Historical Average Research Articles

Understanding vegetative ecophysiological responses of herbaceous species from a Brazilian dry forest by simulating 60‐year precipitation extremes

AbstractIn the Caatinga, a Brazilian dry forest, low water availability is a limiting factor that affects the growth and survival of herbaceous plants. Thus, we hypothesize that simulated extreme changes in precipitation modify aboveground and belowground vegetative responses differently between annual and perennial herbaceous species. Seedlings of the species were obtained through seed germination. During 1 year, they were submitted to three rainfall simulation (RS) treatments, defined from a 60‐year sequence: Twet‐RS of rainy years; Tcontrol‐RS of years close to the historical average; Tdry‐RS of dry years; and with 30 repetitions per treatment. The daily water administration corresponded to the daily average of rainfall in the past. In the simulation of dry years, the annual species reduced the growth drastically, while the perennials showed a moderate reduction. All species reduced biomass and production and expansion of their leaves in dry years but invested in water storage and leaf longevity. Below ground, annuals explored surface water resources and perennials invested in root growth, accessing underground layers. If reductions in rainfall are confirmed, the allocation of herbaceous biomass may follow an “optimal partitioning” strategy, along with maintaining higher water content in the organs, which is an efficient survival strategy.

Forecasting urban unemployment rate in China using ARIMA model

Abstract. The urban unemployment rate is a significant economic indicator that has long drawn researchers interest. Monitoring and predicting changes in the unemployment rate can help in understanding economic trends and implementing appropriate measures. This article aims to forecast urban unemployment rates in China. By collecting previous surveyed urban unemployment rates in China, this article will generate and compare various ARIMA models in order to identify the one with the best forecasting accuracy. The forecast results of the selected model state that the unemployment rate will remain almost unchanged, around 5%, in the second half of 2024 and throughout 2025. Fluctuations are expected to be between 0.01% and 0.03%. The number is much lower than the peak during the pandemic, but it is still above the historical average. This article argues that Chinas economy is gradually stabilizing, and the post-pandemic measures have been effective but are still insufficient. The government still needs to implement additional actions.

Out-of-sample equity premium predictability: An EMD-denoising based model

The poor out-of-sample forecasting performance of the stock returns of various predictors has been widely confirmed in the literature, which casts doubt on the reliability of stock-return predictability. However, the reliability of return predictability is closely related to the noise contained in the data. In this study, we design a new method to address the noise in the framework of empirical mode decomposition. The EMD method provides an efficient return decomposition, and based on which we selectively remove high-frequency components that are more likely to be contaminated by outliers. Our new model delivers statistically and economically significant out-of-sample gains relative to the historical average. The predictive ability mainly originates from the business-cycle risk and survives a series of robustness tests.

Calm Before the Storm?

Atlantic hurricane season poses an annual threat to the billions of dollars of oil and gas hardware pumping hydrocarbons from the depths of the US Gulf of Mexico (GOM). The region’s offshore production amounts to about 1.8 million B/D—roughly 14% of the total crude output in the US, according to the US Energy Information Administration, so any disruption is felt by oil markets across the globe. Prior to the 2024 hurricane season, forecasters pegged this year as primed to be particularly intense. Colorado State University (CSU), known for its storm prognostication, estimated that the basin would experience 23 named storms, with a dozen of those becoming hurricanes (winds of 74 mph or higher), and six of those strengthening to major hurricanes (winds of 111 mph or higher). Similarly, AccuWeather meteorologists expect between 20 and 25 named storms with a possibility of 30 or more. As the season enters its busy period, the basin has only experienced five named storms to date; however, many forecasters are sticking to their initial forecasts and continue to warn of an intense September/October period. “The months of September and October should be very active across the Atlantic,” said AccuWeather lead hurricane expert Alex DaSilva. “We continue to remain very concerned about rapid intensification. Sea-surface temperatures and ocean heat content are near record levels across most of the Atlantic basin. This can act like jet fuel for tropical systems. We also continue to believe that La Niña will emerge sometime in November. When water temperatures near the equator of the eastern Pacific Ocean are lower than the long-term historical averages for an extended period, La Niña is declared. Although this is a phenomenon in the Pacific, it has ramifications for weather patterns over the Atlantic Ocean. Specifically, La Niña can reduce the wind shear across the basin. When wind shear is lower, it makes it easier for tropical systems to take shape and strengthen.” The GOM has experienced one disruption in 2024 courtesy of Hurricane Beryl. Once a Category 5 storm (winds 155 mph and higher)—and the earliest-forming Category 5 storm on record—Beryl weakened as it crossed the southern Yucatán. The storm regained Category 1 strength as it crossed the far western offshore oil patch enroute to landfall near Matagorda Bay, Texas. Shell shut in production at its Perdido spar because of the storm as well as evacuated personnel from it and its Whale facility. Chevron moved all non-essential personnel off its Anchor floating production unit, which was undergoing commissioning at the time. The last major storm to cut a path straight through the heart of the US offshore oil patch was 2021’s Hurricane Ida. Ida tore through the region as a Category 4 storm before slamming into the Louisiana coast near Port Fourchon packing 150 mph winds on 29 August. The storm prompted the shut in of almost 100% of the Gulf’s oil and gas production and caused extensive damage to major hubs in the region, including the Shell-operated West Delta Block 143 complex. The facilities, which serve as the transfer station for production from Shell’s robust Mars corridor, were off line for several months.

