Abstract
Since the advent of Bitcoin, the cryptocurrency market has become an important financial market. However, due to the existence of the cryptocurrency bubble, investors face more difficulties in risk portfolios. We adopt wavelet packet decomposition, nonlinear Granger causality test, risk spillover network, and STVAR model; retain the mature research of multiscale systemic risk based on time and frequency; and thus extend systemic risk to different regimes. We found that when frequency is combined with regimes, the risk spillover center will undergo subversive changes in the long run. We also proposed that BTC will be more robust at extreme values (like longest and shortest periods), while cryptocurrencies with smaller market capitalization will be stronger in the medium term. At the same time, the recession period will also spur on it.
Highlights
In 2009, Nakamoto [1] proposed the concept of cryptocurrency and a proof system for encrypted payment. anks to its blockchain technology, Bitcoin does not involve intermediaries such as clearinghouses and is independent of sovereign risk [2]
Many studies focus on the systemic risks of cryptocurrencies but discuss only linear and nonlinear models based on a one-dimensional perspective
Using a vector autoregressive model based on quantiles, Bouri et al point out that cryptocurrency has an asymmetric effect between the return and overflow behavior of the low quantile and the upper quantile, which provides a nonlinear optimization for systemic risk opportunity
Summary
In 2009, Nakamoto [1] proposed the concept of cryptocurrency and a proof system for encrypted payment. anks to its blockchain technology, Bitcoin does not involve intermediaries such as clearinghouses and is independent of sovereign risk [2]. Due to its nonpolitical nature and commodity attributes [4], Bitcoin, the cryptocurrency of largest market value, is often compared to gold, which serves as a safe haven for investors to carry out risk portfolios [5]. Combining wavelet decomposition and Granger causality test, Li et al [17] found time asymmetry in the causal relationship between the return of different cryptocurrencies and investors’ attention. Fruehwirt et al [19] used wavelet-coherence analysis to measure intraday data (5-min resolution) to study the dynamic time-frequency conversion between cryptocurrencies. Many studies focus on the systemic risks of cryptocurrencies but discuss only linear and nonlinear models based on a one-dimensional perspective. E section describes the recent emerging literature on cryptocurrencies and its research methods for systemic risk.
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