Leverage effects, volatility innovation spillovers, and inter- and intra-market asymmetric dependencies in cryptocurrencies and CFDs on equity indices: Evidence from high-frequency around-the-clock data

Fahad Ali and Muhammad Usman Khurram

International Review of Financial Analysis, 2025, vol. 107, issue C

Abstract: Using 5-min high-frequency data around-the-clock, this study is the first to comprehensively examine asymmetric leverage effects, volatility innovation spillovers, and dependencies between six major developed equity markets – US, UK, France, Germany, Australia, and Japan – and seven major well-studied cryptocurrencies: Bitcoin (BTC), Litecoin (LTC), Ether (ETH), Dash (DSH), EOS, Tron (TRX), and Basic Attention Token (BAT). We employ the contract for differences (CFDs) on the equity indices for data during non-trading periods, several symmetric and asymmetric GARCH-based econometric tools, and a comprehensive sample period spanning from August 5, 2019, to January 31, 2023, consisting of 363,024 observations for each asset. Using sign bias tests, we first identify that leverage effects – a stronger impact of negative innovations on the conditional volatility of returns than the positive innovations of the same size – in cryptocurrencies are more pronounced in the post-Covid period, whereas in equities, they exisit across the sample period, except the first year of the Covid-19, consistent with the notion of fear of missing out during rapid recovery and boom periods. This asymmetric leverage effect is robust using the SAARCH, TGARCH, and APARCH models. We document that spillovers among cryptocurrencies and between equities and cryptocurrencies due to innovation (lagged standardized errors) are stronger than those of persistence (lagged conditional covariances). Regarding inter-class asset hedging opportunities, which we measure via negative coefficients of the innovation term, we find that pairing UK-LTC, US-DSH, Germany-DSH, US-EOS, Japan-EOS, and Germany-TRX are most likely to offer several diversification benefits to investors. Additionally, we examine asymmetric dynamic conditional correlations in inter-class asset settings and find that BAT and LTC among cryptocurrencies and the Australian and French markets among equities are weakly connected with other asset classes, suggesting their potential role in portfolio optimization. Our findings hold practical importance and guide investors in making hedging and diversification decisions and in optimizing cryptocurrency-equity portfolios during different economic, geopolitical, and market conditions around the clock.

Keywords: Leverage effect; Volatility innovation; Spillover effect; Asymmetric dependencies; High-frequency data; Inter-class asset analysis (search for similar items in EconPapers)
JEL-codes: F21 F36 G15 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:107:y:2025:i:c:s1057521925007239

DOI: 10.1016/j.irfa.2025.104636

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