•Wavelet quantile Correlation, a new intuitive measure is proposed to test the safe haven property of bitcoin and gold across different timescales.•Gold is found to be a better safe haven instrument ...compared to bitcoin across different timescales for all the six markets.•Bitcoin exhibit safe haven property for NSE50 for the short run.For NASDAQ and EUROSTOXX, Bitcoin exhibit short run and long run safe haven property.•Bitcoin is better suited as a diversifier.•Gold can be employed as a hedge against stock market fluctuations across different timescales.
We test the suitability of Gold and Bitcoin as safe-haven instruments in the backdrop of the Covid-19 related equity market meltdown by implementing the newly proposed Wavelet Quantile Correlation. We employ daily returns of Bitcoin, Gold, DJIA, CAC40, NSE50, S&P 500, NASDAQ, and EUROSTOXX from 05–01–2015 to 31–12–2020. Our results show that Gold consistently exhibits safe haven properties for all the markets except NSE in the long and short run, while Bitcoin provided mixed results. We find that Gold can act as an effective hedge and diversifier as well.
Intensive measures were initiated by the state with the help of central government health authorities including the National Centre for Disease Control, National Institute of Epidemiology, Indian ...Council of Medical Research (ICMR), and experts from AIIMS, New Delhi. 9 The continued degradation and fragmentation of the natural habitats of bats has resulted in an increased overlap of bat, domestic animals, and human ecologies, which has created increased opportunities for emergence of bat-borne zoonotic diseases. Design of forest management strategies that preserve bats' roosting and foraging landscapes and prevention of viral spillover from bats to humans require a complete understanding of the ecological narrative, linking of bat habitat with human and livestock activity to explain when, where, and why a virus emerges.
We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely ...Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given crypto-currency prices are estimated across different time-scales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.
In recent years, cryptocurrencies have been considered as an asset by public investors and received much research attention. It is a volatile asset, thus predicting its prices is not easy due to the ...dependence on multiple external factors. Machine learning models are becoming popular for cryptocurrency price predictions, while also considering social media data. In this article, we analyze the rate of return of three cryptocurrencies (Bitcoin, Ether, Binance) from an investor point of view. We also consider three traditional external variables: S&P 500 stock market index, gold price, and volatility index. The rate of return prediction is based on three stages. First, we analyze the correlation between the cryptocurrency returns and the traditional external variables. Next, we focus on the influential social media variables (from Twitter, Reddit, and Wikipedia). Later, we use these variables to improve prediction accuracy. Third, we test how the standard time series models (such as ARIMA and SARIMA) and four machine learning models (such as RNN, LSTM, GRU and Bi-LSTM) predict one-day rate of return. Finally, we also analyze the risk of investing in each cryptocurrencies using value risk statistics. Overall, our result shows no correlation between cryptocurrency returns and three traditional external variables. Second, we found that overall LSTM model is the best, GRU is the second-best prediction model, while the impact of the social media variables varies depending on the cryptocurrencies. Finally, we also found that investment in gold offers better returns than cryptocurrency during Covid-19-like situations.
This study investigates how the lockdowns during the COVID-19 outbreak affect the multifractal features of four Non-Fungible Tokens (NFTs) (i.e., Cryptokitties, Cryptopunks, SuperRare, and ...Decentraland) using daily price data ranging from 23 June 2017 to 15 February 2022. The major concern is to assess whether the presence of herd investing and the level of market inefficiency altered for the period between pre-first-lockdown (i.e., 23 June 2017–22 March 2020) and post-first-lockdown (i.e., 23 March 2020–15 February 2022). The generalized Hurst exponents are measured towards a multifractal detrended fluctuation approach. In particular, the empirical results document that multifractality exists for each NFT during the COVID-19 outbreak. Besides, the level of market inefficiency differs among the selected NFTs. The results refer to the case that the post-first-lockdown period is more prone to herd investing for Cryptokitties, Cryptopunks, and Decentraland. Furthermore, testing for MLM (inefficiency) index, the empirical findings show that Cryptokitties became more vulnerable in the post-first-lockdown period. Regarding the impacts of this far-reaching outbreak, the highest MLM (inefficiency) index value is attributed to Cryptopunks before the first lockdown and Cryptokitties after the first lockdown periods.
•Single sample preparation, Single injection covering three groups of 24 antibiotics.•Method validation performance characteristics achieved as per CD 2002/657/EC.•Quantification approach using ...method matrix-match standard calibration curve.•Reliability of the method established by participation in proficiency testing.•Protocol applicable for regulatory monitoring of antibiotics in shrimp samples.
