Maximum Research Articles

Analysis of the heterogeinety impact of oil prices shocks on exchange rates in oil exporting countries: evidence of quantiles-on -quantiles approach

The objective of this study is to investigate the heterogeneous dynamic between exchange rate returns and oil prices in oil exporting countries. Precisely, the researchers used the exchange rates returns of both low incomes, emerging and developed majors oil exporting countries, and decomposed oil price shocks to study the heterogeneity of the intrinsic dynamic between the two variables in these countries, using the quantile-on-quantile analysis. The findings revealed that, although the dynamic between oil prices shocks and exchange rates returns is largely negative in these countries, there are important variability of the shocks impacts across the quantile distribution of both exchange rates returns and the different oil price shocks. More precisely , the study reavealed the heterogeneity of the different oil shocks impacts (oil aggregate demand , oil supply and oil specific demand shocks) on the exchange rates return of a given country , with the shape of the QQR graphs being different according to the source of shock and the magnitude of oil demand and oil specific demand shocks being more significant than that of oil supply shocks in many of the cases. In the same way , the analysis results also indicate that the dynamic between oil price shocks and exchange rate is heterogenous across the countries , with the impact of the shocks being more significant in developing countries than it is in developed countries . As well the behavior of the exchange rates returns at extreme values is different from one country to another. From this insight, the researcher recommends an economy diversification mainly for oil developing countries, and country-specific economy policies to mitigate the incidence of oil shocks, as the impact differs from one country to another.. Keywords: Exchange Rates, Oil Prices, Quantile-on-Quantile, Oil Exporting Countries.

Maximum non-bounded difference method for overbounding Global Navigation Satellite System errors

AbstractGlobal Navigation Satellite Systems (GNSS) support numerous applications, including mission-critical ones that require high integrity for safe operations such as air, maritime, and land-based navigation. These applications require ensuring the upper bound of the positioning computation, including overbounding any assumptions made in positioning algorithms. One of the main assumptions in the GNSS positioning algorithm is that the error follows a Probability Density Function (PDF), widely assumed to be Gaussian. This assumption should be overbounded to ensure safety. Two mathematical definitions of distribution overbounding have been proposed: Cumulative Distribution Function (CDF) overbounding and paired overbounding. Achieving these definitions has been an open problem for the past 20 years, and to date, no methods have effectively achieved either definition in multimode datasets. This paper proposes the first overbounding method, the Maximum non-Bounded Difference (MnBD) method, capable of achieving both definitions. Its effectiveness is assessed by comparison with the combined CDF bounding and paired overbounding approach. The results show that the MnBD method successfully achieves overbounding in both real datasets and 1000 simulated multimode data, tested on Gaussian and Generalized Extreme Value (GEV) distributions. The latter demonstrates MnBD’s ability to handle asymmetric distributions. The developed approach can be utilized in overbounding the GNSS error at the system level and user level to ensure the system integrity.

Natural assets, changes, and variations of the socioeconomic-environmental systems along the Asian drylands belt

Abstract The amount of and changes in a country's natural assets are crucial for developing national strategic plans and policies due to their foundational role in determining the resilience of social-environmental systems (SES), especially under a changing climate. Many integrated metrics on natural assets have been proposed based on individual measures to provide insights into the state of national natural resources. This is particularly true for countries experiencing extreme environmental stresses. Drawing on longitudinal data spanning from 1980 to 2020, the objective of this study is to analyze nuanced distinctions across 23 political entities (PEs) in the dryland regions of mid-latitudinal Asia, investigating their interannual variabilities over the four-decade study period and discerning potential driving forces. We examined three key integrated measures of the SES: ecosystem water use efficiency (WUE), human appropriation of net primary production (HANPP), and human appropriation of water resources (HAWR). We found increased variability as well as spikes in extreme values in each of these three measures of SES function among the 23 PEs over the study period. Water stress played an increasing and more important role than temperature in influencing the magnitude and variations of the three measures. Our results also indicate that human interventions may help increase the efficiency of water use in this dryland region of the world.

