Bunker fuel, commodity prices and shipping market indices following the COVID-19 pandemic. A time-frequency analysis.

This paper deals with the analysis of the evolution of international trade after COVID-19, examining commodity prices, the shipping industry, and the influence of the cost of bunker fuel. To this end, we use techniques based on fractional integration, fractional cointegration VAR (FCVAR) and wavelet analysis. Monthly data relating to heavy fuel oil prices and the shipping market from October 2011 to September 2021 are used. Using fractional integration in the post-break period, a lack of mean reversion is observed in all cases, which means that, for the commodity prices and shipping market indices, a change in trend will be permanent after COVID-19 unless strong measures are carried out by the authorities. Using wavelet analysis, we conclude that the demand shock represented in the indices mentioned above has led the price of fuel oil since the beginning of the pandemic, and bunker fuel is not relevant in determining the cost of maritime transport.post-print2426 K

U.S. historical initial jobless claims. Is it different with the coronavirus crisis? A fractional integration analysis.

This research paper makes an empirical analysis based on long memory to understand the historical behavior of initial unemployment claims (ICSA) in the United States (U.S.) during all the recession periods and epidemic diseases such as Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and COVID-19 since 1967 applying statistical methods based on long range dependence and fractional differentiation. Using unit root/stationarity tests (ADF, PP and KPSS) we discover that the original time series is stationary I(0) and the subsamples are non-stationary I(1). Finally, to analyze the original time series as well as the several periods corresponding to the recessions that occurred in U.S. and the three epidemic diseases, we use AIC and BIC criterion to fit the best ARFIMA model. We conclude that the results display long memory with a degree of integration strictly below 1 (d < 1) for the COVID-19 episode and for the rest of the subsamples, except for the original time series and the 2nd subsample. Thus we can conclude that the impacts will be transient and with long lasting effects of shocks and expecting to disappear on their own in long term. Finally, we use a methodology proposed by Bai and Perron to estimate structural breaks not being necessary to know the time of the breaks in advance. The results are similar to those obtained previously.pre-print363 K

Terrorism and the behavior of oil production and prices in OPEC.

This paper contributes to the discussion of the role that terrorism plays in oil production and oil prices literature by studying the dynamics of terrorist attacks and oil production in OPEC. To this purpose, we use techniques based on fractional integration, fractional cointegration VAR (FCVAR) and wavelet analysis. Monthly data related to OPEC oil production, OPEC basket crude oil prices and terrorist attacks from January 1970 to December 2018 are used. The results, using fractional integration and cointegration techniques, indicate that the time series analyzed are highly persistent and there are no long-term deviations. Finally, using wavelet analysis, we conclude that the impact of the terrorist attacks in oil production and oil prices are non-significant and has a short-term component, recovering the original trend values between 1 and 10 months after the terrorist shock.pre-print555 K

A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis.

The main aim of this paper is to build a Real Time Leading Economic Indicator (RT-LEI) that improves Composite Leading Indicators (CLI)’s performance to anticipate GDP trends and turning points for the Spanish economy. The indicator has been constructed using a Factor Analysis and is composed of 21 variables concerning motor vehicle activity, financial activity, real estate activity, economic sentiment, and industrial sector. The data sources used are Google Trends and Thomson Reuters Eikon-Datastream. This work contributes to the literature, studying the dynamics of GDP, CLI and RT-LEI using Fractional Cointegration VAR (FCVAR model) and Continuous Wavelet Transform (CWT) for its resolution. The results show that the model does not present mean reversion and it is expected the RT-LEI reveals a bear trend in the next two years, alike IMF and Consensus FUNCAS′ forecasts. The reasons are mostly associated with escalating global protectionism, uncertainty related to Catalonia and faster monetary policy normalization.pre-print990 K

Forecasting Spanish economic activity in times of COVID-19 by means of the RT-LEI and machine learning techniques.

