Blockchain Papers

Sabrine Ayed, arouri mohamed, Adel Barguellil

No abstract is available for this record.

Kei Nakagawa, Ryuta Sakemoto

No abstract is available for this record.

George Milunovich, Seung Ah Lee

No abstract is available for this record.

Νikolaos Kyriazis, Stephanos Papadamou, Panayiotis Tzeremes, Shaen Corbet

No abstract is available for this record.

Mina Sami, Wael Abdallah

This paper aimed to assess the effect of the cryptocurrency market on firms’ market value, especially on the sectoral level, in Africa. To reach the study’s main goal, the authors adopted the Panel-Corrected Standard Errors (PCSEs) and Panel Double-Clustered Standard Errors (PDCSEs). Using firm-level data, the results of this study can be summarized as follows: (a) The cryptocurrency market hurts the firm market value in Africa. (b) The firms operating across different sectors respond disproportionally to the cryptocurrency market. For instance, the sectors that offer low returns in Africa (industrial, energy, financial) negatively respond to the cryptocurrency market, while the sectors that offer high returns (real estate and information technology) are not significantly affected. (c) The cryptocurrency market has a perverse effect on less experienced and highly indebted firms. (d) The consistent policies of governments to ban cryptocurrency do not work efficiently.

Gideon Ndubuisi, Christian Urom

No abstract is available for this record.

Jinan Liu, Victor J. Valcarcel

No abstract is available for this record.

Qian Wang, Yu Wei, Yao Wang, Yuntong Liu

Stock market is susceptible to various external shocks for its tight dependence on economic fundamentals, financial speculation, and fragile emotions in massive traders, making it a very risky market for investors. In this paper, we aim to identify whether commonly recognized safe‐haven assets, that is, bitcoin, gold, and commodities, can provide investors with effective hedging utility in international stock markets, especially during periods of extreme market turbulence. By using the spillover index method based on the TVP‐VAR model, we find that firstly, bitcoin, gold, and commodities can only offer weak hedging effects on stock markets. Furthermore, their abilities to act as a safe haven are ranked as: commodities > gold > bitcoin. Secondly, in general, we have observed the increasing hedging ability of these safe‐haven assets in times of extreme market turmoil. Thirdly, among international stock and safe‐haven asset markets, the world and the developed stock markets act as the net spillover transmitters, while bitcoin, gold, and commodities are the net recipients. Lastly, the total spillover effects are time‐varying and increase significantly after the outbreak of extreme events.

José Manuel Carbó, Sergio Gorjón

No abstract is available for this record.

Pasquale De Rosa, Valerio Schiavoni

Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices across all different sets of crypto-assets, it remains unclear what and if there are tight relations between the trading prices of different cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation indicators to coin owners, suggesting possible acquisitions or sells. However, these correlations remain largely unvalidated. In this paper, we shed lights on the trend correlations across a large variety of cryptocoins, by investigating their coin-price correlation trends over a period of two years. Our experimental results suggest strong correlation patterns between main coins (Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting techniques for time-series modeling in the context of crypto-coins. We release our dataset and code to reproduce our analysis to the research community.

