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2,335 results · page 51 of 98
Kwansoo Kim, Nabyla Daidj
International audience
David Vidal-Tomás
No abstract is available for this record.
David Y. Aharon, Zaghum Umar, Xuan Vinh Vo
This study examines the connectedness between the US yield curve components (i.e., level, slope, and curvature), exchange rates, and the historical volatility of the exchange rates of the main safe-haven fiat currencies (Canada, Switzerland, EURO, Japan, and the UK) and the leading cryptocurrency, the Bitcoin. Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility. The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks. Meanwhile, the curvature of the yield curve and the Japanese Yen, Swiss Franc, and British Pound act mainly as net receivers. Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve's level, slope, and curvature, and from any main currency investigated. These findings hint that Bitcoin might provide hedging benefits. However, similar to the static analysis, our dynamic analysis shows that during different periods and particularly in stressful times, Bitcoin is far from being isolated from other currencies or the yield curve components. The dynamic analysis allows us to observe Bitcoin's connectedness in times of stress. Evidence supporting this contention is the substantially increased connectedness due to policy shocks, political uncertainty, and systemic crisis, implying no empirical support for Bitcoin's safe-haven property during stress times. The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times, Bitcoin has the property of a diversifier. The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.
Chidi U. Okonkwo, Bright O. Osu, Farid Chighoub, Ben I. Oruh
This paper investigated the co-movement between the bitcoin (BTC) and the exchange rates of some African currencies to the USD (United States Dollars) using the continuous wavelet transform (CWT) and wavelet coherence (WTC). This was done for the noisy as well as the denoised series. The CWT for the noisy series suggests high volatility for those who hold the currencies for the short term and low volatility for those who hold the currencies for a long-term period. The CWT of the denoised series suggests that volatility at low frequency is driven by noise, while volatility at a higher frequency is driven by market forces. The wavelet coherence suggests that in the presence of noise, bitcoin will be a hedge for the currencies. However, in the absence of noise, bitcoin is a haven for the Egyptian EGP, followed by the Algerian DZD, then the Nigerian NGN, and may not be a haven for the South African ZAR.
Florin Aliu, Artor Nuhiu, Adriana Knápková, Ermal Lubishtani · 5 authors
Abstract Cryptocurrencies are becoming an exciting topic for legislative bodies, practitioners, media, and scholars with diverse academic backgrounds. The work identifies diversification benefits when cryptocurrencies are combined with the equity instruments from Visegrad Stock Exchanges. Furthermore, the results of the study explore financial and economic benefits for the investors of combining cryptocurrencies with equity stocks on the mixed portfolio. Three different independent experiments were conducted to observe diversification benefits generated from cryptocurrencies. Results from the two experiments show that cryptocurrencies employ higher portfolio risk and generate higher returns when they are involved with equity stocks portfolios. The first experiment indicates that cryptocurrencies reduce the risk level of the equity portfolios while increase average returns. Providing the equity portfolios with additional equity stocks lower the portfolio risk which is in line with the theoretical paradigms. Results indicate that cryptocurrencies must be seriously considered by the portfolio managers as an essential aspect of the portfolio diversification benefits. Future studies might raise the samples of selected portfolios with stocks from different stock indexes, to identify the problem from a broader perspective.
Hakwan Lau, Stephen D. Tse
In this review, we evaluate the mechanisms behind the decentralized finance\nprotocols for generating stable, passive income. Currently, such savings\ninterest rates can be as high as 20% annually, payable in traditional currency\nvalues such as US dollars. Therefore, one can benefit from the growth of the\ncryptocurrency markets, with minimal exposure to their volatility risks. We aim\nto explain the rationale behind these savings products in simple terms. The key\nto this puzzle is that asset deposits in cryptocurrency ecosystems are of\nintrinsic economic value, as they facilitate network consensus mechanisms and\nautomated marketplaces (e.g. for lending). These functions create wealth for\nthe participants, and they provide unique advantages unavailable in traditional\nfinancial systems. Our review speaks to the notion of decentralized basic\nincome - analogous to universal basic income but guaranteed by financial\nproducts on blockchains instead of public policies. We will go through their\nimplementations of how savings can be channeled into the staking deposits in\nProof-of-Stake (PoS) protocols, through fixed-rate lending protocols and\nstaking derivative tokens, thereby exposing savers with minimal risks. We will\ndiscuss potential pitfalls, assess how these protocols may behave in market\ncycles, as well as suggest areas for further research and development.\n
Roland Gemayel, Alex Preda
No abstract is available for this record.