Rainfall analysis in Xingu River Basin based on the 1981-2020 timeframe

It is essential understanding the incidence of hydrometeorological variables in each river basin to help planning actions focused on guaranteeing multiple water uses, environmental preservation and prevention measures taken against extreme events. The aim of the current study is to investigate rainfall trends in Xingu River Hydrographic Basin (XRHB) from 1981 to 2020. Data propensity analysis was carried out based on the Mann-Kendall methods, on five-year moving average graph, as well as on variations in Climatological Normals (CN) recorded from 1981 to 2010 and from 1991 to 2020. Moreover, the Log-Normal theoretical distribution was adjusted to show mean rainfall predictions within 10-to-100-year return time (Rt). It was done by applying the Kolmogorov-Smirnov (KS), Chi-Square (?2), Filliben (Fi) and Anderson-Darling (AD) tests. Results have evidenced historical series with no trend and minimal variation (-0.5%) between the two CN intervals. Estimates within a 10-to-100-year RT scenario have evidenced rainfall rates higher than the historical average (HA) ranging from 10.0% to 20.0%. Thus, XRHB is a promising scenario for water security planning purposes, since it meets the fundamentals of Brazilian environmental and water resources laws, as well as target 6.5 of Sustainable Development Goal 6 (SDG 6 - UN), which deals with implementing integrated water-resources management at global level.

The market risk premium in Australia: Forward‐looking evidence from the options market

AbstractThis paper analyses forward‐looking estimates of the expected market return in Australian. By utilising option prices, we compute a lower bound for the capital gain and dividend components of the expected return. Over a 17‐year period, the average 1‐month expected return lower bound is found to be 8.6% per annum, compared with an average realised return of 10.9% per annum. Our option‐based estimates demonstrate significant predictive power beyond historical averages and enable direct measurement of the expected return term structure. This approach complements traditional measures of expected returns and offers valuable insights for practitioners, academics, and policymakers in Australia.

Portfolio Optimization with Sector Return Prediction Models

We analyze return predictability for U.S. sectors based on fundamental, macroeconomic, and technical indicators and analyze whether return predictions improve tactical asset allocation decisions. We study the out-of-sample predictive power of individual variables for forecasting sector returns and analyze multivariate predictive regression models, including OLS, regularized regressions, principal component regressions, the three-pass regression filter, and forecast combinations. Using an out-of-sample Black–Litterman portfolio optimization framework and employing predicted returns as investors’ ‘views’, we evaluate the benefits of sector return forecasts for investors. We find that portfolio optimization with sector return prediction models significantly outperforms portfolios using historical averages as well as passive benchmark portfolios.

European vineyards and their cultural landscapes exposed to record drought and heat

