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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.

This paper analyses monthly price persistence in the EU27 countries over the period 2010–2022 using a fractional integration framework, where the measure of persistence is the fractional differencing parameter d. In addition to full sample estimates, subsample and recursive ones are obtained to examine time variation. On the whole, the results provide clear evidence that both the exogenous shocks considered (namely, the COVID-19 pandemic and the Russia-Ukraine war) have generally increased price persistence in the EU27 (despite their heterogeneity), although the recursive estimates suggest that their impact might have peaked and might now be decreasing. Therefore, any policies adopted to counteract those shocks should be gradually phased out. The exceptions are the Southern European countries, where price persistence appears to have decreased, though in Italy the recursive analysis indicates that it is now rising sharply.

This paper makes a twofold contribution. First, it develops the dynamic factor model of by allowing for fractional integration instead of imposing the classical dichotomy between I(0) stationary and I(1) non-stationary series. This more general setup provides valuable information on the degree of persistence and mean-reverting properties of the series. Second, the proposed framework is used to analyse five annual US Real Economic Activity series (Employees, Energy, Industrial Production, Manufacturing, Personal Income) over the period from 1967 to 2019 in order to shed light on their degree of persistence and cyclical behaviour. The results indicate that economic activity in the US is highly persistent and is also characterised by cycles with a periodicity of 6 years and 8 months.

This paper proposes a long-memory model that includes multiple cycles in addition to the long-run component. Specifically, instead of a single pole or singularity in the spectrum, it allows for multiple poles and, thus, different cycles with different degrees of persistence. It also incorporates non-linear deterministic structures in the form of Chebyshev polynomials in time. Simulations are carried out to analyze the finite sample properties of the proposed test, which is shown to perform well in the case of a relatively large sample with at least 1000 observations. The model is then applied to weekly data on the S&P 500 from 1 January 1970 to 26 October 2023 as an illustration. The estimation results based on the first differenced logged values (i.e., the returns) point to the existence of three cyclical structures in the series, with lengths of approximately one month, one year, and four years, respectively, and to orders of integration in the range (0, 0.20), which implies stationary long memory in all cases.

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.

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration framework. The proposed model is based on a specific test on fractional integration that is more general than the standard methods, which allow for only linear trends.. Its limiting distribution is standard normal, and Monte Carlo simulations show that it performs well in finite samples. Three empirical examples confirm that the suggested specification captures the properties of the data adequately.

This paper analyses the effects of containment measures and monetary and fiscal responses on US financial markets during the Covid-19 pandemic. More specifically, it applies fractional integration methods to analyse their impact on the daily S&P500, the US Treasury Bond Index (USTB), the S&P Green Bond Index (GREEN) and the Dow Jones (DJ) Islamic World Market Index (ISLAM) over the period 1/01/2020–10/03/2021. The results suggest that all four indices are highly persistent and exhibit orders of integration close to 1. A small degree of mean reversion is observed only for the S&P500 under the assumption of white noise errors and USTB with autocorrelated errors; therefore, market efficiency appears to hold in most cases. The mortality rate, surprisingly, seems to have affected stock and bond prices positively with autocorrelated errors. As for the policy responses, both the containment and fiscal measures had a rather limited impact, whilst there were significant announcement effects which lifted markets, especially in the case of monetary announcements. There is also evidence of a significant, positive response to changes in the effective Federal funds rate, which suggests that the financial industry, mainly benefiting from interest rises, plays a dominant role.

This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 13 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 13 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until June 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential only vis-à-vis a single stock index (SP500). whilst in seven other cases, although the estimated value of d is below 1, the value 1 is inside the confidence interval and thus the unit root null hypothesis cannot be rejected. In the remaining cases the estimated values of d are significantly higher than 1. As for the silver differential, the upper bound is 1 only in two cases, whilst in the others mean reversion does not occur. Thus, the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis the New Zealand stock index.

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.