Extreme Precipitation Events During the Wet Season of the South America Monsoon: A Historical Analysis over Three Major Brazilian Watersheds

Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas have significant socioeconomic impacts. The Madeira, São Francisco, and Paraná river basins, three major watersheds in Brazil, are especially vulnerable to wet and drought periods due to their importance as freshwater ecosystems and sources of water for consumption, energy generation, and agriculture. The scarcity of surface meteorological stations in these basins makes meteorological studies challenging, often using reanalysis and satellite data. This study aims to identify extreme weather (wet) and climate (wet and drought) events during the extended wet season (October to March) from 1980 to 2022 and evaluate the performance of two gridded datasets (CPC and ERA5) to determine which best captures the observed patterns in the Madeira, São Francisco, and Paraná river basins. Wet weather events were identified using the 95th percentile, and wet and drought periods were identified using the Standardized Precipitation Index (SPI) on a 6-month scale. In general, CPC data showed slightly superior performance compared to ERA5 in reproducing statistical measures. For extreme day precipitation, both datasets captured the time series pattern, but CPC better reproduced extreme values and trends. The results also indicate a decrease in wet periods and an increase in drought events. Both datasets performed well, showing they can be used in the absence of station data.

Deflection characteristics of semi-rigid base asphalt pavement under traffic speed deflectometer loading

The three-dimensional viscoelastic finite element simulation and on-site validation was combined, so as to study the characteristics of the traffic speed deflection slope curve of the semi-rigid base asphalt pavement. Results show that there is an inert point in the deflection slope curve when only the surface or base structural state changes. With the loading center approaching transverse crack, the ‘three extreme values’ attribute transitions gradually to ‘single extreme value’. For the surface-base penetrating longitudinal crack caused by uneven subgrade settlement, the peak value of deflection slope curve increases significantly as the crack approaches the loading center, reaching an order of magnitude difference, and the attribute transforms from ‘three extreme values’ to ‘single extreme value’. Indicators characterizing the layered structural state were proposed, and the indicator change rules and applicability for the pavement with different surface and base layer thicknesses, under different deflectometer speeds, with or without cracks, were discussed.

Exploring the Impact of Trade Credit and Debt on Systemic Risk in the Presence of Financial Distress and Constraints

Based on the extreme value and network contagion theories, this study intends to explore the effects of trade credit, and short/long-term debt on the systemic risk of 205 Pakistani non-financial firms over the period from 2005 to 2021. To evaluate the firms’ systemic contribution and vulnerability, we apply two firm-specific measures, the change in conditional value-at-risk (DCOVAR) and marginal expected shortfall (MES). Our findings reveal a significantly positive impact of trade credit, and short/long-term debt on the systemic contribution and vulnerability of these firms. The findings also indicate that the underlying relationship is significantly moderated by financial distress and financial constraints. Understanding how different forms of debt influence systemic risk can guide policymakers in crafting regulations or interventions to promote a healthier financial environment. Regulators may reevaluate the balance between short/long-term debt usages or develop guidelines for managing trade credit effectively to mitigate systemic risk.

Unraveling the physiochemical characteristics and molecular insights of Zein protein through structural modeling and conformational dynamics: a synergistic approach between machine learning and molecular dynamics simulations

This research article presents a comprehensive investigation into the three-dimensional structure, physicochemical characteristics and conformational stability of the Zein protein. Machine learning (ML) based homology modeling approach, was employed to predict the 3D structure of Zein protein. Convolutional neural networks (CNNs) were utilized for refining the model, capturing complex spatial features and improving decoy refinement. The predicted 3D structure of Zein protein showed a high-confidence score, i.e. C-score of 0.96. Physiochemical characteristic was also analyzed to investigate its protonation and deprotonation behavior across a range of pH values. A comprehensive analysis of the titration curve and electrostatic charges was performed to uncover valuable molecular insights into the zein protein’s charge distribution, electrostatic interactions and potential conformational changes. Molecular dynamics (MD) simulations were performed to analyze the zein structural behavior under different pH values (2.0, 4.5, 6.8, 10.0 and 12.5), ionic strengths (0 mM, 25 mM, 50 mM, 75 mM, 100 mM) and temperatures (300K, 350K, 375K). Our results demonstrated the influence of these factors on zein protein’s stability and conformational dynamics. At extreme pH values of 2.0 and 12.5, the Zein protein exhibited increased structural deviations and potential unfolding, while intermediate pH values closer to the protein’s isoelectric point (pI) demonstrated more compact and stable conformations. Analysis of root mean square deviation, radius of gyration, solvent accessible surface area and Ramachandran plot provided clear understandings of the protein’s compactness and surface exposure, confirming the impact of pH, ionic strength and temperature on the protein’s conformation.

