Erik Mauricio Muñoz Henríquez, Francisco A. Gálvez-Gamboa
<p>El objetivo de este trabajo es analizar el efecto spillover entre el mercado de las criptomonedas, los mercados financieros y commodities, utilizando índices de volatilidad realizada de las diez criptomonedas con mayor capitalización de mercado y la volatilidad implícita de las cotizaciones del Oro (GVZ) y el Petróleo (OVX), y el mercado financiero norteamericano (VIX) y europeo (VSTOXX) a través del Spillover Index basado en un Vector Autorregresivo (VAR). Los resultados indican que Ethereum es el mayor transmisor de volatilidad, seguido por Cardamo, mientras que los mayores receptores de volatilidad son ChainLink y BinanceCoin. Además, demostramos a través de la utilización de la volatilidad implícita que la contribución de los mercados financieros al spillover no excede el 3%, incluso resultados menores se evidencian con ambos commodities. El análisis de impulso-respuesta muestra el mayor efecto sobre las criptomonedas proviene del VIX, junto con una respuesta negativa ante un shock en el OVX y GVZ.</p>
Meiryani Meiryani, Caineth Delvin Tandyopranoto, Jason Emanuel, A. S. L. Lindawati · 7 authors
This study aims to determine the effect of global price movements for energy sector commodities, especially Crude Oil and Natural Gas Prices, on cryptocurrency price movements. This study focuses more on the Bitcoin cryptocurrency. This study uses quantitative methods, and the data collection used is secondary data with weekly data and the period from January 1, 2020-July 31, 2021. The number of observations used in this study amounted to 79 observations. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using Stata and SPSS software, the Multiple Linear Regression method, and the Classical Assumption Test. The results of this study show that global prices for energy sector commodities, especially Crude Oil, Natural Gas, have a positive effect on Bitcoin price movements. These results indicate a link between energy and Bitcoin caused by Bitcoin miners who are mining Bitcoin using energy so that when the price of Bitcoin rises, the price of energy will also increase.
Benjamin A. Jones, Andrew L. Goodkind, Robert P. Berrens
Abstract This paper provides economic estimates of the energy-related climate damages of mining Bitcoin (BTC), the dominant proof-of-work cryptocurrency. We provide three sustainability criteria for signaling when the climate damages may be unsustainable. BTC mining fails all three. We find that for 2016–2021: (i) per coin climate damages from BTC were increasing, rather than decreasing with industry maturation; (ii) during certain time periods, BTC climate damages exceed the price of each coin created; (iii) on average, each $1 in BTC market value created was responsible for $0.35 in global climate damages, which as a share of market value is in the range between beef production and crude oil burned as gasoline, and an order-of-magnitude higher than wind and solar power. Taken together, these results represent a set of sustainability red flags. While proponents have offered BTC as representing “digital gold,” from a climate damages perspective it operates more like “digital crude”.
Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.
Virtual digital assets including cryptocurrencies, non-fungible tokens and decentralized financial asset have been initially used as an alternative currency but are currently being purchased as an asset and hedging instruments. Exponentially growing trading volume witnesses the growing inclination of investors towards these assets, and this calls for volatility analysis of these assets. In this reference, the present study assessed and compared the volatility of returns from investment in virtual digital assets, equity and commodity market. Daily closing prices of selected cryptocurrencies, non-fungible tokens and decentralized financial assets, stock indices and commodities have been analysed for the post-covid period. Since returns were observed to be heteroscedastic, autoregressive conditional heteroscedastic models have been used to assess the volatility. The results indicate a low correlation of commodity investment with all other investment opportunities. Also, Tether and Dai have been observed to be negatively correlated with stock market. This indicates the possibility of minimizing risk through portfolio diversification. In terms of average returns, virtual digital assets are discerned to be better options than equity stock or commodity yet the variance scenario of these investment avenues is not very rosy. The volatility parameters reveal that unlike commodity market, virtual digital assets have got a significant impact of external shocks in the short-run. Further, the long run persistency of shocks is observed to be higher for the UK stock market, followed by Ethereum, Tether and Dai. The present analysis is crucial as the decision about its acceptance as legal tender money is still sub-judice in some countries. The results are expected to provide insight to regulatory bodies about these assets.
The present study examines the nonlinear relationship between the bitcoin prices and total bitcoin energy consumption over the period November 2010 and October 2021. A discrete threshold regression (TR) model is deployed to estimate the unknown thresholds that trigger the Bitcoin prices regime change. The designated TR model identifies six regimes of change for Bitcoin price movements. The estimated critical threshold specifications (total bitcoin energy consumption) that trigger the regime change of Bitcoin prices are estimated as 0.13, 2.52, 14.06, 43.17, and 146.29 respectively. The study finds that the impact of total bitcoin energy consumption on bitcoin prices are only statistically significant in the higher (4th and 6th) regimes respectively. The message here is that the impact of total bitcoin energy consumption on bitcoin prices is not uniform.