CONTEXTEuropean vineyards, producing over 50% of the world's wine and hosting significant cultural landscapes, face threats from climate change and related severe weather events. The summer of 2022, especially July, posed significant challenges due to extreme drought and high temperatures. However, this period also provided an opportunity to study how such events might involve viticulture in Europe and to explore mitigation solutions. OBJECTIVEThe objectives are (1) to characterize the severity of the extreme event of July 2022 in European wine regions regarding primary climatic parameters, (2) map vineyards at risk due to agricultural drought and high land surface temperature, and (3) discuss the role of various water-related interventions for mitigating similar events. METHODSAfter identifying the locations of European vineyards using the Corine Land Cover 2018 (CLC2018), open-access satellite data were employed to: (1) assess anomalies in Maximum Air Temperature (NATm), Land Surface Temperature (LST), Precipitation (P), and Soil Moisture (SM) in July 2022 compared to long-term averages; (2) identify regions at higher risk that experienced extreme agricultural drought (Vegetation Health Index, VHI = Extreme) and LST > 35 °C. RESULTS AND CONCLUSIONSIn July 2022, European vineyards experienced an average increase of 11% in NATm, a 9% rise in LST, and a reduction of 47% in P and 30% in SM compared to historical averages. 18% of European vineyards were at risk of drought and excessive heat, particularly in Portugal (31%), France (27%), and Italy (21%), including 10 viticultural cultural landscapes. Findings highlight the urgent need for long-term sustainable water management practices over emergency interventions. This research supports informed decision-making, emphasizing that climate resilience is necessary for preserving the cultural heritage of European wine-growing areas. SIGNIFICANCEThis research provides an overview of the dynamics of extreme events on viticulture at a continental scale, promoting climate-aware viticultural systems and offering insight scalable to global viticulture under changing climatic conditions.

Achieving Sustainable Goals Using an Effective Budget-Allocation Multicriteria Mives Model: Case Study of a Spanish Water Utility Company

Within the global sustainability agenda, companies must align their operations with specific Sustainable Goals (SGs) and develop corresponding budgeting strategies. This paper presents a robust multicriteria decision-making model tailored for a Spanish utility company specializing in water services. The model ensures that financial allocations reflect the company's vision and its interpretation of sustainable development within its service areas. This approach aims to promote objectivity, traceability, and sustainability in decision-making, as a critical component in achieving SGs. The article emphasizes the importance of aligning water companies' budget allocations with SGs, highlighting key indicators for consideration. It provides a practical illustration of how the company's financial allocations were synchronized with SGs using a multicriteria decision-making model. A case study is included, involving a budget allocation of 40 million euros compared across four scenarios with the historical average of budgets from the Past Three Investment Plans (P3IP). The results reveal significant variations in budget allocation across different infrastructure categories, demonstrating the system's robustness, such as a 70% variation in production infrastructures, 27% in transportation, 25% in distribution, and 40% in the remaining areas, depending on the scenario.

Neoadjuvant aromatase inhibitor therapy for ER+ breast cancer: The NAOMI trial.

e12646 Background: Neoadjuvant Endocrine Therapy (NET) is an effective and well tolerated treatment for locally advanced ER+ breast cancer (BC) yet it is underutilized. It can induce comparable clinical response rates to chemotherapy but not pathologic complete response. There is a knowledge gap regarding its effect on residual cancer cells and fatty acid oxidation which is essential for survival of dormant ER+BC cells after estrogen withdrawal in vitro. The NAOMI trial is a study testing the effects of NET with the AI letrozole in early ER+ BC. It aimed to assess the effect of letrozole on residual cancer cells as well as markers of mitochondrial and fatty acid metabolism. Herein we present the results of the molecular and clinical analyses. Methods: NAOMI is a phase II single-arm open-label study with use of letrozole for 4 to 12 weeks prior to surgical resection. Eligible patients were women over 18 years of age, post-menopausal, with stage I-III ER+, HER2- BC. Exclusion criteria included prior ET use or other neoadjuvant options. The primary objective was to assess whether residual cancer cells exhibit upregulation of the fatty acid transporter CPT1A compared to baseline. Secondary objectives included the proportion of subjects who had clinical response and whether residual cancer cells exhibit markers of altered mitochondrial and fatty acid metabolism compared to baseline tumors. Protocol was amended to include adherence rates to adjuvant AI. Results: A total of 77 women have consented to study participation since December 2022 including 57 evaluable patients. Molecular analyses were performed on diagnostic and post-letrozole tumor specimens from the first 33 patients. Immunohistochemistry was done using antibodies against Ki67, CPT1A, FASN, perilipin, CD36, and MT-CO2. CPT1A histoscore between baseline and post letrozole specimens were similar (paired t-test p=0.2636). Markers of altered mitochondrial and fatty acid metabolism including MT-CO2, CD36, FASN, Perilipin were unchanged when comparing pre and post-AI tissues. Post hoc comparisons of changes in Ki67 (marker of biological response to letrozole) vs. changes in metabolic markers were performed. Ki67+ cells were substantially decreased during neoadjuvant letrozole therapy (paired t-test p<0.0001). Linear regression analysis of residual tumor cell proliferation [post-letrozole log2(Ki67+1)] vs. letrozole-induced change in log2(MT-CO2) showed that tumors with more residual cell proliferation had greater levels of MT-CO2 (p=0.0278). Patients enrolled in NAOMI had a rate of adherence to adjuvant ET of 82%, higher than historical averages. Conclusions: NET is associated with a decrease in Ki67+ breast cancer cells however it has minimal impact on fatty acid and mitochondrial metabolism. It is suggested to increase adherence to adjuvant ET. Additional studies are planned to elucidate mechanisms of resistance in those samples that did not show a decrease in Ki67. Clinical trial information: NCT04568616 .

