The Performance of Top Coronavirus Vaccine Stocks during COVID-19 Pandemic: A Multifractal Analysis

Bu çalışma, 9 Aralık 2019-6 Ocak 2022 arasında beş aşı hissesinin (Pfizer, BioNTech, Moderna, Johnson&Johnson ve AstraZeneca) koronavirüs pandemisinde işgünü haftalık verileri temelinde çoklu fraktal özelliklerinin nasıl etkilendiǧini araştırmaktadır. Çalışmanın temel amacı sürü yatırımının ve piyasa etkinlik düzeyinin aşılama dönemi öncesinde (9 Aralık 2019 - 8 Aralık 2020) ve sonrasında (9 Aralık 2020 - 6 Ocak 2022) deǧişiminin varlıǧını ortaya koymaktır. Genelleştirilmiş Hurst üsleri çoklu fraktal eǧiliminden arındırılmış dalgalanma analizi yoluyla hesaplanmaktadır. Genel olarak, ampirik sonuçlar COVID-19 salgını sırasında her aşı hissesi için çoklu fraktal varlıǧın mevcut olduǧunu göstermektedir. Ayrıca çoklu fraktal özelliklere göre etkinlik düzeyi aşı hisseleri arasında farklılık göstermektedir. Elde edilen sonuçlar aşılama sonrası dönemin BioNTech ve Moderna hisse senetleri için sürü yatırımına daha yatkın olduǧunu göstermektedir. Güncel salgının etkileri göz önüne alındıǧında COVID-19 aşılama sürecinin öncesi ve sonrasında en yüksek MLM (etkinsizlik) indeks deǧerinin BioNTech'e ait olduǧu ortaya konmaktadır.Alternate :This study assesses how the coronavirus pandemic (COVID-19) affects the 5-day week multifractal properties of five vaccine stocks (i.e., Pfizer, BioNTech, Moderna, Johnson & Johnson, and AstraZeneca) using weekday index data ranging from 9 December 2019 to 6 January 2022. The main concern is to document whether the presence of herd investing and the level of market efficiency changed between pre-vaccination (i.e., 9 December 2019 - 8 December 2020) and post-vaccination (i.e., 9 December 2020 - 6 January 2022). The generalised Hurst exponents are calculated through multifractal detrended fluctuation analysis. Overall, the empirical results show multifractality for each vaccine stock during the COVID-19 outbreak. Besides, the efficiency level differs among the vaccine stocks based on multifractal properties. The results indicate that the post-vaccination period is more prone to herd investing in BioNTech and Moderna stocks. Considering the impacts of this far-reaching outbreak, the highest MLM (inefficiency) index value is also attributed to BioNTech before and after the COVID-19 vaccination process.

Foreign Exchange Volatility and the Bubble Formation in Financial Markets: Evidence From The COVID-19 Pandemic

This paper applies recursive right-tailed unit root tests to detect bubble activity for Turkish Lira against financially most-traded five currencies (i.e., the US Dollar (USD/TRY), the British pound (GBP/TRY), the Euro (EUR/TRY), the Chinese Yuan (CNY/TRY) and the Russian Ruble (RUB/TRY)) over January 2, 2015 to February 12, 2021. It can be identified from the Supremum Augmented Dickey-Fuller (SADF) and the Generalized Supremum Augmented Dickey-Fuller (GSADF) tests statistics that there is a high degree of evidence of bubble activity which characterizes all five exchange rates both in the full-sample period and in the sub-periods, including the pre-COVID-19 era (January 2, 2015 to November 15, 2019) and the COVID-19 era (November 18, 2019 to February 12, 2021). The empirical results also indicate that positive bubbles are common for each selected exchange rate and the multiple bubbles were intensified during the COVID-19 period, referring that forex markets became relatively more inefficient compared to the pre-COVID-19 period.

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis.

This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods-namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)-are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.