Blockchain Papers

Olfa Belhassine, Chiraz Karamti

No abstract is available for this record.

Κωνσταντίνος Γκίλλας, Maria Tantoula, Manolis Tzagarakis

No abstract is available for this record.

Worrawat Saijai, Paravee Maneejuk, Songsak Sriboonchitta

No abstract is available for this record.

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.

Hao Wang, Xiaoqian Wang, Siyuan Yin, Hao Ji

No abstract is available for this record.

Godfrey Uzonwanne

No abstract is available for this record.

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.

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.

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.

Walid Chkili

No abstract is available for this record.

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.

Saiful Izzuan Hussain, Nurulkamal Masseran, Nadiah Ruza, Muhammad Aslam Mohd Safari

Abstract Extreme value theory(EVT) has been used to study the frequency and probability related to extreme situations in finance. This approach focuses on the extreme values and able to provide a better estimation for risk models. In this study, Generalized Pareto Distribution (GPD) is employed to model daily extreme returns in the Bitcoin market from 2017 to 2019. These periods have witnessed three phases of extreme volatility for the cryptocurrency market. The returns level for the Bitcoin range between 17.011 and 18.746. The results demonstrate heavy tail and finite tail distribution characteristics for the tails. The findings provide a better understanding of the tails’ behaviour in the cryptocurrency market and help investors to make a financial decision.

Vincenzo Candila

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.

Seyram Pearl Kumah, Jones Odei‐Mensah

No abstract is available for this record.

M. Kabir Hassan, Md. Bokhtiar Hasan, Md. Mamunur Rashid

No abstract is available for this record.

Νikolaos Kyriazis

This study is an integrated survey of GARCH methodologies applications on 67 empirical papers that focus on cryptocurrencies. More sophisticated GARCH models are found to better explain the fluctuations in the volatility of cryptocurrencies. The main characteristics and the optimal approaches for modeling returns and volatility of cryptocurrencies are under scrutiny. Moreover, emphasis is placed on interconnectedness and hedging and/or diversifying abilities, measurement of profit-making and risk, efficiency and herding behavior. This leads to fruitful results and sheds light on a broad spectrum of aspects. In-depth analysis is provided of the speculative character of digital currencies and the possibility of improvement of the risk–return trade-off in investors’ portfolios. Overall, it is found that the inclusion of Bitcoin in portfolios with conventional assets could significantly improve the risk–return trade-off of investors’ decisions. Results on whether Bitcoin resembles gold are split. The same is true about whether Bitcoins volatility presents larger reactions to positive or negative shocks. Cryptocurrency markets are found not to be efficient. This study provides a roadmap for researchers and investors as well as authorities.

Thomas Chuffart

No abstract is available for this record.

Zied Ftiti, Waël Louhichi, Hachmi Ben Ameur

No abstract is available for this record.

Pierre Venter, Eben Maré

In this paper, the Heston–Nandi futures option pricing model is applied to Bitcoin futures options. The model prices are compared to market prices to give an indication of the pricing performance. In addition, a multivariate Bitcoin futures option pricing methodology based on a multivatiate GARCH model is developed. The empirical results show that a symmetric model is a better fit when applied to Bitcoin futures returns, and also produces more accurate option prices compared to market prices for two out of three expiry dates considered.

Zi‐Yi Guo

No abstract is available for this record.

Walid Mensi, Khamis Hamed Al‐Yahyaee, Idries Mohammad Wanas Al-Jarrah, Xuan Vinh Vo · 5 authors

No abstract is available for this record.

Mohamed Fakhfekh, Ahmed Jeribi, Ahmed Ghorbel, Néjib Hachicha

Purpose In a first place, the present paper is designed to examine the dynamic correlations persistent between five cryptocurrencies, WTI, Gold, VIX and four stock markets (SP500, FTSE, NIKKEI and MSCIEM). In a second place, it investigates the relevant optimal hedging strategy. Design/methodology/approach Empirically, the authors examine how WTI, Gold, VIX and five cryptocurrencies can be applicable to hedge the four stock markets. Three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) are implemented to estimate dynamic optimal hedge ratios. Findings The reached findings prove that both of the Bitcoin and Gold turn out to display remarkable hedging commodity features, while the other assets appear to demonstrate a rather noticeable disposition to act as diversifiers. Moreover, the results show that the VIX turns out to stand as the most effectively appropriate instrument, fit for hedging the stock market indices various related refits. Furthermore, the results prove that the hedging strategy instrument was indifferent for FTSE and NIKKEI stock while for the American and emerging markets, the hedging strategy was reversed from the pre-cryptocurrency crash to the during cryptocurrency crash period. Originality/value The first paper's empirical contribution lies in analyzing emerging cross-hedge ratios with financial assets and compare hedging effectiveness within the period of crash and the period before Bitcoin crash as well as the sensitivity of results to refits choose to compare between short term hedging strategy and long-term one.

Christoph J. Börner, Ingo Hoffmann, Jonas Krettek, Tim Schmitz

Abstract Cryptocurrencies (CCs) have become increasingly interesting for institutional investors’ strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher standard deviation and tail risk. The question then arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, with the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components based on modern methods from image/pattern recognition.

Christoph J. Börner, Ingo Hoffmann, Lars M. Kürzinger, Tim Schmitz

This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.