Inflation – Causes and Measures. Inflation in Romania

Abstract The causes of global inflation developments are the central subject of political debates, after in 2022 it became a global phenomenon, remaining above the average of the pre-pandemic period. According to the World Bank, there is a wide range of shocks to global supply and demand, oil prices and global interest rates that together drive global inflation. According to the International Monetary Fund, the global recovery is becoming increasingly sluggish and with growing regional divergences, with a future downward trend in global growth below the historical average of the period 2000-2019. Moreover, it is estimated that inflation will subside in the next period, but without reaching the estimated target for 2025. However, Romania has the highest inflation rate in the EU, increasing double the average of the EU countries. There are views that monetary policy, more rigor in fiscal consolidation and lower international commodity prices can control inflation and rein in debt growth. But what measures have been adopted in this regard and why does Romania have a different evolutionary trajectory?

Creating and establishing a sustainable culture of clinical and translational research at an academic cancer center.

e23220 Background / Objectives: At the Ellis Fischel Cancer Center (EFCC) at the Univ. of Missouri (MU) School of Medicine (SOM), our recent (2016 - 2020) accurals to interventional oncology clinical trials (CT) have been suboptimal. Hence, the EFCC research leadership undertook a number of initiatives since 2021 to improve the function and productivity of the oncology CT enterprise. The objective of our retrospective review was to determine the impact of our initiatives including improving enrollments to clinical trials. Methods: These major programmatic initiatives have included the following: (1) CT portfolio analysis; (2) faculty survey to assess concerns/ needs related to enrollment; (3) Development of a "One-Pager" list of open CT; (4) Working with faculty leader "champions"; (5) Increasing the number of clinical research staff; (6) Initiation of a monthly "Clinical Trials Working Group (CTWG)" to monitor enrollment to clinical trials, troubleshooting obstacles to accruals and on-going CT portfolio analysis and adjustments; (7) Development of multi-disciplinary "Disease Working Groups" (DWGs) in Breast, GU, GYN-Onc., Lung, GI and CNS cancers to establish a Protocol Review & Monitoring System (PRMS) with the objective of aligning our clinical trials with our patient population; (9) Initiation of a monthly “Investigator Education Workshop” (IEW) to educate our faculty on the conduct of investigator initiated CT and pharma sponsored CT; (10) Initiation of a "Top Enrolling Ceremony" to honor the top enrollers to clinical trials. Results: The analysis of our CT portfolio revealed that in both December 2020 and May 2021, more than half [i.e., 52% (n = 17/33 and n = 13/25, respectively] of our CT had never enrolled a patient in the lifetime of the protocol. Since the implementation of the PRMS in late 2021, we have observed a continual decrease in the status of those clinical trials with “never” any enrollments from a historical average of 52% to 34% in January 2022, to 32% in January 2023 and to 28% in October 2023, p = 0.0173. In addition, implementation of the PRMS also had an immediate significant impact such that only 53% of all of the protocols reviewed by the Protocol Review and Monitoring Committee were approved in 2021 vs. a historical average of 75% from 2018 - 2020. Moreover, between 2021 and 2023, an average total of 82 patients were enrolled to interventional oncology clinical trials vs. the historical average of 42 patients per year from 2016- 2020, representing an overall increase of 95% p = 0.0004. Conclusions: The development and establishment of the PRMS within the last 3 years has had a very positive impact on our clinical trial enterprise at the EFCC by reducing active clinical trials that are not suitable for our patient population and significantly increasing our enrollment to interventional oncology clinical trials. For 2024, we will continue our implementation of the PRMS at the EFCC.

