze quantitative survey data from more than 300 retail ICO investors and argue that investors demonstrate a unique set of personality traits that are connected to several behavioral biases. Biases are found to affect investment satisfaction, with overconfidence and disposition bias surprisingly being positively associated with investment satisfaction. The insights generated in this work are then also discussed in the context of meme stocks and more recent financial developments where similar biases and personality traits might influence retail investor behavior. Our findings may help investors understand and improve their investment behavior and decisions in various asset classes and situations that are comparable to the past ICO craze.
Cryptocurrencies has been considered as both an investment tool and a great invention that will replace money and change the world order. Although crypto currency trading has been investigated in many aspects, the psychological dimension that directly affects investors has often been ignored. Control of cryptocurrency trading is in the hands of investors rather than a central authority or institution. Thus, the value of cryptocurrencies changes with the reactions of investors. This situation suggests that psychological factors may be more prominent in cryptocurrency trading. Cryptocurrency trading has many similarities with gambling and betting, such as risk taking, getting quick returns, extreme gains or losses. Some significant components of behavioral addiction are also seen in individuals who spend so much time with cryptocurrency trading. The purpose of this article is to provide a better understanding of the psychological effects of cryptocurrency trading, which has entered our lives over a relatively brief period of time and reached millions of investors.
Options play a significant role in the financial markets. These types of securities’ values are derived from other securities such as stocks or bonds and, in recent years, cryptocurrencies. There are two types of option contract structures: American and European. Uncovered, or naked, options are buying or selling options primarily on speculation, where the investor does not hold any ownership in the underlying asset, in this case stock or cryptocurrency, from which the option premium is derived. The following three strategies are buying and selling call options, put options, and straddles. Black-Scholes-Merton model is one of the first pricing models that was used to calculate the fair price for a call or a put option premium based on five variables such as current spot price (cryptocurrency), strike price, volatility, time, and risk-free rate. It’s important to reintroduce some mathematical concepts that are essential for valuating options, including the natural logarithms, probability theory, and normal distribution.
In traditional finance, the Black & Scholes model has guided almost 50 years of derivatives pricing, defining a standard to model any volatility-based product. With the rise of Decentralized Finance (DeFi) and constant product Automated Market Makers (AMMs), Liquidity Providers (LPs) are playing an increasingly important role in markets functioning, but, as the recent bear market highlighted, they are exposed to important risks such as Impermanent Loss (IL). In this paper, we tailor the formulas introduced by Black & Scholes to DeFi, proposing a method to calculate the greeks of an LP. We also introduce Impermanent Gain, a product that LPs can use to hedge their position and traders can use to bet on a rise in volatility and benefit from large market moves.
In this paper, we propose a novel approach for forecasting cryptocurrency portfolios, harnessing modified versions of the N-BEATS deep learning architecture, integrated with convolutional network layers, Transformer mechanisms, and the Mish activation function. Our thorough evaluation, featuring an extensive sample size exceeding 4 million portfolio test samples, shows these variations outperforming traditional and other deep learning forecasting methods across various metrics. Particularly noteworthy is our N-BEATS Perceiver model, a Transformer-based variation, which not only delivers superior forecast accuracy but also exhibits a robust risk profile with less downside. Furthermore, the model performs exceptionally well under the TOPSIS method across a broad spectrum of portfolio evaluation parameters, making it a valuable asset for both portfolio selection and risk management in the dynamic cryptocurrency market.
David Kerr, Karen A. Loveland, Katherine Taken Smith, L. Murphy Smith
In this study, we examine major cryptocurrencies, present notable fraud cases, describe fraud risks, and analyze cryptocurrency financial performance. People debate whether cryptocurrency is an investment opportunity, the new Dutch Tulip Bubble, or a giant Ponzi scheme. There have been a number of high-profile fraud cases associated with cryptocurrencies, such as the FTX scandal in late 2022, thereby making fraud a real concern to current and potential future investors. Regarding financial performance, cryptocurrencies experienced a major collapse in value in the most recent period of the study, about three times worse than the major stock market indices. While in prior periods, cryptocurrencies have significantly outperformed stock market indices, recent fraud cases and the extreme volatility of cryptocurrencies indicate that investing in cryptocurrencies comes with much higher risk than traditional stock market investments. The debate over the investment potential of cryptocurrencies continues, whether they have long term value or are simply the new Dutch Tulip Bubble. The study’s findings will be useful to investors, regulators, and academic researchers regarding the cryptocurrency industry.
This paper discusses whether the Bitcoin exchange-traded fund (ETF), which tracks the value of Bitcoin, improves equity portfolios, by using a robust portfolio performance analysis. The equity portfolio is represented by an ETF that tracks the Standard & Poor’s 500. We use data from a turbulent investment period within the coronavirus pandemic, to study the diversification benefits of Bitcoin. We compare the performances of diverse portfolios composed of both ETFs, which include 40 classical dynamic volatility model-based portfolios and 900 score-driven portfolios. For the score-driven portfolios, the dynamic association is modelled by score-driven Clayton, rotated Clayton, Gumbel, rotated Gumbel and Student’s t copulas. We compare portfolio strategies using the model confidence set test. We find that score-driven portfolios outperform classical volatility model-based portfolios and the equity portfolio. Our results may provide suggestions for cryptocurrency investors on portfolio optimization and may also have policy implications for regulators and policymakers.
