Blockchain Papers

S. Sapna, Biju R. Mohan

No abstract is available for this record.

Tamimu Mohammed Gadafi, Touray Musa, Liu Yawen

This study introduces a novel hybrid stochastic modeling framework for simulating Ethereum price dynamics by integrating Poisson and Gaussian processes. The model captures both abrupt price jumps, modeled using a Poisson process, and continuous price variations, represented by a Gaussian process. Our analysis reveals that significant price fluctuations occur approximately every 4.33 days, with an average daily return of 0.0041 and an annualized volatility of 0.8631, underscoring the extreme volatility inherent in Ethereum’s market behavior. By combining these processes, the model effectively encapsulates the intrinsic price patterns of Ethereum, including persistent oscillations and sudden surges. Simulations of future price trajectories demonstrate the model’s efficacy in replicating real world Ethereum price dynamics, offering valuable insights for traders and analysts in devising risk management strategies and making informed decisions in highly volatile cryptocurrency markets. The findings highlight the importance of hybrid models in addressing the unique challenges of modeling Ethereum’s price behavior.

Florentin Şerban

Traditional portfolio optimization techniques predominantly rely on the classical mean–variance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional mean–variance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—with market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in return–risk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.

Sergiy Andriychuk

Cryptocurrencies have rapidly emerged as a significant financial asset class, influencing global monetary systems and financial markets. However, their extreme volatility, speculative nature, and evolving regulatory landscape pose challenges to investors, policymakers, and financial analysts. This study presents an in-depth quantitative analysis of cryptocurrency volatility and risk assessment, focusing on Bitcoin (BTC-USD) and its correlation with traditional financial assets, including the EUR/USD exchange rate and S&P 500 index. Our research employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to measure the dynamic volatility patterns of Bitcoin, revealing the asset’s substantial fluctuations over time and its sensitivity to market shocks. Additionally, we utilize Monte Carlo simulations to forecast potential future price movements of Bitcoin, highlighting risk scenarios and the probability distribution of price trajectories over a one-year period. The Value-at-Risk (VaR) model is implemented to estimate potential losses within a given confidence interval, providing a robust measure of downside risk. Furthermore, the study examines the integration of cryptocurrency markets with traditional financial instruments by analyzing cross-asset correlations and volatility spillover effects. The findings suggest that while Bitcoin remains a highly volatile asset, its correlation with the broader financial system is increasing, indicating a potential shift towards mainstream financial adoption. The results contribute to the ongoing debate on whether cryptocurrencies serve primarily as speculative instruments or as viable components of diversified investment portfolios. These insights are valuable for institutional investors, risk managers, and policymakers in designing more effective risk mitigation strategies for cryptocurrency investments.

Haoran Wu, Meng‐Lan Yueh

This paper applies the Lévy-GJR-GARCH model to explore the empirical dynamics of Bitcoin, Ethereum, and Ripple. It highlights volatility clustering, pronounced skewness, and high kurtosis in cryptocurrency markets. The study finds that models integrating innovation distributions more accurately capture and explain the volatility processes and tail risks in these assets. Advanced models, especially those accounting for extreme tail-end and asymmetric jump effects, are better suited for adapting to market changes and providing precise risk indicators, effectively identifying potential losses.

호진 이, Kyung-Jin Park

No abstract is available for this record.

Bernhard K. Meister, Henry Price

In this chapter, structures that generate yield in cryptofinance will be analysed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin’s finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment.

Joel Hasbrouck, Thomas J Rivera, Fahad Saleh

We develop an economic model of a decentralized exchange with concentrated liquidity (e.g., Uniswap v3 and v4), with a particular focus on the economics of liquidity provision. We demonstrate that providing liquidity for a risky/risk-free asset pool is comparable to investing in a covered call, except that the call option therein is sold at intrinsic rather than market value. Hence, when providing liquidity, liquidity providers forgo the time premium of the call option in exchange for fees, and thus equilibrium liquidity provision decreases in the time premium. Finally, we provide an expression for equilibrium liquidity provision that is useful for empirical work. This paper has been This paper was accepted by Lin William Cong for the Virtual Special Issue on Digital Finance.