Carmen López-Martín, Sonia Benito Muela, Raquel Arguedas Sanz
No abstract is available for this record.
Zhenghui Li, Zhiming Ao, Bin Mo
We employ the quantile-coherency approach and causality-in-quantile method to revisit the roles of Bitcoin, U.S. dollar, crude oil and gold for USA, Chinese, UK, and Japanese stock markets. The main results show that the impact of global financial assets varies across different investment horizons and quantiles. We find that in most cases, the correlation between global financial assets and stock indexes is not significant or is weakly positive. From the perspective of investment horizons (frequency domain), the correlation in the short term is mostly manifested in Bitcoin, while in the medium and long term it is shifted to dollar assets. At the same time, the relationships are significantly higher in the medium and long term than in the short term. From the point of view of quantiles, it shows a weak positive correlation at the lower quantile. However, the correlation between the two is not significant at the median quantile. At the high quantiles, there is a weak negative linkage. According to the causality-in-quantiles approach results, in most cases global financial assets have different degrees of predictive capacity for the selected stock markets. Especially around the median quantile, the predictive ability was strongest.
Carol Alexander, Arben Imeraj
In <b>The Bitcoin VIX and Its Variance Risk Premium</b>, published in the Spring 2021 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Carol Alexander</b> and <b>Arben Imeraj</b> (both of the <b>University of Sussex</b>) introduce the bitcoin volatility index. CryptoCompare now streams this index every 15 seconds, under the ticker BVIN. Alexander and Imeraj are the first to investigate the bitcoin variance risk premiums and the behavior of the term structure of fair-value variance swap rates. The authors collect price data on bitcoin derivatives traded on the Deribit exchange via its application programming interface. They construct a family of indexes for different maturities using the same methodology used by CBOE’s equity volatility index, the VIX. They describe the methodology, noting that it accounts for information in volatility skews but assumes no jumps in prices. They also compare the indexes with those created with an alternative technique that does not rely on the no-jump assumption. In addition, they explore the diversification potential of bitcoin variance through correlation matrixes with other assets’ volatility indexes, realized volatilities, and other variance risk premiums. <b>TOPICS:</b>Currency, mutual funds/passive investing/indexing, statistical methods, performance measurement
Ze Shen, Qing Wan, David J. Leatham
One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.
Ali Raheman, Anton Kolonin, Ben Goertzel, Gergely Hegykozi · 5 authors
We present the cognitive architecture of an autonomous agent for active portfolio management in decentralized finance, involving activities such as asset selection, portfolio balancing, liquidity provision, and trading. Partial implementation of the architecture is provided and supplied with preliminary results and conclusions.
Shinji Kakinaka, Ken Umeno
This study investigates asymmetric multifractality and market efficiency of the major cryptocurrencies during the COVID-19 pandemic while accounting for different investment horizons. By applying the asymmetric multifractal detrended fluctuation analysis, we show that the outbreak affected the efficiency property of price behaviors differently between short- and long-term horizons. After the outbreak, the markets exhibited stronger multifractality in the short-term but weaker multifractality in the long-term. We also analyze asymmetric market patterns between upward and downward trends and between small and large price fluctuations and confirm that the outbreak has greatly changed the level of asymmetry in cryptocurrency markets.
Masood Tadi, Irina Kortchemski
Purpose This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios. Design/methodology/approach This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. Findings By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. Originality/value This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.
