V. Akila, Nitin M.V.S., I S N V R Prasanth, S. R. M. A. Ayeshmi M. ¡ 5 authors
Cryptocurrencies have gained immense popularity in recent years as an emerging asset class, and their prices are known to be highly volatile. Predicting cryptocurrency prices is a difficult task due to their complex nature and the absence of a central authority. In this paper, our proposal is to employ Long Short-Term Memory (LSTM) networks, a type of deep learning technique to forecast the prices of cryptocurrencies. We use historical price data and technical indicators as inputs to the LSTM model, which learns the underlying patterns and trends in the data. To improve the accuracy of the predictions, we also incorporate a Change Point Detection (CPD) technique using the Pruned Exact Linear Time (PELT) algorithm. This method allows us to detect significant changes in cryptocurrency prices and adjust the LSTM model accordingly, leading to better predictions. We evaluate our approach predominantly on Bitcoin cryptocurrency, but the model can be implemented on other cryptocurrencies provided there are valid historical price data. Our experimental results show that our proposed model outperforms the baseline LSTM algorithm, achieving higher accuracy and better performance in terms of Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Our research findings suggest that combining deep learning techniques such as LSTM with change point detection techniques such as PELT can improve cryptocurrency price prediction accuracy and have practical implications for investors, traders, and financial analysts.
Christian Fieberg, Gerrit Liedtke, Thorsten Poddig, Thomas Walker ¡ 5 authors
Abstract We propose CTREND, a new trend factor for cryptocurrency returns, which aggregates price and volume information across different time horizons. Using data on more than 3,000 coins, we employ machine learning methods to exploit information from various technical indicators. The resulting signal reliably predicts cryptocurrency returns. The effect cannot be subsumed by known factors and remains robust across different subperiods, market states, and alternative research designs. Moreover, it survives the impact of transaction costs and persists in big and liquid coins. Finally, an asset pricing model that incorporates CTREND outperforms competing factor models, providing a superior explanation of cryptocurrency returns.
Using Russian-Ukraine war as an exogenous event, we investigate whether Bitcoin is used to evade financial sanctions. We follow three avenues to explore this problem. First, we investigate Bitcoin trading volume pre- and post- Russia's invasion. Second, we explored price and return relationships between Bitcoin and other major asset classes during the same period. Lastly, we investigate the associations between Bitcoin trading volume and Russia oil export by sea. Overall, our results suggest that Bitcoin is not used to evade sanctions in large scale.
Environmental externalities from cryptomining may be large, but have not been linked causally to mining incentives. We exploit daily variation in Bitcoin price as a natural experiment for an 86 megawatt coal-fired power plant with on-site cryptomining. We find that carbon emissions respond swiftly to mining incentives, with price elasticities of 0.69-0.71 in the short-run and 0.33-0.40 in the longer run. A $1 increase in Bitcoin price leads to $3.11-$6.79 in external damages from carbon emissions alone, well exceeding cryptomining's value added (using a $190 social cost of carbon, but ignoring increased local air pollution). As cryptomining requires ever more computing power to mine a given number of blocks, our study highlights both the revitalization of US fossil assets and the potential value of financial industry accounting standards that incorporate cryptomining externalities.
We examine the volume-volatility relationship across Bitcoin futures and spot markets, using daily realised volatility measures estimated from high frequency intraday data. We estimate realised spot volatility across five major exchanges using both the standard volume weighted price and using a new approach, inspired by the CME Bitcoin Reference Rate methodology. We find that unexpected trading volume is the most important explanatory variable for BRR spot volatility, explaining 20% of variation in price volatility at exchange level. Conversely, we find that both expected and unexpected CME Bitcoin futures volumes play a very limited or even calming role in systemic volatility. Our findings suggest that CME Bitcoin futures are not independently contributing to systemic risk in Bitcoin over the period studied.
This study examines dynamic connectedness linkages between precious metals, manufacturing metals, oil, natural gas, and Bitcoin. The Quantile-VAR methodology is utilised to identify causal spillovers from 2015 through 2022, where results demonstrate significantly stronger pairwise connectedness at extreme quantiles, where the gold-silver and copper-oil pairs exhibit the strongest linkages. Additionally, the overall dynamic connectedness is higher at the lowest and highest quantiles, particularly reinforced during inflationary periods. Copper is identified as the strongest generator of spillovers, followed by silver, nickel, and zinc. There are mixed findings when analysing gold and aluminium, whereas oil, natural gas, and Bitcoin are identified as net receivers. This study provides insight into commodities and cryptocurrency marketsâ diversifying and hedging abilities during alternative economic and financial conditions.
Abstract Numerous turbulent events in the recent past have raised the issue of an asset that could play the role of safe haven. Although for many years it was considered that gold has the role of a safe haven, an increasing number of recent works challenge such a point of view. The emergence of cryptocurrencies after the Global financial crisis has opened up numerous questions, one of them being whether cryptocurrencies, as an asset (money) independent of governments, can play the role of safe haven. Therefore, the paper examines whether gold and bitcoin, the latter as the best representative of crypto-currencies, can play the role of safe haven in relation to European indices. In the paper, this hypothesis was confirmed for gold and rejected for bitcoin.