Srisht Fateh Singh, Vladyslav Nekriach, Panagiotis Michalopoulos, Andreas Veneris · 5 authors

ABSTRACT This paper investigates the current landscape of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility, while also allowing them to capitalize on future volatility trends. The paper discusses these applications of option contracts in the context of decentralized finance (DeFi), emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of options contracts on cryptocurrencies, decentralized platforms account for less than 1 % of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption‐related challenges that decentralized exchanges face and provides commentary on existing platform responses. Subsequently, the paper analyzes the impact of absent options markets on the inefficiencies of automated market maker liquidity. It examines historical on‐chain data for 14 ERC20 token pairs on Ethereum. The analysis shows 1143 instances in which deeper liquidity levels, as high as more, could have been achieved by establishing an options market.

João Pedro Malim Franco, Márcio Poletti Laurini

No abstract is available for this record.

Warodom Werapun, Naratorn Boonpeam, Esther Sangiamkul, Jakapan Suaboot

The emergence of decentralized finance (DeFi) allows arbitrageurs to obtain risk-free income from price gaps of cryptocurrency tokens in many global markets. Several automated arbitrage techniques have been invented to profit from single or multiple platforms, including Centralized and Decentralized Exchange (CEX and DEX), triangular, and DEX-Fait. This paper proposes the arbitrage strategy of cross-cryptocurrency exchanges (ASCEX), a novel automated arbitrage strategy for CEX-DEX platforms, to maximize profit and loss (PNL) using a token route searching algorithm. Based on feature comparison, ASCEX outperforms the existing trading strategies available. Our actual trade experiment shows that ASCEX can generate up to 0.95% monthly risk-free profit compared to 0.34% trading on DEX alone.

Ehsan Mohammadian Amiri, Akbar Esfahanipour

This study aims to develop a dynamic portfolio trading system for high-risk profiles of cryptocurrencies in two phases: 1) portfolio selection and 2) portfolio construction. In the first phase, we propose a novel algorithmic trading model applying a Convolutional Neural Network (CNN) using a 2-D convolution layer with eight kernels of 3×3 sizes based on the prediction of selected technical indicators to predict buy/sell trading signals. To effectively increase the accuracy of the CNN model, first, the H-step ahead predictions of the selected technical indicators based on Long-short-term-memory (LSTM) along with the indicators themselves have been used to construct input matrices of the CNN model. A new price labeling approach was proposed to determine buying or selling points using the zigzag indicator (ZZ) in our CNN model. Assets with buy signals have been selected to construct the proposed portfolio. In the second phase, we propose a novel robust approach based on Holt-Winters-Multiplicative (HWM) to determine the realized crypto portfolio weights robustly by considering the seasonal effects. The experimental results show that our developed system outperforms the competing models for 30 cryptocurrencies with a high-risk profile in the two phases.

Mojtaba Safari, Nawapon Nakharutai, Phisanu Chiawkhun, Parkpoom Phetpradap

No abstract is available for this record.

Authors unavailable

<h2 style="text-align: center;"><span style="color:#e74c3c;">Volume 30&nbsp;Number 1, 2025</span></h2> <h2 style="text-align: center;">Quantile Time-Frequency Price Connectedness Between Classic Cryptocurrencies, NFT, DEFI, and B...</h2>

Pawel Lachowicz

No abstract is available for this record.

N.-H. Chen, Jennifer Chan, Linh Nghiem

No abstract is available for this record.