Research on the passion investment market is still limited, especially on the issue of persistence. This paper is the first to investigate it using both R/S analysis and fractional integration in the case of 3 fine wine price indices, 10 diamond price indices, 15 art price indices, and 1 stamp price index at the daily, monthly and quarterly frequency. The results can be summarised as follows: wine prices are found to be highly persistent, whilst stamp prices appear to be only weakly persistent, though they can still be characterised as a long-memory process; as for diamond prices, they can be persistent (Diamonds & Gems), anti-persistent (Diamonds Carat indices) or even random (Polished Prices Diamond Index). The dynamic R/S analysis also shows that persistence is time-varying and tends to fluctuate around the average. These findings can be explained by the different degree of liquidity of the assets examined and have implications for both academics investigating market efficiency and practitioners interested in forecasting prices.

Abstract This paper examines the relationship between the logarithms of carbon dioxide (CO2) emissions and real Gross Domestic Product (GDP) in China by applying fractional integration and cointegration methods. These are more general than the standard methods based on the dichotomy between stationary and non-stationary series, allow for a much wider variety of dynamic processes, and provide information about the persistence and long-memory properties of the series and thus on whether or not the effects of shocks are long-lived. The univariate results indicate that the two series are highly persistent, their orders of integration being around 2, whilst the cointegration tests (using both standard and fractional techniques) imply that there exists a long-run equilibrium relationship between the two variables in first differences, i.e. their growth rates are linked together in the long run. This suggests the need for environmental policies aimed at reducing emissions during periods of economic growth.

This paper analyses the impact of the Covid-19 pandemic on the degree of persistence of European stock markets. Specifically, it uses fractional integration methods to estimate persistence at the daily, weekly and monthly frequencies in the case of ten major European stock market indices; the effects of the pandemic are assessed by comparing the pre-pandemic estimates (over the period 2005–2019) to those from a sample extended until July 2021 which includes the pandemic period. The approach used is more general than the standard one based on the stationarity versus non-stationarity dichotomy and allows for a wider range of dynamic processes. Three different model specifications are considered, and these are estimated under two alternative assumptions for the disturbances (white noise and autocorrelation). The findings indicate that there has not been any significant impact of the Covid-19 pandemic on the degree of persistence of the European stock market indices, though their volatility persistence has decreased.

This paper analyses the explanatory power of the frequency of abnormal returns in the FOREX over the period 1994–2019. The following hypotheses are tested: frequency of abnormal returns is asignificant driver of price movements (H1); it does not exhibit seasonal patterns (H2); it is stable over time (H3). For our purposes avariety of statistical methods are applied including ADF, PP and KPSS tests, Granger causality tests, correlation analysis, regression analysis, Probit and Logit regression models. No evidence is found of either seasonal patterns or instability. However, there appears to be astrong positive (negative) relationship between returns in the FOREX and the frequency of positive (negative) abnormal returns. On the whole, the results suggest that the latter is an important driver of price dynamics in the FOREX, is informative about crises and can be the basis of profitable trading strategies, which is inconsistent with market efficiency.

This paper investigates unemployment persistence in the 27 EU member states by applying fractional integration methods to quarterly data (both seasonally adjusted and unadjusted) from 2000q1 to 2020q4. The obtained evidence points to high levels of persistence in all cases. With seasonally adjusted data, a small degree of mean reversion is found in the case of Belgium, Luxembourg and Malta, but this evidence disappears under the assumption of weakly correlated disturbances. More cases of mean reversion are found instead when analysing the unadjusted series. In particular, countries such as Belgium, France, Croatia, Italy, Luxembourg and Malta display orders of integration significantly lower than 1. In addition, significant negative time trends are found in the case of Bulgaria, Croatia, Malta and Romania, and a positive one for Luxembourg. Finally, the Covid-19 pandemic had mixed effects, with (seasonal) persistence increasing in some countries whilst decreasing in others and not changing in a minority of cases. On the whole, our results support the hysteresis hypothesis for the European economies.

