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AbstractDifferences in the behaviour of asset prices depending on data frequency have not been thoroughly investigated in the literature despite their possible importance. In particular, high-frequency data might contain more information about financial assets because they are updated more rapidly in response to news. This paper explores persistence in high-frequency data (and also daily and monthly ones) in the case of the EuroStoxx 50 futures prices over the period from 2002 to 2018 (720 million trade records) using R/S analysis and the Hurst exponent as a measure of persistence. The results show that persistence is sensitive to the data frequency. More specifically, monthly data are highly persistent, daily ones follow a random walk, and intraday ones are anti-persistent. In addition, persistence varies over time. These findings imply that the Efficient Market Hypothesis (EMH) only holds in the case of daily data, whilst it is possible to make abnormal profits using trading strategies based on reversal strategies at the intraday frequency.

AbstractThis paper examines price effects related to witching days in the US stock market using both weekly and daily data for three major indices, namely the Dow Jones, S&P500 and Nasdaq, over the period 2000–2021. First it analyses whether or not anomalies in price behaviour arise from witching by using various parametric (Student’s t-test, and ANOVA) and non-parametric (Mann-Whitney) tests as well as an event study method and regressions with dummies; then it investigates whether or not any detected anomalies give rise to profit opportunities by applying a trading simulation approach. The results suggest the presence of the anomaly in daily returns on witching days which can be exploited by means of suitably designed trading strategies to earn abnormal profits, especially in the case of the Nasdaq index. Such evidence is inconsistent with the Efficient Market Hypothesis (EMH).

AbstractThis paper examines tourism persistence in a group of Southeastern European (SEE) countries (Albania, Bosnia, Bulgaria, Croatia, Montenegro, North Macedonia, Serbia and Slovenia) by applying fractional integration methods to monthly data on foreign tourist arrivals and overnight stays. The results indicate that the COVID-19 pandemic has increased the degree of persistence of these series as measured by the fractional differencing parameter; specifically, it has removed the mean reversion property in some countries. In addition, it has reduced the importance of the seasonal component.

AbstractThis paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (Bitcoin—BITC, Stella—STEL, Litecoin—LITE, Ethereum—ETHE, XRP (Ripple), Dash, Monero—MONE, NEM, Tether—TETH) and two technology-related stock market indices (the KBW NASDAQ Technology Index—KFTX, and the NASDAQ Artificial Intelligence index—AI) over the period 1 January 2020–5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down; by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.