Investigating long range dependence in temperatures in Siberia

In this paper we examine monthly mean temperatures in 40 selected stations in Siberia for the time period January 1937–December 2020 using long range dependence techniques. In particular, we use a fractionally integrated model that incorporates a linear time trend along with a seasonal structure. Our results show first that long memory is present in all stations with significantly positive values for the differencing parameter, though, at the same time the seasonal component seems to be important in all cases. Performing seasonal unit root tests, the results support nonstationary seasonality and working with the seasonal differenced data, the results differ depending on the structure of the error term: if the errors are uncorrelated, long memory is present; however, allowing autocorrelation, this feature disappears in favor of a short memory pattern

Short-term price overreactions: identification, testing, exploitation

This paper examines short-term price reactions after one-day abnormal price changes and whether they create exploitable profit opportunities in various financial markets. Statistical tests confirm the presence of overreactions and also suggest that there is an “inertia anomaly”, i.e. after an overreaction day prices tend to move in the same direction for some time. A trading robot approach is then used to test two trading strategies aimed at exploiting the detected anomalies to make abnormal profits. The results suggest that a strategy based on counter-movements after overreactions does not generate profits in the FOREX and the commodity markets, but in some cases it can be profitable in the US stock market. By contrast, a strategy exploiting the “inertia anomaly” produces profits in the case of the FOREX and the commodity markets, but not in the case of the US stock market

Investigating long range dependence in temperatures in Siberia

In this paper we examine monthly mean temperatures in 40 selected stations in Siberia for the time period January 1937–December 2020 using long range dependence techniques. In particular, we use a fractionally integrated model that incorporates a linear time trend along with a seasonal structure. Our results show first that long memory is present in all stations with significantly positive values for the differencing parameter, though, at the same time the seasonal component seems to be important in all cases. Performing seasonal unit root tests, the results support nonstationary seasonality and working with the seasonal differenced data, the results differ depending on the structure of the error term: if the errors are uncorrelated, long memory is present; however, allowing autocorrelation, this feature disappears in favor of a short memory pattern

Long-term price overreactions: are markets inefficient?

This paper examines long-term price overreactions in various financial markets (commodities, US stock market and FOREX). First, a number of statistical tests are carried out for overreactions as a statistical phenomenon. Second, a trading robot approach is applied to test the profitability of two alternative strategies, one based on the classical overreaction anomaly, the other on a so-called “inertia anomaly”. Both weekly and monthly data are used. Evidence of anomalies is found predominantly in the case of weekly data. In the majority of cases strategies based on overreaction anomalies are not profitable, and therefore the latter cannot be seen as inconsistent with the EMH

The weekend effect: an exploitable anomaly in the Ukrainian stock market?

This paper provides some new empirical evidence on the weekend effect (one of the best known anomalies in financial markets) in Ukrainian futures prices. The analysis uses various statistical techniques (average analysis, Student's ttest, dummy variables, and fractional integration) to test for the presence of this anomaly, and then a trading simulation approach to establish whether it can be exploited to make extra profits. The statistical evidence points to abnormal positive returns on Fridays, and a trading strategy based on this anomaly is shown to generate annual profits of up to 25%. The implication is that the Ukrainian stock market is inefficient

Long Memory in Earthquake Time Series: The Case Study of the Geysers Geothermal Field.

The present study aims at proving the existence of long memory (or long-range dependence) in the earthquake process through the analysis of time series of induced seismicity. Specifically, we apply alternative statistical techniques borrowed from econometrics to the seismic catalog of The Geysers geothermal field (California), the world’s largest geothermal field. The choice of the study area is essentially guided by the completeness of the seismic catalog at smaller magnitudes (a drawback of conventional catalogs of natural seismicity). Contrary to previous studies, where the long-memory property was examined by using non-parametric approaches (e.g., rescaled range analysis), we assume a fractional integration model for which the degree of memory is defined by a real parameter d, which is related to the best known Hurst exponent. In particular, long-memory behavior is observed for d > 0. We estimate and test the value of d (i.e., the hypothesis of long memory) by applying parametric, semi-parametric, and non-parametric approaches to time series describing the daily number of earthquakes and the logarithm of the (total) seismic moment released per day. Attention is also paid to examining the sensitivity of the results to the uncertainty in the completeness magnitude of the catalog, and to investigating to what extent temporal fluctuations in seismic activity induced by injection operations affect the value of d. Temporal variations in the values of d are analyzed together with those of the b-value of the Gutenberg and Richter law. Our results indicate strong evidence of long memory, with d mostly constrained between 0 and 0.5. We observe that the value of d tends to decrease with increasing the magnitude completeness threshold, and therefore appears to be influenced by the number of information in the chain of intervening related events. Moreover, we find a moderate but significant negative correlation between d and the b-value. A negative, albeit weaker correlation is found between d and the fluid injection, as well as between d and the annual number of earthquakes.post-print4396 K

Atmospheric pollution in the ten most populated US cities. Evidence of persistence

The degree of persistence in daily PM25 and O3 in the ten most populated US cities, namely New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas and San Jose is examined in this work. We employ a methodology based on fractional integration, using the order of integration as a measure of the degree of persistence. Using data for the time period from January 1, 2019 to December 31, 2020, our results indicate that fractional integration and long memory features are both present in all the examined cases, with the integration order of the series being constrained in the (0, 1) interval. Based on this, the estimation of the coefficients for the time trend produces results which are substantially different from those obtained under the I(0) assumption