This paper examines reactions in the Ukrainian stock market to force majeure events, which are divided into four groups: economic force majeure, social force majeure, terrorist acts, natural and technological disasters. More specifically, using daily data for the main Ukrainian stock market index (namely PFTS) over the period from January 1, to December 31, 2018 this study investigates whether or not force majeure events create (temporary) inefficiencies and there exist profitable trading strategies based on exploiting them. For this purpose, cumulative abnormal returns and trading simulation approaches are used in addition to Student’s t-tests. The results suggest that the Ukrainian stock market absorbs new information rather fast. Negative returns in most cases are observed only on the day of the event. The only exception is technological disasters, the market needing up to ten days to react fully in this case. Despite the presence of a detectable pattern in price behavior after force majeure events (namely, a price decrease on the day of the event) no profitable trading strategies based on it are found as their outcomes do not differ from those generated by random trading.

This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).

This paper explores the frequency of price overreactions in the US stock market by focusing on the Dow Jones Industrial Index over the period 1990–2017. It uses two different methods (static and dynamic) to detect overreactions and then carries out various statistical tests (both parametric and non-parametric) including correlation analysis, augmented Dickey–Fuller tests (ADF), Phillips-Perron (PP) tests, Granger causality tests, and regression analysis with dummy variables. The following hypotheses are tested: whether or not the frequency of overreactions varies over time (H1), is informative about crises (H2) and/or price movements (H3), and exhibits seasonality (H4). The null cannot be rejected except for H4, i.e., no seasonality is found. On the whole, it appears that the frequency of overreactions can provide useful information about market developments. A sharp increase in the number of overreactions occurs in crisis periods. The frequency of overreactions is linked to the VIX index and therefore could be used as an alternative measure of market sentiment and market fear, and it also affects stock returns. Further, our findings provide evidence supporting market inefficiency since price predictability can allow investors to design profitable trading strategies; in addition, the fact that the frequency of overreactions varies over time is consistent with the Adaptive Expectations Hypothesis.

This paper is a comprehensive investigation of calendar anomalies in the Ukrainian stock market. It employs various statistical techniques (average analysis, Student’s t-test, ANOVA, the Kruskal-Wallis test, and regression analysis with dummy variables) and a trading simulation approach to test for the presence of the following anomalies: day-of-the-week effect; turn-of-the-month effect; turn-of-the-year effect; month-of-the-year effect; January effect; holiday effect; Halloween effect. The results suggest that in general calendar anomalies are not present in the Ukrainian stock market, but there are a few exceptions, i.e. the turn-of-the-year and Halloween effect for the PFTS index, and the month-of-the-year effect for UX futures. However, the trading simulation analysis shows that only trading strategies based on the turn-of-the-year effect for the PFTS index and the month-of-the-year effect for the UX futures can generate exploitable profit opportunities that can be interpreted as evidence against market efficiency.

This research note investigates whether or not calendar anomalies (such as the January, day-of-the-week and turn-of-the-month effects) characterize the Russian stock market, which could be interpreted as evidence against market efficiency. Specifically, OLS, GARCH, EGARCH and TGARCH models are estimated using daily data for the MICEX market index over the period Sept. 1997–Apr. 2016. The empirical results show the importance of taking into account transactions costs (proxied by the bid-ask spreads): once these are incorporated into the analysis, calendar anomalies disappear, and therefore, there is no evidence of exploitable profit opportunities based on them that would be inconsistent with market efficiency.

This paper analyses the relationship between monetary policy and the stock market with the aim of gaining new insights into the transmission mechanism of monetary policy. The empirical findings shed light on the importance of stock prices for money demand and therefore provide useful information to monetary authorities to decide on policy actions. A technique developed by Wickens and Motto (2001) for identifying shocks by estimating a VECM for the endogenous variables is employed. The reported evidence suggests that stock markets play a significant role in the money demand function.