Tail risk transmission between decomposed oil shocks and green finance markets: evidence from an EVT–TVP–VAR approach

ABSTRACT This paper provides a novel framework based on extreme value theory, i.e. the autoregressive conditional Fréchet–time-varying parameters–vector autoregression (AcF–TVP–VAR) framework, to explore the tail risk spillovers between decomposed oil shocks (supply, demand, and risk shocks) and green finance markets. The empirical results reveal that there are significant time-varying tail risk spillovers between the two markets, with spillovers intensifying during extreme events. Additionally, throughout the sample period, supply shock is found to be a net transmitter of tail risk, while clean energy market is a net receiver. Notably, the safe haven properties of green bond market are captured, indicating it can serve as a diversified investment option. The findings are important for financial investors and policymakers.

Extreme value statistics for analysing simulated environmental extremes

AbstractWe present the methods employed by team ‘Uniofbathtopia’ as part of a competition organised for the 13th International Conference on Extreme Value Analysis (EVA2023), including our winning entry for the third sub-challenge. Our approaches unite ideas from extreme value theory, which provides a statistical framework for the estimation of probabilities/return levels associated with rare events, with techniques from unsupervised statistical learning, such as clustering and support identification. The methods are demonstrated on the data provided for the EVA (2023) Conference Data Challenge – environmental data sampled from the fantasy country of ‘Utopia’ – but the underlying assumptions and frameworks should apply in more general settings and applications.

OWA operators in the insurance industry

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

The GARCH-EVT-Copula Approach to Investigating Dependence and Quantifying Risk in a Portfolio of Bitcoin and the South African Rand

This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be α=1.007, implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolio’s diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure.

Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI

Abstract: The volatility of global energy markets, particularly electricity prices, plays a crucial role in influencing international economic activities. With the ongoing global energy transition and the push for low-carbon development, predicting electricity prices has become increasingly important for policymakers and market participants. This paper explores the forecasting capabilities of the ARIMA and LSTM models in analyzing electricity prices in the United States, drawing from data spanning 2001 to 2024.ARIMA, a traditional time series model, is valued for its simplicity and effectiveness in capturing linear trends, while LSTM, a deep learning-based model, excels at handling long-term dependencies in complex datasets. This study reveals that while both models offer valuable insights, each exhibits limitations. ARIMA struggles with non-linear patterns and volatility, whereas LSTM tends to underestimate extreme price values. The findings highlight the potential of hybrid models that combine traditional and machine learning approaches to enhance forecasting accuracy in the increasingly dynamic energy market. This research provides essential guidance for improving decision-making processes in the context of the global shift towards clean energy.

SpatialGEV: Fast Bayesian inference for spatial extreme value models in R

SpatialGEV: Fast Bayesian inference for spatial extreme value models in R

Size scaling of large landslides from incomplete inventories

Abstract. Landslide inventories have become cornerstones for estimating the relationship between the frequency and size of slope failures, thus informing appraisals of hillslope stability, erosion, and commensurate hazard. Numerous studies have reported how larger landslides are systematically rarer than smaller ones, drawing on probability distributions fitted to mapped landslide areas or volumes. In these models, much uncertainty concerns the larger landslides (defined here as affecting areas ≥ 0.1 km2) that are rarely sampled and often projected by extrapolating beyond the observed size range in a given study area. Relying instead on size-scaling estimates from other inventories is problematic because landslide detection and mapping, data quality, resolution, sample size, model choice, and fitting method can vary. To overcome these constraints, we use a Bayesian multi-level model with a generalised Pareto likelihood to provide a single, objective, and consistent comparison grounded in extreme value theory. We explore whether and how scaling parameters vary between 37 inventories that, although incomplete, bring together 8627 large landslides. Despite the broad range of mapping protocols and lengths of record, as well as differing topographic, geological, and climatic settings, the posterior power-law exponents remain indistinguishable between most inventories. Likewise, the size statistics fail to separate known earthquakes from rainfall triggers and event-based triggers from multi-temporal catalogues. Instead, our model identifies several inventories with outlier scaling statistics that reflect intentional censoring during mapping. Our results thus caution against a universal or solely mechanistic interpretation of the scaling parameters, at least in the context of large landslides.

