Price leadership and volatility linkages between oil and renewable energy firms during the covid-19 pandemic

The COVID-19 pandemic is shaving a strong influence in all areas of society, like wealth, economy, travel, lifestyle habits, and, amongst many others, financial and energy markets. The influence in standard energies, like crude oil, and renewable energies markets has been twofold: from one side, the predictability of volatility has strongly decreased; secondly, the linkages of the price time series have been modified. In this paper, by using DCC-GARCH and Price Leadership Share method-ology, we can investigate the changes in the influences between standard energies and renewable energies markets by analyzing one-minute time series of West Texas Intermediate crude oil futures contract (WTI), the Brent crude oil futures contract (BRENT), the STOXX Europe 600 oil & gas index (SXEV), and the European renewable energy index (ERIX). Our results confirm volatility spillover between the time series. However, when assessing the accuracy of the predictability of the DCC-GARCH model, the results show that the model fails its prediction in the period of higher instability. Besides, we found that price leadership has been strongly influenced by the virus spreading stages. These results have been obtained by dividing the period between September 2019 and January 2021 into 6 subperiods according to the pandemic stages

A multivariate high-order markov model for the income estimation of a wind farm

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm

Incremental events in the construction of sambaquis, southeastern Santa Catarina.

Moundbuilding is a cross-cultural phenomenon of nearly world-wide scope. In this article some of the moundbuilding processes related to the sambaquis (shellmounds) from the coast of Santa Catarina State, Brasil, are examined, focusing on field research at the sambaqui Jaboticabeira II. By means of analysing the mounding up processes at that site, attention is drawn to its social and demographic corollaries, which speak of considerable social elaboration and territorial permanence in time.A construção de cômoros artificiais (mounds) é um fenômeno de amplo alcance em termos globais. Neste artigo são examinados alguns dos processos responsáveis pela construção destas estruturas no litoral sul de Santa Catarina, tomando-se como referência os trabalhos realizados no sambaqui Jaboticabeira II, município de Jaguaruna. Além da análise dos processos envolvidos na construção daquele sambaqui, procura-se chamar a atenção para os aspectos sociais e demográficos destes mesmos processos, evidenciando um sistema regional de considerável complexidade social e duração cronológica

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Intelligent design: stablecoins (in)stability and collateral during market turbulence

How does stablecoin design affect market behavior during turbulent periods? Stablecoins attempt to maintain a "stable" peg to the US dollar, but do so with widely varying structural designs. The spectacular collapse of the TerraUSD (UST) stablecoin and the linked Terra (LUNA) token in May 2022 precipitated a series of reactions across major stablecoins, with some experiencing a fall in value and others gaining value. Using a Baba, Engle, Kraft and Kroner (1990) (BEKK) model, we examine the reaction to this exogenous shock and find significant contagion effects from the UST collapse, likely partially due to herding behavior among traders. We test the varying reactions among stablecoins and find that stablecoin design differences affect the direction, magnitude, and duration of the response to shocks. We discuss the implications for stablecoin developers, exchanges, traders, and regulators

Entropy Measures for Credit Sovereign Ratings: The European Union Case

In this chapter, we analyze the main entropy measures applied to the sovereign credit rating data of the European Union. The dataset used for the investigation consists of ratings from four major rating agencies: Moody’s, S&P, Fitch, and DBRS, and it covers a 15-years period approximately which includes the great financial crisis of 2008-2011. This analysis allows us to compare the durations of the individual rating states among the agencies and to understand if those agencies really respond to the individual rating states with different durations. We perform the entire investigation considering the right censoring statistical problem

CO2 is the contrast media to choose in patients with initial Chronic Kidney Disease undergoing endovascular aneurysm repair to prevent further renal function deterioration

Objective: Even low quantities of iodine contrast media (ICM) could be responsible for exacerbation of a chronic kidney disease (CKD). Aim of this study was to determine whether it is more reasonable to perform endovascular aneurysm repair (EVAR) procedures in patients with initial CKD using CO2 rather than ICM to prevent further kidney deterioration. Methods: A retrospective analysis was performed at our institution to identify patients with preoperative CKD at initial stage (class G3a-G3b according to KDOQI-KDIGO classification) who underwent either CO2-EVAR or ICM-EVAR. Primary endpoint was renal function evaluation; secondary endpoints were technical success, perioperative complications, hospital stay, and reinterventions and overall mortality at follow-up. Results: Both CO2-EVAR and ICM-EVAR groups were composed of 21 patients. There were no differences in demographics, anatomy, and comorbidities, apart from worse ASA score in CO2-EVAR group (100% vs 57.1%, p =.001). Preoperative serum creatinine and glomerular filtration rates (GFR) were comparable (1.73 vs 1.6 mg/dl, p =.082 and 39.71 vs 43.04 mL/min/1.73 m2, p =.935). At follow-up (16.7 ± 18.1 months), CO2-EVAR was not associated with significant changes in creatinine and GFR, whereas ICM-EVAR determined a significant increase in creatinine (1.6 mg/dl vs 1.91 mg/dl, p =.04) and decrease in GFR values (43 vs 37.9 mL/min/1.73 m2, p =.04), determining the need for dialysis in one patient. Conclusions: ICM seems to be a determining factor in worsening renal function; therefore, an effort should be made to standardize the use of CO2 as the contrast medium of choice in patients with initial renal insufficiency undergoing EVAR