Assessment of extreme precipitation events in Brazil during 2023: Insights from CHIRPS and rain gauge data

This study conducts a comprehensive analysis of extreme precipitation events that occurred during the year 2023 in Brazil, aiming to provide valuable insights into climate risks and support decision-making related to disaster prevention. Leveraging data from the Climate Hazard Infrared Precipitation with Stations (CHIRPS) and complementing it with observational data from the network gauges of the National Center for Monitoring and Alerts of Natural Disasters (Cemaden), the research focuses on assessing indices derived from rainfall data, notably days with precipitation exceeding 50 mm, and juxtaposes these events against historical averages from 1992 to 2022 to discern anomalies. Data are aggregated at the municipal level, considering the maximum value within each municipal domain. The results highlight the influence of the El Niño phenomenon in the Southern region of Brazil, especially in Rio Grande do Sul, underscoring the importance of considering global and local climatic factors in impact assessment. Additionally, specific analyses are presented for municipalities such as Petrópolis (RJ), Manaus (AM), and São Sebastião (SP), revealing trends and anomalies that warrant attention for adaptation measures and disaster risk reduction. The study concludes that 2023 served as an illustrative year for climate projections, emphasizing the urgency to advance mitigation actions and reduce greenhouse gas emissions to address future climate challenges.

Dual generative adversarial networks for merging ocean transparency from satellite observations

ABSTRACT Satellite ocean transparency data have low spatial coverage due to cloud shading, sun glint, swath width, and temporal revisit. Merging multiple satellite ocean transparency data can improve spatial coverage and create a high-accuracy data set. This study proposed a new satellite ocean transparency merging model based on dual generative adversarial networks (ZSD-merging GAN), and the products of full-coverage and high-accuracy ocean transparency were produced. The ZSD-merging GAN comprises the guess GAN and the merging GAN. The guess GAN is used to generate the guess of the ocean transparency merged product, while the merging GAN combines the guess and satellite ocean transparency data to produce the merged product. The experiments show that the spatial coverage of the ZSD-merging GAN product is 100%. The root-mean-square error (RMSE) and average relative error (ARE) between the ZSD-merging GAN product and unmerged ocean transparency data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on JPSS1 are 4.31 m and 11%, respectively, which are better than 5.59 m and 17% for historical average, 5.55 m and 19% for guess product, 5.08 m and 17% for Poisson blending product, and 5.12 m and 21% for Kriging interpolation product.

Temporally dense monitoring of pathogen occurrence at four drinking-water well sites – Insights and Implications

Yearlong, event based, microbiological and chemical sampling was conducted at four public water supply well sites spanning a range of geologic settings and well depths to look for correlation between precipitation events and microbial occurrence. Near-continuous monitoring using autosamplers occurred just before, during, and after 5–7 sampling events triggered by rainfall and/or snowmelt. Microbial genetic material was noted at all four locations during all but one sampling event, but was exceedingly variable in time, where one sample would have no detections and the next sample could be a relatively high concentration. The highest microbial sums (microbial concentrations summed over an event) were observed during months in which precipitation exceeded historical averages. Extended wet conditions through the spring thaw resulted in the highest percentage of microbial positive samples, though at relatively low concentrations. Sampling events that followed drier than normal periods showed longer lag times between the onset of precipitation and microbial occurrence, as well as lower microbial detection rates. Although a general lag time pattern was observed at each site, the largest offset in time was observed at the site with the greatest depth to water. The study's temporally dense representation of drinking water pathogen characterization suggests that single event or infrequent periodic sampling of a drinking water supply cannot provide a representative characterization of the probability that pathogens are present, which likely has ramifications for calculating health risk assessments.

How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons

AI techniques are making inroads into the field of drug discovery. As a result, a growing number of drugs and vaccines have been discovered using AI. However, questions remain about the success of these molecules in clinical trials. To address these questions, we conducted a first analysis of the clinical pipelines of AI-native Biotech companies. In Phase I we find AI-discovered molecules have an 80-90% success rate, substantially higher than historic industry averages. This suggests, we argue, that AI is highly capable of designing or identifying molecules with drug-like properties. In Phase II the success rate is ∼40%, albeit on a limited sample size, comparable to historic industry averages. Our findings highlight early signs of the clinical potential of AI-discovered molecules.

Managing an epidemic within a pandemic: orthopedic opioid prescribing trends during COVID-19.