An accurate, reliable and fast multi-residue, multi-class method using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) was developed and validated for simultaneous determination and quantification of 24 pharmacologically active substances of three different classes (Quinolones including fluoroquinolones, sulphonamides and tetracyclines) in aquaculture shrimps. Sample preparation involves extraction with acetonitrile containing 0.1% formic acid and followed by clean up with n-hexane and 0.1% methanol in water by UPLC-MS/MS within 8 min. The method was validated according to European Commission Decision 2002/657. Acceptable values were obtained for linearity (5–200 μg kg−1), specificity, Limit of Quantification (5–10 μg kg−1), recovery (between 83 and 100%), repeatability (RSD < 9%), within lab reproducibility (RSD < 15%), reproducibility (RSD ≤ 22%), decision limit (105–116 μg kg−1) and detection capability (110–132 μg kg−1). The validated method was applied to aquaculture shrimp samples from India.
Nipah Virus (NiV) is a highly fatal emerging zoonotic virus and a potential threat to global health security. Here we describe the characteristics of the NiV outbreak that occurred in Kerala, India, ...during May-June 2018.
We used real-time reverse transcription polymerase chain reaction analysis of throat swab, blood, urine, and cerebrospinal fluid specimens to detect NiV. Further, the viral genome was sequenced and subjected to phylogenetic analysis. We conducted an epidemiologic investigation to describe the outbreak and elucidate the dynamics of NiV transmission.
During 2-29 May 2018, 23 cases were identified, including the index case; 18 were laboratory confirmed. The lineage of the NiV responsible for this outbreak was closer to the Bangladesh lineage. The median age of cases was 45 years; the sex of 15 (65%) was male. The median incubation period was 9.5 days (range, 6-14 days). Of the 23 cases, 20 (87%) had respiratory symptoms. The case-fatality rate was 91%; 2 cases survived. Risk factors for infection included close proximity (ie, touching, feeding, or nursing a NiV-infected person), enabling exposure to droplet infection. The public health response included isolation of cases, contact tracing, and enforcement of hospital infection control practices.
This is the first recorded NiV outbreak in South India. Early laboratory confirmation and an immediate public health response contained the outbreak.
•We test the hedge and safe haven property of NFTs against Bitcoin and Ethereum fluctuations.•We find that NFTs exhibit hedge ability in varying degrees against Bitcoin fluctuations.•The Wavelet ...Quantile Correlation analysis shows that NFTs offer short, medium, and long-run hedge ability Bitcoin fluctuations.•We find that NFTs exhibit short-run safe haven property against bitcoin fluctuations.•NFTs act as a short to medium term diversifier against Ethereum fluctuations.
In this study, we test the hedge and safe haven properties of NFTs against Bitcoin and Ethereum market fluctuations in the backdrop of COVID-19 and the Russia-Ukraine war. We employ daily returns of Bitcoin, Ethereum, and four NFTS, namely Decentraland, Cryptopunks, Cryptokitties, and SuperRare, from 04 April 2018 to 7 July 2022. For analytical purposes, we estimate dynamic hedge effectiveness and wavelet quantile correlations. The dynamic hedge effectiveness results confirm the NFTs' hedge ability against Bitcoin. The WQC results show that NFTs offer short, medium, and long-run hedge properties and short run safe haven property against Bitcoin. Further, we show that NFTs are at best a short to medium term diversifier against Ethereum fluctuations.
We test the nature of weak form informational efficiency present in the wine market using daily return of LIV-EX 50 index from 1/1/2010 to 12/6/2020. First, we employ a number of statistical tests ...including variance ratio tests, tests for linear and non-linear dependence and Hurst coefficient. The tests are applied on the full dataset and on four non overlapping sub-samples of equal length. The variance ratio tests provide a mixed regarding informational efficiency. Evidence of non-linear dependence in the return series was found. The Hurst coefficient values confirm the presence of long run persistence in the wine market. Based on the mixed evidence, we test the possibility of adaptive nature of the wine market. We employ the newly proposed Adaptive Index (AI) to quantify the degree of information inefficiency in the wine market at any instance. Our results confirm that wine market is adaptive and periodically shifts between states of efficiency and inefficiency. The wine market is found to be relatively free from the Covid-19 induced shock and the safe haven property of wine is thus confirmed. Finally, impact of various macroeconomic and financial events on wine market efficiency is identified by using AI.
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