Samuel Rikli, Nico, Bigler Daniel, Moritz Pfenninger, Joerg, Osterrieder
Modeling financial time series is challenging due to their high volatility and unexpected happenings on the market. Most financial models and algorithms trying to fill the lack of historical financial time series struggle to perform and are highly vulnerable to overfitting. As an alternative, we introduce in this paper a deep neural network called the WGAN-GP, a data-driven model that focuses on sample generation. The WGAN-GP consists of a generator and discriminator function which utilize an LSTM architecture. The WGAN-GP is supposed to learn the underlying structure of the input data, which in our case, is the Bitcoin. Bitcoin is unique in its behavior; the prices fluctuate what makes guessing the price trend hardly impossible. Through adversarial training, the WGAN-GP should learn the underlying structure of the bitcoin and generate very similar samples of the bitcoin distribution. The generated synthetic time series are visually indistinguishable from the real data. But the numerical results show that the generated data were close to the real data distribution but distinguishable. The model mainly shows a stable learning behavior. However, the model has space for optimization, which could be achieved by adjusting the hyperparameters.
Giacomo De Nicola
We analyze the intraday time series of Bitcoin, comparing its features with those of traditional financial assets such as stocks and exchange rates. The results shed light on similarities as well as significant deviations from the standard patterns. In particular, our most interesting finding is the unusual presence of significant negative first-order autocorrelation of returns calculated on medium-frequency timeframes, such as one, two and four hours, signaling the presence of systematic mean reversion. It is also found that larger price movements lead to stronger reversals, in percentage terms. We finally point out the potential exploitability of the phenomenon by implementing a basic algorithmic trading strategy and retroactively applying it to the data. We explain the findings mainly through (i) investor and trader overreaction, (ii) excess volatility and (iii) cascading liquidations due to excessive use of leverage by market participants.
Salim Lahmiri, Stelios Bekiros
No abstract is available for this record.
José Benito Hernández C., Andrés García-Medina, Miguel Andrés Porro V.
We studied the effects of the recent financial turbulence of 2020 on the cryptocurrency market, taking into account both prices and volumes from December 2019 to July 2020. Time series were transformed into transaction matrices, and the Apriori algorithm was applied to find the association rules between different currencies, identifying whether the price or the volume of the currencies compose the rules. We divided the data set into two subsets and found that before the decline in cryptocurrency prices, the association rules were generally formed by these prices and that, then, the volumes of the transactions dominated to form the association rules.
Jong‐Min Kim, Chulhee Jun, Junyoup Lee
This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies.
Erdinç Akyıldırım, Ahmet Faruk Aysan, Oğuzhan Çepni, S. Pinar Ceyhan Darendeli
No abstract is available for this record.
Mahir Iqbal, Muhammad Hammad Iqbal, Fawwad Hassan Jaskani, Khurum Iqbal · 5 authors
In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the
Usha Rekha Chinthapalli
In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.
Edoardo Beretta
The paper explores the role, evolution and ruling principles of the concept of “money” in the 21st Century. In this continuously evolving context, cryptocurrencies and Blockchain technology are widely considered the most relevant monetary innovations of the last decades. By means of a macro-founded logical-analytical approach combined with statistical evidence, the paper provides arguments: 1. dismissing the “innovation myth” behind cryptocurrencies because of de facto representing a comeback of the private issue of means of payments and, more problematically, seigniorage at its best; 2. confirming that crypto-tokens do not comply with basic, still ruling monetary principles; 3. suggesting that excess liquidity is already invested in crypto-markets (which are themselves “inflationary”, namely not backed by real value (i.e. GDP). The concrete risk is, once again in economic history, represented by facing a financial bubble.
Alvaro Guinea, Alet Roux
A model is proposed for Bitcoin prices that takes into account market attention. Market attention, modeled by a mean-reverting Cox-Ingersoll-Ross processes, affects the volatility of Bitcoin returns, with some delay. The model is affine and tractable, with closed formulae for the conditional characteristic functions with respect to both the conventional and a delayed filtration. This leads to semi-closed formulae for European call and put prices. A maximum likelihood estimation procedure is provided, as well as a method for changing to a risk-neutral measure. The model compares very well against classical and attention-based models when tested on real data.