Multiscale estimates for the condition number of non-harmonic Fourier matrices

This paper studies the extreme singular values of non-harmonic Fourier matrices. Such a matrix of size m × s m\times s can be written as \[ Φ = [ e − 2 π i j x k ] j = 0 , 1 , … , m − 1 , k = 1 , 2 , … , s \Phi =[ e^{-2\pi i j x_k}]_{j=0,1,\dots ,m-1, k=1,2,\dots ,s} \] for some set X = { x k } k = 1 s \mathcal {X}=\{x_k\}_{k=1}^s . Its condition number controls the stability of inversion, which is of great importance to super-resolution and non-uniform Fourier transforms. Under the assumption m ≥ 6 s m\geq 6s and without any restrictions on X \mathcal {X} , the main theorems provide explicit lower bounds for the smallest singular value σ s ( Φ ) \sigma _s(\Phi ) in terms of distances between elements in X \mathcal {X} . More specifically, distances exceeding an appropriate scale τ \tau have modest influence on σ s ( Φ ) \sigma _s(\Phi ) , while the product of distances that are less than τ \tau dominates the behavior of σ s ( Φ ) \sigma _s(\Phi ) . These estimates reveal how the multiscale structure of X \mathcal {X} affects the condition number of Fourier matrices. Theoretical and numerical comparisons indicate that the main theorems significantly improve upon classical bounds and recover the same rate for special cases but with relaxed assumptions.

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Modeling of Reversed Fréchet Distribution under Linear Normalizing

Extreme value theory (EVT) is becoming increasingly important as a suitable model of extreme events to represent phenomena in various fields of applications where extreme values may appear and have detrimental effects. In EVT, the Reversed Fréchet (RF) distribution is a special case of the Reversed generalized extreme value distribution (RGEVD) for modeling extreme data under linear normalizing. The purpose of this paper is divided into four objectives: Firstly, we discuss the practical aspects of the two methods that belong to the domain attraction of RF distribution. The second objective is to use RF distribution to model the behavior of two extreme air pollutants. The third objective is based on a diagnostics plot and hypothesis testing used to evaluate an appropriate shape of the tail distribution. Lastly, for the fourth objective, we attempt to obtain the return level period (RLP) that is expected to be exceeded. Maximum likelihood estimation (MLE) is derived and used to estimate the parameters of RF distribution. The results show that applications are significant for modeling by RF distribution in all cases of two air pollutants. Software that was used in all computations and graphics is the R statistical program with packages fExtreme and ismev.

Digitally Driven Urban Governance: Framework and Evaluation in China

With the rapid development of digital technology, the role of digitalisation in urban governance continues to emerge. Building a theoretical analysis framework and evaluation system of digitally driven urban governance has important theoretical and practical significance for stimulating the efficiency of digital technology tools and improving the energy level of urban digital governance. This paper aims to explore the mechanism of urban governance enabled by digital technology, innovatively change the previous thinking mode that only attaches importance to facility construction and e-government platforms, adopt ecological thinking, and comprehensively consider the role of “soft elements” such as strategic support, industrial support, the security environment, talent support, and the market environment. Then, the extreme value variance method and the coefficient of variation method are used to calculate the overall capacity and secondary index scores of each city, and the standard deviation of secondary index scores is used to represent the sub-environmental balance of the cross-sectional data of China’s provinces. In order to further explore which indicators restrict the improvement of China’s urban digital governance capacity, this study also constructs an obstacle degree model. The results show the following: (1) The overall capability of China’s digitally driven urban governance is low, with a total score of 27.25, indicating that China’s digitally driven urban governance is in its infancy. (2) There is a significant development imbalance among Chinese provinces, with Beijing ranking first with a score of 81.16, and Tibet, Qinghai, Xinjiang, Heilongjiang, and Ningxia scoring less than 13.30 points, ranking as the bottom 5 among the 31 provinces. (3) The shortcomings of talent support, industrial support, and the security environment restrict the improvement of the entire digital ecological governance ability.

Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation

The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes.

Electrochemical Investigation of Symmetric Aminoquinones

Abstract Quinones have a wide range of valuable properties and potential applications in medicinal chemistry, materials science, optoelectronic devices, and batteries. Molecular redesign using different functional groups, like amines, can optimize their properties and prevent undesired side reactions. However, the synthesis of aminoquinones can be particularly challenging at times, and there is a need for simple and efficient routes to access these compounds without metal catalysts or halogenated starting materials. Here, we demonstrate the synthesis and electrochemical characterization of a series of aminoquinones derived from renewable sources, namely vanillin or 2-methoxyhydroquinone. We employ a series of primary and secondary amines, varying in their electronic situation as well as steric demand. Depending on the type of starting material, either the desired aminoquinone or the related Schiff-base adduct was obtained. The aminoquinones were further explored towards their stability at different pH values. At extreme pH values, the deeply colored aminoquinones decompose, accompanied by decolorization of the solutions within a few minutes (pH 14) or hours (pH 1). At intermediate pH values (3-8) the aminoquinones are stable upon storage in solution, where they feature a quasi-reversible redox chemistry and fast, diffusion limited kinetics.