Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

Huynh, Toan Luu Duc

doi:10.3390/jrfm12020052

Cited by 82 publications

(22 citation statements)

References 87 publications

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“…"What drives Bitcoin" has been one of the most commonly investigated questions by researchers in recent years. While the current body of literature also includes traditional determinants such as supply and demand (Ciaian et al, 2016) and trading volume (Balcilar et al, 2017), the valuation of cryptocurrencies differs fundamentally from that of traditional instruments, investor attention (Nasir et al, 2019) and spillover effects (Luu Duc Huynh, 2019;Huynh et al, 2018). Unlike fiat money, cryptocurrencies and bitcoin are not issued by any government or legal entity but have a fixed supply therefore rendering traditional fiat money valuation models with the assumption of unlimited supply inapplicable.…”

Section: Introductionmentioning

confidence: 99%

Do FEARS drive Bitcoin?

Burggraf

Huynh

Rudolf

et al. 2020

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PurposeThis study examines the prediction power of investor sentiment on Bitcoin return.Design/methodology/approachWe construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.).FindingsUsing a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions.Research limitations/implicationsThe findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency.Originality/valueThis study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.

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Section: Introductionmentioning

confidence: 99%

Do FEARS drive Bitcoin?

Burggraf

Huynh

Rudolf

et al. 2020

RBF

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“…This approach is employed to study the substitution and reinforcement effects among all analyzed cryptocurrencies. Recent existing digital finance literature adopted variants of the vector autoregressive approach to analyze the transmission between cryptocurrencies (Bação et al, 2018;Huynh, 2019). Thus, we analyze the effects of all cryptocurrencies on all other ones in the USD market and across all the selected emerging markets economies.…”

Section: Vector Error Correction (Vec) Modelmentioning

confidence: 99%

Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?

et al. 2021

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We analyzed cryptocurrency dynamics in the global U.S. dollar–denominated market and the emerging market economies (EMEs) with a view to ascertaining whether activities in these markets are predominantly shaped by reinforcement or substitution effect. Cryptocurrencies analyzed include the Bitcoins, Ethereum, Litecoin, Steller, Bitcoin Cash, and USD Tether. The results suggest that, on average, correlation between digital assets in the cryptocurrencies’ ecosystem is positive. However, there is evidence of an outlier with respect to the USD Tether (USDT) in the global market, revealing that the USDT is negatively associated with all other cryptocurrencies. This is supported by the dynamic regression results that provided evidence of reinforcement effect in favor of the USDT in the global crypto market, thus confirming the status of the USDT as “Stablecoin” as it is pegged 1:1 to USD. In the global market context, the results also revealed that USDT/USD returns had identical outliers that could portend lesser chances of extreme gains or losses compared with suggestions of extreme gains or losses in the EMEs. Furthermore, USDT did not seem to have similar evolution in the EMEs where it had relatively marginal influence in the markets. The vector error correction (VEC) estimate showed mixed results between Altcoins in all the markets; moreover, our finding showed that reinforcement effects hold in favor of Steller (XLM) both in the Russian ruble and Indian rupee crypto markets, whereas the Chinese yuan crypto market was predominantly characterized by substitution effect in favor of Bitcoin.

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“…They also observe that connectedness of overall returns falls significantly immediately before Bitcoin price hype events. Luu Duc Huynh [56] studies spillover risks on cryptocurrency markets from quite a different perspective by using Student's-t Copulas and a SVAR (Structural Vector Autoregressive Model) Granger causality. This study find that Ether is more probable than any other cryptocurrency to be independent in this market, where Bitcoin is more inclined to be the spillover effect recipient, noting that investors must pay more attention to 'bad news'.…”

Section: Literature Reviewmentioning

confidence: 99%

Bitcoin and Fiat Currency Interactions: Surprising Results from Asian Giants

2021

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This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018.

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Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas

Cited by 82 publications

References 87 publications

Do FEARS drive Bitcoin?

Do FEARS drive Bitcoin?

Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?

Bitcoin and Fiat Currency Interactions: Surprising Results from Asian Giants

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