เกวลิน อยู่เย็น, ภูมิศิริ ดำรงวุฒิ

รายงานเอกัตศึกษาฉบับนี้มีวัตถุประสงค์เพื่อศึกษาและวิเคราะห์ถึงปัญหาในการจัดเก็บภาษี เงินได้บุคคลธรรมดาและภาษีมูลค่าเพิ่มสำหรับโทเคนที่ไม่สามารถทดแทนกันได้ (NFT) ใน 4 ด้านหลัก ๆ ได้แก่ (1) การจัดประเภทเงินได้ (2) ค่าใช้จ่ายที่ถือเป็นรายจ่ายทางภาษี (3) หลักการจัดเก็บภาษีเงินได้ (4) การพิจารณาสถานะทางกฎหมายของ NFT ภายใต้กฎหมายภาษีีมูลค่าเพิ่ม โดยได้ทำการศึกษาเปรียบเทียบกับกฎหมายประเทศสหรัฐอเมริกาและประเทศออสเตรเลีย เพื่อนำมาวิเคราะห์และหาข้อเสนอแนะในการกำหนดแนวทางการจัดเก็บภาษีข้างต้นให้เหมาะสมกับประเทศไทยต่อไป สำหรับประเทศไทยพบว่า กรณีที่ NFT ไม่เข้าลักษณะตามคำนิยาม พ.ร.ก. สินทรัพย์ดิจิทัลฯและมีเงินได้จากการซื้อขาย NFT ดังกล่าว ไม่ว่าจะเป็นเงินได้ฝั่งผู้สร้าง NFT หรือนักลงทุน ถือเป็นเงินได้พึงประเมินตามมาตรา 40 (8) โดยสามารถหักค่าใช้จ่ายที่เกิดขึ้นตามความจำเป็นและสมควร ในขณะที่ผลประโยชน์ที่ได้รับจากการโอนคริปโทเคอร์เรนซีขณะทำธุรกรรมซื้อขาย NFT ถือเป็นเงินได้ตามมาตรา 40 (4) (ฌ) ส่วนหลักการจัดเก็บภาษีเงินได้สำหรับ NFT ถือว่าเป็นแหล่งเงินได้ในประเทศไทยหรือในต่างประเทศให้ใช้หลักพิจารณาจาก Wallet ที่ใช้ในการซื้อขาย NFT อีกทั้งการพิิจารณาสถานะทางกฎหมายของ NFT ภายใต้กฎหมายภาษีมูลค่าเพิ่ม สรุปได้ว่า NFT เข้าลักษณะเป็นสินค้าอยู่ในบังคับต้องเสียภาษีมูลค่าเพิ่ม อย่างไรก็ตาม ยังไม่ได้มีการกำหนดแนวทางในการจัดเก็บภาษีมูลค่าเพิ่มไว้อย่างชัดเจน ทั้งนี้ จากการศึกษาแนวทางการจัดเก็บภาษีเงินได้บุคคลธรรมดาเกี่ยวกับ NFT ในประเทศสหรัฐอเมริกาและประเทศออสเตรเลียพบว่า แนวทางการจัดเก็บภาษี NFT นั้นเหมือนกับแนวทางการจัดเก็บภาษีในสินทรัพย์ดิจิทัลอื่น ๆ จึงส่งผลให้เกิดแตกต่างกับประเทศไทย เนื่องจากประเทศสหรัฐอเมริกาและประเทศออสเตรเลียมองว่า เงินได้ที่เกิดจากการซื้อขาย NFT ของนักลงทุนควรเสียภาษีจากส่วนเกินทุน (Capital Gain) แทนเงินได้ปกติ (Ordinary Income) และกรณีในส่วนของการพิจารณาหลักการจัดเก็บภาษีเงินได้จากการซื้อขาย NFT จะมุ่งพิจารณาถิ่นที่อยู่หรือสัญชาติของผู้มีหน้าที่เสียภาษีเป็นสำคัญ นอกจากนี้ แนวทางการจัดเก็บภาษีมูลค่าเพิ่มของ NFT ทั้ง 2 ประเทศนั้นมีแนวทางให้ NFT อยู่ภายใต้บังคับการเสียภาษีมูลค่าเพิ่มเหมือนเช่นกับประเทศไทย แต่จะมีการกำหนดหน้าที่และวิธีการในการจัดเก็บภาษีมูลค่าเพิ่มไว้อย่างชัดเจน โดยให้ NFT Marketplace ทำการหน้าที่จัดเก็บภาษีขายหรือภาษีสินค้าและบริการและนำส่งให้แก่ภาครัฐ ดังนั้น ประเทศไทยจึงควรนำหลักการทางภาษีที่มีแนวทางอย่างชัดเจนในต่างประเทศมาปรับใช้ให้เกิดความเหมาะสมต่อไป

Md Shahedur R. Chowdhury, Damian S. Damianov

No abstract is available for this record.

Xilong Tan, Yubo Tao

Cryptocurrencies are widely known for their limited publicly available information, making it challenging to predict market returns. Technical analysis has emerged as an essential tool in this context, but its effectiveness in the cryptocurrency market remains an open question. Using data from nearly 3,000 cryptocurrencies at daily, weekly, and monthly horizons from 2013 to 2022, we systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns and find that price-based signals are more effective in predicting short-term horizons, while volume-based signals are more powerful in predicting long-term horizons. Further analysis shows that machine learning techniques can significantly improve the performance of technical indicators, and technical indicators based on different information respond differently to the COVID-19 outbreak. These results provide direct evidence that volume imparts information to technical analysis independently of price.

Zhongbao Zhou, Zhengyang Song, Helu Xiao, Tiantian Ren

No abstract is available for this record.