In response to the opioid epidemic, a multitude of policy and clinical-guideline based interventions were launched to combat physician overprescribing. However, the sudden rise of the Covid-19 pandemic disrupted all aspects of healthcare delivery. The purpose of this study was to evaluate how opioid prescribing patterns changed during the Covid-19 pandemic within a large multispecialty orthopedic practice. A retrospective review of 1,048,559 patient encounters from January 1, 2015 to December 31, 2022 at a single orthopedic practice was performed. Primary outcomes were the percent of encounters with opioids prescribed and total morphine milligram equivalents (MMEs) per opioid prescription. Differences in outcomes were assessed by calendar year. Encounters were then divided into two groups: pre-Covid (1/1/2019-2/29/2020) and Covid (3/1/2020-12/31/2022). Univariate analyses were used to evaluate differences in diagnoses and outcomes between periods. Multivariate analysis was performed to assess changes in outcomes during Covid after controlling for differences in diagnoses. Statistical significance was assessed at p < 0.05. The percentage of encounters with opioids prescribed decreased from a high of 4.0% in 2015 to a low of 1.6% in 2021 and 2022 (p < 0.001). MMEs per prescription decreased from 283.6 ± 213.2 in 2015 to a low of 138.6 ± 100.4 in 2019 (p < 0.001). After adjusting for diagnoses, no significant differences in either opioid prescribing rates (post-COVID OR = 0.997, p = 0.893) or MMEs (post-COVID β = 2.726, p = 0.206) were observed between the pre- and post-COVID periods. During the Covid-19 pandemic opioid prescribing levels remained below historical averages. While continued efforts are needed to minimize opioid overprescribing, it appears that the significant progress made toward this goal was not lost during the pandemic era.

Psychosocial Educational Guidance Center as a retention and permanence strategy

Introduction: In the CCAV de Facatativá Virtual Care Community Center of the National Open and Distance University (UNAD), students with difficulties in their academic process have been identified, reflected in low historical average, repetitive loss of courses, long stay at the institution with little progress in their studies making it difficult to complete their professionaltraining; Therefore, the need arises to create a Retention and Permanence strategy that allows support for students from the academic and personal spheres, which is why the Psychosocial Educational Guidance Center (COPE) was created. Objective: Create COPE as a retention and permanence strategy where timely attention is provided regarding the difficulties presented in the development of the educational process of CCAV de Facatativá students. Methodology: Qualitative research, with action research design, protocols, contact formats, semi-structured interviews, field notes and questionnaires were used to collect information. Results: Of the 49 users, 16 % were first-time students who continued their professionalization project with the university; 84 % corresponded to former students, of whom 81 % continued their academic process, and finally, 2 % of users decided to give up their academic training. Conclusions: It contributed to the promotion of personal reflection of users, and the potentialization of their personal resources, favoring the change of thoughts, emotions and/or behaviors in favor ofgreater academic and personal well-being.

Graph Convolutional Gated Recurrent Unit Network for Traffic Prediction Using Loop Detector Data

Traffic prediction is challenging due to the stochastic nonlinear dependencies in spatiotemporal traffic characteristics. We introduce a Graph Convolutional Gated Recurrent Unit Network (GC-GRU-N) to capture the critical spatiotemporal dynamics. Using 15-min aggregated Seattle loop detector data, we recontextualize the prediction challenge across space and time. We benchmark our model against Historical Average, LSTM, and Transformers. While Transformers outperformed other models, our GC-GRU-N came in a close second with notably faster inference time — six times quicker than Transformers. We offer a comprehensive comparison of all models based on training and inference times, MAPE, MAE, and RMSE. Furthermore, we delve into the spatial and temporal characteristics of each model’s performance.

Quantifying the Impact of Risk on Market Volatility and Price: Evidence from the Wholesale Electricity Market in Portugal

This research aims to identify suitable procedures for determining the size of risks to predict the tendency of electricity prices to return to their historical average or mean over time. The goal is to quantify the sensitivity of electricity prices to different types of shocks to mitigate price volatility risks that affect Portugal’s energy market. Hourly data from the beginning of January 2016 to December 2021 were used for the analysis. The symmetric and asymmetric GARCH model volatility, as a function of past information, help to eliminate excessive peaks in data fluctuations. The asymmetric model includes additional parameters to separately obtain the impact of positive and negative shocks on volatility. The MSGARCH model is estimated to be in two states, allowing for transitions between low- and high-volatility states. This approach effectively represents the significant impact of shocks in a high-volatility state, indicating an acknowledgment of the lasting effects of extreme events on financial markets. Furthermore, the MSGARCH model is designed to obtain the persistence of shocks during periods of elevated volatility. Accurate price forecasting aids power producers in anticipating potential price trends and allows them to adjust their operations by considering the overall stability and efficiency of the electricity market.