The main aim of this paper is to analyse and estimate the behaviour of the Spanish economic activity in the next 12 months, by means of a Real-Time Leading Economic Indicator (RT-LEI), based on Google Trends, and the real GDP. We apply methodologies based on fractional integration and cointegration to measure the degree of persistence and to examine the long-term relationship. Finally, we carry out a forecast using a Machine Learning model based on an Artificial Neural Network. Our results indicate that the Spanish economy will experience a contraction in 1Q-21 and will require strong measures to reverse the situation and recover the original trend.pre-print334 K

Global CO2 emissions and global temperatures: Are they related

This paper deals with the analysis of the relationship between CO2 emissions and temperatures. For this purpose, global CO2 emissions and four measures of global temperatures (land, land and ocean, northern and southern temperatures) are used. We used techniques based on fractional integration and cointegration. The results indicate first that the orders of integration differ in the two variables. Thus, while emissions are I(1) or I(d) with d higher than 1, temperatures display orders of integration strictly smaller than 1 and thus invalidating the hypothesis of cointegration between the two variables. Due to this, another approach is conducted where we suppose that the emissions are weakly exogenous in relation to the temperatures. The results using this approach show a significantly positive relationship between the two variables with a long memory pattern.pre-print303 K

Automobile components: lithium and cobalt. Evidence of persistence.

This paper deals with the analysis of persistence in the prices of two technologically important metals, namely, lithium and cobalt. Along with them, we also examine four additional series corresponding to World, European, US and Japanese automobiles and component indices. For this purpose, we use long memory techniques based on fractional integration and cointegration. The results indicate that all the series are highly persistent, though we do not find any evidence supporting long run equilibrium relationships between the variables examined.pre-print1100 K

Water prices: persistence, mean reversion and trends.

Time series referring to water prices at different regions all over the world are examined in this paper by using fractionally integrated methods. We look at series corresponding to the following regions: Asia Pacific and Russia, Europe, United States and Latin America as well as global data. The results indicate large degrees of persistence, with the values of the differencing parameter being close to one in all cases and higher under the assumption of uncorrelated errors. If autocorrelation is permitted, a small degree of mean reversion is found in all except the Latin American series. The possibility of structural breaks is also investigated and the results indicate the presence of multiple breaks in the data: three in the case of Latin America and global data; four in Europe and USA and five for the Asian Pacific and Russia. Nevertheless, we do not observe a significant change in the degree of persistence across subsamples and once more mean reversion is found if autocorrelation is permitted.post-print353 K

Lithium industry in the behaviour of the mergers and acquisitions in the U.S. oil and gas industry.

Is lithium affecting the U.S. oil and gas industry strategies? Lithium has an increasingly strategic role as clean technologies emerge, affecting the strategies of oil and gas companies in response to energy trends. This paper contributes to this literature, studying the dynamics of lithium industry and mergers and acquisitions in the U.S. oil and gas industry in time-frequency domain. We use methodologies based on Continuous Wavelet Transform (CWT) and Vector AutoRegressive Models (VAR), and the results indicate that both time series are correlated in the long term, where M&A U.S. oil and gas industry dependence on lithium industry has increased, starting in the early 2014 until the end of the sample. Evidence of causality is not found between both time seriespre-print716 K

Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis.

This paper analyzes the dynamics of U.S. lithium mining companies, the lithium industry and West Texas Intermediate (WTI) crude oil prices using a Fractional Cointegration Vector AutoRegressive model (FCVAR model) and a Continuous Wavelet Transform (CWT) for its resolution. The results indicate evidence of a negative relationship between FMC Corp with Albermale and SQM stock prices. These results are similar if we analyze the risk based on the beta term structure of each company. Analyzing the fractional differencing parameter for the stock prices and their logs, we observe that they are very persistent, and there are no long-term deviations in the stock prices. The same happens when analyzing the beta term structure. Based on Continuous Wavelet Transform (CWT) methods, our results show that lithium mining companies and the lithium industry are weakly correlated with WTI crude oil prices at higher frequencies (short-run) and persist through the sample period. At lower frequencies (long-term) the time series reached a high level of dependence between late 2012 to mid 2016, concluding that the lithium mining companies and the lithium industry reflect and foreshadow the responsiveness of the WTI crude oil prices during the period mentioned above.pre-print399 K