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Original title:
Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness
Authors: Šíla, Jan ; Kočenda, Evžen ; Kukačka, Jiří ; Krištoufek, Ladislav
Document type: Research reports
Year: 2023
Language: eng
Series: IES Working Papers, volume: 24/2023
Abstract: Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. The results suggest that crypto connectedness reflects important events and exhibits more variable and cyclical dynamics than those of traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that information about market downturns spills over substantially faster than news about comparable market surges. Overall, the connectedness dynamics are predominantly driven by fundamental crypto factors, while the asymmetry measure also depends on macro factors such as the VIX index and the expected inflation.
Keywords: Asymmetric effects; Cryptocurrency; Dynamic connectedness; Volatility
Project no.: GA23-06606S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2023/E/kocenda-0577571.pdf
Original record: https://hdl.handle.net/11104/0347641
Permalink: http://www.nusl.cz/ntk/nusl-537189
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Authors: Šíla, Jan ; Kočenda, Evžen ; Kukačka, Jiří ; Krištoufek, Ladislav
Document type: Research reports
Year: 2023
Language: eng
Series: IES Working Papers, volume: 24/2023
Abstract: Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. The results suggest that crypto connectedness reflects important events and exhibits more variable and cyclical dynamics than those of traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that information about market downturns spills over substantially faster than news about comparable market surges. Overall, the connectedness dynamics are predominantly driven by fundamental crypto factors, while the asymmetry measure also depends on macro factors such as the VIX index and the expected inflation.
Keywords: Asymmetric effects; Cryptocurrency; Dynamic connectedness; Volatility
Project no.: GA23-06606S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2023/E/kocenda-0577571.pdf
Original record: https://hdl.handle.net/11104/0347641
Permalink: http://www.nusl.cz/ntk/nusl-537189
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Record created 2023-12-17, last modified 2024-04-15