Bahram Alidaee, Haibo Wang, Wendy Wang

Since the introduction of Modern Portfolio Theory (MPT) in 1952, its practical applications, associated challenges, and computational efficiency on large and high-frequency datasets, particularly datasets not used to develop and optimize the model, have drawn extensive research interest. This study has examined the performance of various portfolio models that have explored the concept of MPT on U.S. stock and cryptocurrency markets, i.e., discrete Markowitz portfolio selection (DMPS), the optimal dynamic portfolio (ODP), the binary unconstrained ODP (BUODP) with a quantum annealing solver, and the 1/N naive diversification (ND). Their performance is then compared to the indices that measure the performance of corresponding market exchange-traded funds (ETFs) for stock markets. Our findings show that the DMPS and ODP perform better than other models, delivering better returns in a shorter period. Both run significantly faster than the BUODP (with quantum annealing) with computation time of approximately 0.5 seconds for the S&P 400, 500, and 600 markets whereas BUODP takes 30 seconds; they also mitigate risk and outperform ETFs and ND model for the out-of-sample test with diversified portfolios combining NASDAQ stocks and cryptocurrencies. Furthermore, we have analyzed the impact of data with different frequency intervals, i.e., weekly, daily, hourly, and one-minute, on portfolio performance of the stock markets. The results suggest that data collection frequencies do not make differences in portfolio selections and weights. This study contributes to the advancement of portfolio theory, providing insights and practical values, especially in addressing computational efficiency for high-frequency and large-scale datasets and saving computational costs.

<p>Zhongyuan Xu</p>

The cryptocurrency market poses a huge challenge to portfolio optimization due to its high volatility and complex market dynamics. To address these issues, this paper uses reinforcement learning (RL) algorithms for dynamic portfolio optimization, aiming to improve the return and risk control capabilities of the portfolio through intelligent decision-making. This paper adopts a strategy based on deep reinforcement learning. By interacting with the cryptocurrency market, the agent can continuously optimize asset allocation, maximize investment returns while controlling volatility. The experimental results show that compared with traditional strategies, the reinforcement learning model has obvious advantages in key indicators such as cumulative return rate, annualized volatility, maximum drawdown and Sharpe ratio. Specifically, the cumulative return rate of the reinforcement learning model reaches 85.12%, the annualized volatility is 45.76%, and the maximum drawdown is controlled at -22.34%, showing strong income acquisition and risk management capabilities. In addition, the dynamic adjustment of asset allocation has optimized the weights of various cryptocurrencies, effectively dispersed risks, and improved the overall performance of the investment portfolio.

Alshaikh A. Shokeralla

No abstract is available for this record.

Aubain Nzokem

The paper presents two series representations of a L{\'e}vy process for the Generalized Tempered Stable (GTS) distribution: a series representation generated by the inverse tail integral and a short noise representation. Both series representations are used to simulate the daily returns of Bitcoin and Ethereum. The Q-Q plot analysis shows smooth linear patterns, indicating strong agreement between the empirical and theoretical GTS distributions.

William C. Johnson

No abstract is available for this record.

Matúš Horváth, Tomáš Výrost

No abstract is available for this record.

Aidan Christopher Joneleit

The decentralized financial system that forms the basis of crypto assets is highly volatile and constantly changing, making the process of valuation one of the thorniest challenges of the modern-day finance sector. This study deals primarily with the analysis of several factors through which the value of cryptocurrency is determined and also seeks to stress how much caution is needed while addressing both risks and rewards. By employing multivariable and stochastic analyses, the study deconstructs the various factors at play in the bitcoin market. It is clear from the result that the volatile environment of the digital currency is a combination of one’s mood, new laws, and technological development. Thus, the findings underline the information that while it is possible to become an exception and make a brilliant rise at the financial top, there is also a large and often unpredictable downside involved. It is a valuable resource for the policymakers who have to come up with the legislation to regulate innovation without compromising the markets’ veracity, these results can be a useful tool for all investors who struggle to make the right decisions in the world of decentralized cryptocurrencies.

Dias Saparbekov

This study assesses the out-of-sample forecasting capabilities of risk-neutral density models in Bitcoin options market, with a focus on the Normal Inverse Gaussian (NIG) density. Understanding forward-looking price dynamics becomes critical as cryptocurrencies continue to gain a reputation in financial markets. This research examines how the NIG model, with its capability to capture skewness and kurtosis, compares to the benchmark log-normal (LN) distribution. The analysis applies the likelihood ratio test to evaluate the predictive performance of the models. As a result, NIG model improves the accuracy of tail forecasts, outperforming LN in capturing extreme market movements, which holds implications for risk management and market timing in Bitcoin market.