This paper studies the monthly expiration effect in the bitcoin markets. The emergence of trading in bitcoin futures in regulated markets is an ideal occasion to test this effect on an asset with singular characteristics. Our results with intraday data show that around the time of maturity there are significant changes in the trading volume, volatility and return of bitcoin, an asset that is traded in many exchanges simultaneously. Therefore, there is a clear expiration effect related to bitcoin futures. The closer to the expiration time (shortly beforehand or afterwards), the more intense these effects are. However, in spite of these general results, the expiration effect is not homogeneous across exchanges and depends on the characteristics of the futures contract in question. Robustness tests are also applied to confirm the results. The increasing participation of institutional investors is consistent with our findings, particularly in relation to the expiration effects of cash-settled futures, as these contracts are more appealing for sophisticated investors who could be interested in arbitrage or speculative processes.
The cryptocurrency market has enormous growth potential. In this study, the aim is to investigate how the news (shocks) affects cryptocurrency market volatility. This is significant because, while cryptocurrencies are gaining popularity among investors, the market’s extreme volatility discourages some prospective buyers, while also causing large losses for inexperienced investors. From 8 March 2019 to 30 November 2022, data from Bitcoin, Binance Coin, Ethereum, Dogecoin, and XRP were collected for the current study. The E-GARCH model was applied to the framed dataset to achieve the research aim. We discovered that the value of the size factor for all currencies was statistically significant, indicating that the news (shocks) significantly impacts volatility. Furthermore, volatility persistence in all cryptocurrencies is found to be very high and statistically significant. These study findings can help investors understand the impact of the news (shocks) on volatility in cryptocurrency returns.
Samuel Gaskin, Rafay Kalim, Kelvin J. Wallace, David Islip · 6 authors
This article addresses the shortcomings of the existing literature regarding cryptocurrency portfolio construction. First, we address the effectiveness of time-series models that capture stylized features. We perform a comparison study on various methods for estimating distributions for asset returns, including normal, historical, and GARCH models within a CVaR setting. The goal of this comparison is to determine the financial benefits of constructing portfolios based on estimated distributions that consider stylized features of crypto return series. Next, we create and compare various prediction models for cryptocurrencies and integrate them with mean-variance optimization to base performance on portfolio management metrics, such as Sharpe ratio and level of diversification, rather than statistical metrics like accuracy and R<sup>2</sup> on which the literature solely focuses. We determine it is unclear which optimization approach (CVaR or Robust MVO) leads to better crypto portfolios, and so, to address this, we compare optimization procedures on out-of-sample data through a thorough cross-validation of hyperparameters for each technique. We then compare the resulting risk-optimal portfolios from each technique. The results show that a CVaR approach with a GARCH simulation and a decision tree prediction model with robust mean-variance optimization yield portfolios of similar risk. We also show that using statistical metrics to evaluate models may not always yield the best financial performance.
The adoption of new financial instruments is naturally met with skepticism and apprehension. Capital markets as we know them today possess the amazing capability to package any exposure into a digestible instrument for market participants to utilize. However, prudence requires us to verify that these instruments do not suffer from inefficiencies that may prove hazardous to investors. In this article, the authors verify the efficiency of CME Bitcoin Futures Options by testing boundary arbitrage, put-call parity arbitrage, and box spread arbitrage conditions. The results strongly suggest that no reasonable arbitrage opportunities exist and that CME Bitcoin Futures Options are well-suited for institutional scale investing. Hence, we believe their use by institutions to hedge, speculate, and/or facilitate transactions between decentralized markets (“DeFi”) and traditional markets (“TradFi”) will grow significantly in the next few years.
Abstract Cryptocurrency returns diverge excessively from normality, with the interrelationship of Skewness and Kurtosis being accordant with a parabolic form, yet this connection is scantly documented. We begin by demonstrating diagrammatically the attributes of the S‐K plane for cryptocurrencies. Moreover, by taking advantage of the panel structure of the data, we estimate a quadratic model for the S‐K plane. Then we investigate whether the type and the infrastructure of the cryptocurrency, as well as the period under examination, alter the architecture of the plane. We find that the squared Skewness of tokens substantially lowers the slope of Kurtosis, while the same applies to the earlier era of the market.
