The Asymmetric Effect of Panic Index on Cryptocurrencies

Abstract

The Covid 19 pandemic is the first major crisis facing cryptocurrencies. Therefore, the reaction of the cryptocurrency markets is important. News about epidemics affects investors' decisions. Panic index (PIndex) is an index created from news about the Covid 19 outbreak. In the study, it is used to measure the impact of decisions on the crypto money market. As cryptocurrencies, Bitcoin (BTC), Etherium (ETH), and Ripple (XRP), which have the highest transaction volume in the crypto money market, are included in the analysis. The relationship between Panic Index and the three major cryptocurrencies with the largest share in the cryptocurrency market was investigated by Ardl and Hatemi-J asymmetric causality test. Traditional causality tests acknowledge that the effects of positive and negative changes are the same. However, there may be asymmetric information and different investor behaviors in financial markets. In the study, Hatemi-J [1] Asymmetric Causality Test was conducted to examine the asymmetric relationship and symmetric relationship between Pindex and cryptocurrencies by separating them into positive and negative shocks. According to the results of the Hatemi-J causality analysis, positive shocks in the panic index are the cause of negative shocks for all cryptocurrencies. In other words, increases in the panic index are caused to fall the value of Bitcoin, Ethereum, and Ripple cryptocurrencies decrease. The results show that cryptocurrencies were not a safe haven for the investor during the Covid 19 period, as they acted similarly to other financial assets.

Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality

“…The estimated values of the long-run coefficients equal 0.008382 , 0.00099 , and 0.03054 , while the coefficient equals 0.006814 , 0.00072 , and 0.028211 . Unlike Vurur ( 2021 ), a 1% increase or decrease in PI, HI, and FNI increases Bitcoin returns by 0.008382% (0.006814%), 0.00099% (0.00072%), and 0.03054% (0.028211%), respectively. The outcomes support Béjaoui et al ( 2021 ), who claimed that the pandemic fosters investing in Bitcoin.…”

“…3 presents the results. The CUSUM test provides a plot of the long- and short-term coefficients of the cumulative error terms of the number of observations with a 5% confidence interval, while the CUSUMQ test assesses the coefficients by squaring the cumulative error terms (Vurur 2021 ). The recursive and squared recursive residuals are drawn against breakpoints for CUSUM and CUSUMQ, respectively.…”

“…Umar et al ( 2021 ) used only the Media Coverage Index and established that virtual currencies are net shock transmitters, whereas fiat currency is a net receiver. Vurur ( 2021 ) employed only the Panic Index and found that digital coins are more vulnerable to adverse headlines. Umar and Gubareva ( 2020 ) covered only Coronavirus Panic Index but explored Bloomberg Galaxy Crypto Index.…”

“…In contrast, this study employs the exponential GARCH (EGARCH) model, which permits the capture of volatility asymmetry (Tiwari et al 2019 ). Furthermore, prior papers employed the robust least squares estimation method (Anamika et al 2021), difference-in-difference framework (Chen et al 2022 ), quantile regression model (Rahadian and Nurfitriani, 2022 ), or linear autoregressive distributed lag (ARDL) models (Demir et al 2020 ; Vurur 2021 ; Havidz et al 2022b ) which do not consider the asymmetric effect. Therefore, we have used the nonlinear autoregressive distributed lag (NARDL) model of Shin et al ( 2014 ) to simultaneously capture both asymmetric long and short-run relationships among variables (González et al 2021 ; Long et al 2021 ).…”

COVID-19 full vaccination and blockchain size: empirical evidence from the cryptocurrency market

“…, 2021a, b). The panic index was utilized to examine its impact on the volatility of major fiats and the Bloomberg Galaxy Crypto Index (BGCI) (Umar and Gubareva, 2020) as well as on its asymmetric effect on cryptocurrency values (Vurur, 2021). Along with the panic index, the sentiment index was examined for Dow Jones returns (Haroon and Rizvi, 2020) and global equities (Huynh et al.…”