Identification of high-frequency volatility and risk prevention in cryptocurrencies

Abstract

Purpose (1) How can high-frequency data be utilized more effectively to identify and extract various risks in the cryptocurrency market? (2) Do the risk characteristics of different cryptocurrencies exhibit consistency or variability across multiple dimensions? (3) Based on these risk characteristics, how can more precise risk prevention and hedging strategies be provided to investors? Design/methodology/approach (1) The threshold optimal detection (TOD) model can accurately separate jump and continuous behaviors by setting appropriate threshold values, effectively identifying extreme price fluctuations in the market. (2) The method for separating trend and cyclical behaviors can be achieved through filter design and application. This approach effectively distinguishes between long-term and short-term fluctuations in the cryptocurrency market, enabling a clearer analysis of market risks across different time scales. (3) We use linear regression to estimate the sensitivity of cryptocurrency returns to different risk factors, represented by the β coefficient. (4) The time-frequency domain characteristics of wavelet coherence analysis allow for simultaneous examination of risk frequencies in the cryptocurrency market, providing a more comprehensive understanding of market behavior. Findings First, by integrating high-frequency data with multidimensional risk decomposition techniques, this study systematically identifies and analyzes various risk features within the cryptocurrency market, enriching the existing literature on high-frequency volatility and risk identification. Second, this paper innovatively decomposes continuous risk into trend risk and cyclical risk, providing a more refined framework for managing market volatility risks. Finally, the paper proposes differentiated response strategies tailored to various risk characteristics, particularly in jump risk management, offering practical guidance on the use of derivatives such as options to provide actionable solutions for investors. Originality/value This paper proposes a multidimensional risk extraction and analysis method by examining high-frequency data of nine major cryptocurrencies from December 2020 to July 2024. It not only explores the characteristics of jump risk and continuous risk but also further decomposes continuous risk into trend risk and cyclical risk using filtering techniques, revealing the heterogeneous performance of different cryptocurrencies in both long-term and short-term volatility. This multidimensional risk analysis allows for a more comprehensive capture of various market fluctuation patterns, providing investors and risk managers with more effective response strategies.