This research assesses the prediction of Bitcoin prices using the autoregressive integrated moving average (ARIMA) and long-short-term memory (LSTM) models. We forecast the price of Bitcoin for the following day using the static forecast method, with and without re-estimating the forecast model at each step. We take two different training and test samples into consideration for the cross-validation of forecast findings. In the first training sample, ARIMA outperforms LSTM, but in the second training sample, LSTM exceeds ARIMA. Additionally, in the two test-sample forecast periods, LSTM with model re-estimation at each step surpasses ARIMA. Comparing LSTM to ARIMA, the forecasts were much closer to the actual historical prices. As opposed to ARIMA, which could only track the trend of Bitcoin prices, the LSTM model was able to predict both the direction and the value during the specified time period. This research exhibits LSTM's persistent capacity for fluctuating Bitcoin price prediction despite the sophistication of ARIMA.
This paper develops a theoretical model of the bitcoin market and demonstrates that the bitcoinâs volatile and explosive price path is a consequence of the Bitcoin protocolâs system of supply management. The model implies that the marginal cost of mining the target supply of bitcoins is the fundamental value of the bitcoin since it corresponds to an equilibrium in the Bitcoin protocol and the rent-seeking tournament among miners. The data provide strong empirical evidence of cointegration between the bitcoinâs price and the marginal cost of mining the target supply of bitcoins, demonstrating the existence of their long-run equilibrium relationship. Current bubble detection techniques indicate that there is no evidence of explosive departures in the price of the bitcoin from its model-implied fundamental value. Since the raw price data exhibit explosive behavior, the apparent bubbles in the price of the bitcoin can be attributed to its nonstationary market fundamentals. ⢠The bitcoinâs price dynamics result from the protocolâs interference in the market. ⢠The Bitcoin protocol works against the self-correcting mechanism of the market. ⢠Adjustments of the difficulty result in volatile and explosive behavior in the price. ⢠There is cointegration between the bitcoinâs price and the marginal cost of mining. ⢠Apparent bubbles in the price can be attributed to nonstationary market fundamentals.
The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite processing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally influence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence. These results inform strategic considerations for stakeholders to more effectively plan, utilize, and advocate for economic activities on Ethereum, enhancing the understanding and optimization of within the rapidly evolving economy.
A K Das, Aryan Ramachandran, Ayush Thombare, Swarali Ghangurde
This paper examines the relationship between green bonds and cryptocurrencies that specifically follow the Proof-of-Stake consensus mechanism. A lot has been discussed about the positive bi-directional and asymmetric relationship between green bonds and bitcoins, the most widely known proof-of-work cryptocurrency and the ineffectiveness of green bonds as hedging instrument for bitcoins. In this study, the author has tried to check whether there is a relationship between green bonds and Proof-of-stake cryptocurrencies using the Vector Error Correction Model (VECM). The secondary data under consideration is the daily data of the S&P Green Bond Index (SPGB) to track the performance of relevant green bonds and the daily data of Solana and Cardano to track the performance of Proof-of-stake cryptocurrencies. It is found that there exists a negative long-term relationship between green bonds and the selected cryptocurrencies. The author also conducts a portfolio analysis to demonstrate how green bonds function as a useful risk-diversification tool in conjunction with cryptocurrencies that follow the proof-of-stake consensus mechanism.
Grzegorz Dudek, Piotr Fiszeder, PaweĹ Kobus, Witold Orzeszko
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threats and developing risk management strategies. The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several models and forecasting methods are compared in terms of their forecasting accuracy, i.e., HAR (heterogeneous autoregressive), ARFIMA (autoregressive fractionally integrated moving average), GARCH (generalized autoregressive conditional heteroscedasticity), LASSO (least absolute shrinkage and selection operator), RR (ridge regression), SVR (support vector regression), MLP (multilayer perceptron), FNM (fuzzy neighbourhood model), RF (random forest), and LSTM (long short-term memory). The realized variance calculated from intraday returns is used as the input variable for the models. In order to assess the predictive power of the models considered, the model confidence set (MCS) procedure is applied. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF. The research provides a useful reference point for the development of more sophisticated models.
This paper examines whether cryptocurrencies are hedging instruments for green and non-green energy instruments. We differantiate between cryptocurrencies with two types of consensus mechanisms, Proof-of-work and Proof-of-stake, which reflect the demand for energy used for the coins' confirmation. We obtained dynamic conditional correlations from SV models and apply them to calculate hedge ratios. Based on the sample from January 2019 till December 2022 we find that clean energy sources are better hedges for oil than clean or dirty cryptocurrencies due to high volatility of the latter instruments. Cryptocurrencies are better hedging instruments for oil than for clean energy assets. We also find evidence that investors in clean crytocurrencies are more environmentally aware than those investing in the dirty one.