José Luís Miralles Quirós, María del Mar Miralles Quirós
Research background: A current strand of the financial literature is focusing on detecting inefficiencies, such as the day-of-the-week effect, in the cryptocurrency market. However, these studies are not considering that there are no daily closes in this market, and it is possible to trade cryptocurrencies on a continuous basis. This fact may have led to biases in previous empirical results. Purpose of the article: We propose to analyse the day-of-the-week effect on the Bitcoin from an alternative perspective where each hourly data in a day is considered an event. Focusing on that objective, we employ hourly closing prices for Bitcoin which are taken from the Kraken exchange, one of the world leading exchanges and trading platforms in the cryptocurrency markets, for the period spanning from January 2016 to December 2021. Methods: Contrary to the previous empirical evidence, we do not calculate daily returns, but rather the first stage of our proposed approach is devoted to analysing the hourly mean returns for each of the 24 hours of the day for each day of the week. We look for statistically significant hourly mean returns that could advance the importance of the hourly differentiation in the Bitcoin market. In a second stage, we calculate different post-event cumulative returns which are defined as the change in log prices over a time interval. Finally, we propose different investment strategies simply based on the significant hourly mean returns we obtain and we evaluate their performance in terms of the Sharpe ratio. Findings & value added: We contribute to the debate about the degree of Bitcoin?s market efficiency by providing an alternative methodology based on an event study hourly approach. Furthermore, we provide evidence that by investing in different post-event hourly windows it is possible to outperform the classic buy-and-hold strategy.
Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.
Bitcoin remains the most popular cryptocurrency and has attracted significant research attention, especially in the hedging and safe-haven literature. As many investors in bitcoin are concentrated heavily in cryptocurrencies as opposed to other assets, a question arises whether alternative cryptocurrencies (altcoins) can used as safe-havens and hedges against Bitcoin? We find that only meme coins offer hedging benefits but a wider range – Defi, meme coins, smart contracts, metaverse and privacy cryptocurrencies – can all act as safe-havens against bitcoin. We further show that their ability to act as hedges and safe-havens varies depending on whether the market is in a bubble or non-bubble period.
In recent times, the rise of Non Fungible Tokens has been inevitable. An NFT is a special kind of cryptographic token that represents the ownership of a unique piece of digital property. They are tamper-proof due to the use of distributed public ledger that records verified information across a network of computers. With the rise of crypto-trading, the NFT market segment has seen an accumulation in its trading volume in the capital market. There are various factors determining the price and sales of the NFTs and there is a need for meaningful insights from the data generated intermittently over time. With this as motivation, this proposed research work explores the factors that create an impact on the NFT market and in-depth data analysis with the help of brokerage firm data. This proposed work helps NFT enthusiasts would be able to derive the correlation between cryptocurrency market and NFT market.
This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as ‘spikes’. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones.
Abstract Many studies have associated cryptocurrencies with bubbles, especially during stressed market conditions such as the recent outbreak of the second wave of COVID‐19. Although the majority of studies have focused on Bitcoin, we investigate the predictability of bubble formation in the cryptocurrency market by using the log‐periodic power law and we uncover some important stylized facts of this market. Our sample consists of data for a selection of 15 cryptocurrencies for the period between 1 January 2021 and 1 September 2021 which coincides with the second wave of COVID‐19. We analyse 86 speculative bubbles, and we find that the cryptocurrency market has three times higher drawdown over equities during stressed market conditions.
A year ago, one thousand USD invested in Bitcoin (BTC) alone would have appreciated to three thousand five hundred USD. Deep reinforcement learning (DRL) recent outstanding performance has opened up the possibilities to predict price fluctuations in changing markets and determine effective trading points, making a significant contribution to the finance sector. Several DRL methods have been tested in the trading domain. However, this research proposes implementing the proximal policy optimisation (PPO) algorithm, which has not been integrated into an automated trading system (ATS). Furthermore, behavioural biases in human decision-making often cloud one’s judgement to perform emotionally. ATS may alleviate these problems by identifying and using the best potential strategy for maximising profit over time. Motivated by the factors mentioned, this research aims to develop a stable, accurate, and robust automated trading system that implements a deep neural network and reinforcement learning to predict price movements to maximise investment returns by performing optimal trading points. Experiments and evaluations illustrated that this research model has outperformed the baseline buy and hold method and exceeded models of other similar works.
Recently, Digital money is booming, and bitcoin shows potential in the field of investment as a representative of digital currency. According to modern portfolio theory, most of the investors are absolute risk-averter, and a diversified portfolio can effectively reduce the risk. So investors usually combine bitcoin with other assets to reduce non-systemic risks. Therefore, it is of great importance to formulate a feasible portfolio that can make steady returns for investors. For this reason, we build models to find suitable strategy to quantify the proportion of assets invested so that investors can make optimal investment decisions. We measure the return, risk, and efficiency of risk model’s portfolio by sharpe ratio. And based on DEA method, the multi-stage portfolio with V-type transaction cost is evaluated by comparing the portfolio from risk model with the portfolio by applying DEA method, and finally we prove that the strategy is the optimal one. Finally, the advantages and disadvantages of this model are analyzed and summarized.