Zynobia Barson, Peterson Owusu, Anokye M. Adam, Emmanuel Asafo‐Adjei

We employ a frequency‐dependent asymmetric and causality analysis to investigate the connectedness between gold and cryptocurrencies during the COVID‐19 pandemic. Hence, the variational mode decomposition‐based quantile regression is utilised. Findings from the study divulge that the variational mode functions at the lower quantiles are mostly significant and negative indicating that gold acts as a safe haven, a diversifier at most market conditions with insignificant coefficients, and a hedge at normal market conditions for most cryptocurrencies at various investment horizons. Particularly, hedging benefits mostly occur in the short‐ and medium‐term for Bitcoin and Ripple, as well as Bitcoin and Dogecoin in the long‐term with gold. This implies that there is high persistence in the hedging properties of gold with Bitcoin, followed by Ripple. We notice more significant relationship between gold and some cryptocurrencies in the long‐term of the COVID‐19 pandemic relative to the medium‐term emphasising the delayed responses of prices to information. Investors are recommended to be observant and mindful of investing in these markets due to the different dynamics.

Zaghum Umar, Afsheen Abrar, Adam Zaremba, Тамара Теплова · 5 authors

No abstract is available for this record.

Yizhi Wang

The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public’s attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590m news stories from the LexisNexis News & Business database and applies the historical decomposition to assess the historical variations of the NFTsAI. Then the empirical analysis is performed via a TVP-VAR volatility spillover connectedness model. The empirical results show that NFTsAI indicates NFT markets are dominated by cryptocurrency, DeFi, equity, bond, commodity, F.X. and gold markets. And NFT markets are volatility spillover receivers. In addition, NFT assets could impede financial contagion and have significant diversification benefits. Employing a panel pooled OLS regression model as a supplementary analysis and a GARCH-MIDAS model as a robustness test. This study reveals that NFTsAI has sufficient power to explain the return of NFT assets from a fixed effect perspective, and NFTsAI contains useful forecasting information for both short and long-term volatility of NFT markets, separately. The new NFTsAI and the empirical findings contain useful insights for risk-averse investors, portfolio managers, institutional investors, academics and financial policy regulators.

Carol Alexander, Arben Imeraj

We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data on hourly historical bitcoin option prices reveals that bitcoin implied volatility curves behave very differently from those of equity index options. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument because the basis risk for the perpetual is very much smaller than it is for calendar futures. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black–Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30% for out-of-the-money puts, and reach an average of 15% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.

Elie Bouri, Ladislav Krištoufek, Nehmé Azoury

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.

Donglian Ma, Hisashi Tanizaki

Abstract In this study, an investigation is conducted into the phenomenon of price clustering in Bitcoin (BTC) denominated in the Japanese yen (JPY). It answers two questions using tick-by-tick data. The first is whether price clustering exists in BTC/JPY transactions, and the other is how the scale of price clustering varies throughout a trading day. With the assistance of statistical measures, the last two digits of BTC price were discovered to cluster at the numbers that end with ’00’. In addition, the scales of BTC/JPY clustering at ’00’ tended to decline at the specific hour intervals. This study contributes to the emerging literature on price clustering and investor behavior.

Nusret Cakici, Syed Jawad Hussain Shahzad, Barbara Będowska-Sójka, Adam Zaremba

No abstract is available for this record.

Barbara Będowska-Sójka, Agata Kliber

The article aims to verify whether cryptocurrencies can hedge extreme price movements in Brent crude oil. The COVID-19 pandemic revealed that oil prices are heavily influenced by economic uncertainty and the mobility factor. We analyse Brent crude oil prices from February 10, 2020, to February 10, 2022. We consider Bitcoin, BNB, Ether, Tether, and USD Coin, the top five cryptocurrencies by market capitalization, as possible hedges. We explore their potential to protect oil investments using two approaches. The first focuses on price movement, and the second one on minimizing portfolio volatility. We use three modelling techniques: asymmetric causality in prices, a threshold vector-autoregressive model for returns, and dynamic conditional correlation analysis. We show that while stablecoins provide the best protection against downward movements in oil prices, they do not reduce investment volatility.

Carlos Trucíos, James W. Taylor

Abstract Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long‐memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression‐based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID‐19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

Nisarg Patel, Raj Parekh, Nihar Thakkar, Rajesh Gupta · 8 authors

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However,Dashcoin is an individual cryptocurrency, but it is derived fromBitcoinandLitecoin. The change inBitcoinandLitecoinprices affects theDashcoin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction ofDashcoin through the past days’ prices ofDash,Litecoin, andBitcoinas they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.