We investigate the dynamic volatility connectedness of regional stocks, gold, Bitcoin, oil, and uncertainty index related to infectious diseases for the period from January 2014 to June 2022. We investigate the connectivity during Ebola & MERS periods, the normal period, the COVID-19 period and the full sample period. We find that the regional stock indices of the US, Europe, Africa and Latin America are net volatility transmitters whereas regional indices of Asia Pacific, Middle East and North Africa, and other assets like gold, oil and Bitcoin are net volatility recipients throughout the sample periods. By employing the TVP-VAR-based dynamic connectedness approach, we find the temporal evolution of system-wide total connectedness and pair-wise connectedness of financial assets to exhibit higher intensity of volatility spillover during the COVID-19 pandemic as compared to other sub-sample periods. We further observe, based on quantile connectedness approach, that the degree of dynamic connectedness is strong and significant across all the quantile spectrums only during the COVID-19 period. We observe that the safe haven characteristics of assets like gold, oil and Bitcoin diminish during the COVID-19 period due to strong dynamic connectedness with regional stock indices. Our findings have implications for policymakers, investors and portfolio managers in better risk management during periods of health epidemics and pandemics.
This paper examines return spillovers within and between different DeFi, cryptocurrency, stock, and safe-haven assets. For the period January 2019 to March 2022, we find that DeFi and cryptocurrency asset markets exhibit strong within-market and between-market return spillovers, that stock and safe-haven markets show weak connectedness, and that safe-haven assets are minor receivers and transmitters of between-market spillover effects. The connectedness between markets is time-varying and reveals structural changes in early 2020. Furthermore, we document that financial conditions shape the dynamics of return spillover effects between markets.
Muhammad Irfan, Mubeen Abdur Rehman, Sarah Nawazish, Yu Hao
This study aims to investigate the performance and behavior of fiat- and gold-backed cryptocurrencies to support stakeholders through the preparation of a portfolio from 1 January 2021 to 30 June 2022. Moreover, while searching for a hedge or a diversifier to construct a less risky portfolio with handsome returns, the prices of fiat-backed cryptocurrencies report high fluctuation during the sample period. ARIMA-EGARCH models have been employed to examine the volatile behavior of these cryptocurrencies. The empirical results are mixed as Bitcoin has been highly volatile during the economic recession. Due to its volatility, investors seek a safe haven. Ripple, on the other hand, shows low risk compared to Bitcoin. The results further reveal that PAX gold is more volatile than PM gold, while Bitcoin, being a highly traded cryptocurrency, is significantly correlated to other cryptocurrencies. The implications of this research showing the volatility of gold- and fiat-backed cryptocurrencies are equally important to stakeholders, such as investors, and policymakers.
Prior to the publication of this article, the author, Kathleen Moriarty, passed away on December 20, 2022. Kathleen was a pioneer in the investment management community, best known for her role in shepherding the first exchange-traded fund, the SPDR S&P 500 ETF, to launch, which earned her the moniker “Spider Woman.” Throughout her career, Kathleen was a humble and thoughtful colleague who was universally recognized as a kind-hearted and beloved individual. She will be deeply missed by friends, family, colleagues, and clients, and her contributions to the industry will not be soon forgotten.
Son yıllarda riskleri ve getirileri ile dikkat çeken yüksek oynaklık içeren kripto piyasasında, kripto paraların birbirleri ile olan etkileşimi yatırımcıların portföy kararları için önemli unsur olmuştur. Kripto paralar, yatırım portföyünde bir çeşitlendirme aracı ya da alternatif yatırımlara karşı hedge unsuru olarak görülmüştür. Bu makalede Bitcoin, Binance, Cardano, Dogecoin, Ripple, Ethereum ve IOTA para birimlerinin haftalık kapanış fiyatlarını içeren 231 gözlem kullanılarak, kripto paraların kendi aralarındaki doğrusal olmayan dinamik ilişkiler araştırılmıştır. Bu amaçla, kriptolar arasında doğrusal olmayan uzun dönemli ilişkiler ve nedensel ilişkiler sorgulanmıştır. Çoğu kripto paranın birbirleri ile yüksek ve pozitif korelasyona sahip olduğu tespit edilmiştir. Ekonometrik bulgular, Bitcoin ile Ethereum arasında uzun dönemli ilişkinin ve Bitcoin ile diğer para birimleri arasında karşılıklı etkileşimin olduğu yönündedir. Bulgular, kripto para piyasasının yüksek oynaklık içerdiği dönemlerde, yatırımcıların kripto para birimleri arasında riskten korunmada zorluk yaşayabileceği anlamına taşımaktadır. Diğer bir ifadeyle, kripto para piyasasının kendi içindeki çeşitlendirme çabasının yatırımcılara getireceği faydasının sınırlı kalacağı da bu çalışmanın diğer bir bulgusudur.
Marek Zatwarnicki, Krzysztof Zatwarnicki, Piotr Stolarski
In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. At that time, the interest in market indicators and trading strategies remained low, leading to the conclusion that most investors did not use decision-support indicators. The correct and backtested use of technical analysis signals can give the trader a significant advantage over most market participants. This work introduces an algorithmic approach to examining the effectiveness of the signals generated by one of the most popular market indicators, the Relative Strength Index (RSI). A model corresponding to an actual cryptocurrency exchange was used to backtest the strategies. The results show that the RSI as a momentum indicator in the cryptocurrency market involves high risk. Using alternative RSI applications can allow traders to gain an advantage in the cryptocurrency market. Comparing the results with the traditional buy and hold strategy shows the credible potential of the indicated method and the usage of signals generated by the technical analysis indicators.