This paper applies long-memory techniques (both parametric and semi-parametric) to examine whether Brexit has led to any significant changes in the degree of persistence of the FTSE (Financial Times Stock Index) 100 Implied Volatility Index (IVI) and of the British pound’s implied volatilities (IVs) vis-à-vis the main currencies traded in the FOREX (foreign exchange market), namely the euro, the US dollar and the Japanese yen. We split the sample to compare the stochastic properties of the series under investigation before and after the Brexit referendum, and find an increase in the degree of persistence in all cases except for the British pound-yen IV, whose persistence has declined after Brexit. These findings highlight the importance of completing swiftly the negotiations with the European Union (EU) to achieve an appropriate Brexit deal.

This study examines the short- and long-run linkages between employment growth, inflation and output growth applying panel cointegration and causality tests to data for 119 countries over the period 1970–2010. We find evidence of positive Granger causality running from output growth to employment growth in the short run. Employment growth Granger causes output growth with a negative sign in the long run. Inflation Granger causes employment and output growth positively in the short run and negatively in the long run.

This paper investigates how output growth, employment growth and inflation influence each other in the short/long run. It builds on Phillips (1962) and Blanchard Fischer (1989) assessment that empirical links between output, employment and prices are central issue in modern macroeconomics. This paper brings a global perspective on short and long term links between employment growth, inflation and output growth using panel cointegration framework with non-stationary heterogeneous panel (119 countries over 1970–2010). The empirical results (on global and national level) strongly support the existence of a long-run equilibrium relationship between output growth, employment growth and inflation. A central finding is that possible trade-off effects between growth, employment and prices varies significantly among economies. Therefore, universal answers to questions Is inflation good for growth or Is there a trade-off between employment and growth are not straightforward for general macroeconomic theory. Each country must design own economic policy (targeting) taking into account the quantitative relationships between growth, employment and prices. This has important policy implications also for price setting policies, cost management, market strategy and risk management through productivity-demand disturbances effects on the business environment.

El artículo argumenta que el hecho de que la evidencia empírica sobre persistencia sea confusa no es muy sorprendente, dado que la teoría económica está limitada al tener que especificar el modelo estadístico. Este punto se ilustra dos maneras: primero, resaltamos el hecho de que el concepto de persistencia depende del modelo, es una función del modelo adoptado a partir de la teoría económica. Segundo, se analice tema relacionado con la definición de un conjunto de parámetros de interés. En particular, consideramos un caso bivariado simple. Dada la exogeneidad débil de los regresores los parámetros del único vector de cointegración pueden ser estimados equivalente mente dentro de un sistema completo de ecuaciones o con un modelo uniecuacional. Por el contrario, si la persistencia está siendo medida, la exogeneidad débil de los regresores no se mantiene, ya que los parámetros de interés no puede ser escritos sólo como una función de aquellos del modelo condicional y el concepto de exogeneidad (ya no débil) del modelo se convierte en más relevante. Una vez más, la teoría económica puede ser vista como esencial en el ejercicio de especificación del modelo.

En este artículo se argumenta que la persistencia no es una característica invariable de una serie de tiempo, sino que depende del contexto en el cual la serie se utiliza: dado que los parámetros de cualquier modelo dinámico se definen en relación a un conjunto particular de información, cualquier cambio en el conjunto de variables condicionales puede afectar las estimaciones resultantes. Definimos persistencia de una variable como la tasa a la cual su función de autocorrelación converge a cero, y demostramos que la inferencia sobre la persistencia de una variable no varía en función de la adición de otras variables condicionales siempre y cuando éstas variables no sean Granger-causales sobre la variable de interés. Más aún, establecemos que la persistencia medida es una función del modelo elegido y que esto es más fundamental para sistemas inestables. Nuestros hallazgos sugieren que, a menos que se impongan más restricciones derivadas de la teoría económica, temas como la efectividad de las políticas de estabilización no pueden ser resueltos empíricamente, y que por ende, el debate entre los teóricos keynesianos y RBC no puede cerrarse.