Index Series Research Articles

Risk Modeling and Connectedness Across Global and Industrial US Fintech Stock Market: Evidence from the COVID‑19 Crisis

The main purpose of this paper is to test the performance of GARCH models in estimating and forecasting VaR (value at risk) of the US Fintech stock market from July 20, 2016, to December 31, 2021. In addition, this study examines the impact of COVID‑19 on the risk spillover between the adequate VaR series of the US global KFTX index and the five Fintech industries. Specifically, we compare different VaR estimates (862 in‑sample daily returns) and predictions (550 out‑of‑sample daily returns) of several GARCH model specifications under a normal and Student‑t distribution with 1% and 5% significance. The Backtesting results indicate that I‑GARCH with Student‑t distribution is a good model for estimating and forecasting VaR of the US Fintech stock market before and during COVID-19. Moreover, the total connectedness results suggest that global and each Fintech industry increases significantly under turbulent market conditions. Given these considerations, this paper provides policymakers and regulators with a better understanding of risk in the Fintech industry without inhibiting innovation.

Indices of oral and written narratives differentiating Mandarin-speaking school-age children with and without developmental language disorder

Little evidence has been reported for narrative-related difficulties for Chinese Mandarin-speaking school-age children with developmental language disorder (DLD). This study aimed to capture the indices from narratives that can differentiate Chinese children with and without DLD. Oral narrative and written narrative samples were collected from children with DLD ( Mage = 8.37, SD = 1.25; N = 26) and typically developing (TD) children ( Mage = 8.46, SD = 1.28; N = 26). The narrative samples were compared on a series of indices of narratives in both oral and written narratives. Results showed that at the macrostructure, Mandarin Chinese-speaking school-age children with DLD lagged behind TD children in story structures; at the microstructure, children with DLD had weaker performance in story productivity, lexical diversity, and syntax diversity. Moreover, less group difference was found in the written modality relative to the oral modality. The results suggest indices of story productivity, lexical diversity, and syntactic complexity could be potential linguistic markers in the assessment of school-age Mandarin-speaking children with DLD. The present findings advance our understanding of deficits in narratives for Mandarin-speaking children.

An exergy-ecology assessment method and its application for basic building heating schemes

ABSTRACT Building heating is the main type of heating consumption in China. At present, there is a lack of attention given to the assessment of ecological cost or impact considering energy depreciation for various heating schemes. In this work, a method of exergy-ecology assessment is proposed to evaluate the energy and ecological characteristics of four basic building heating schemes. The solar-equivalent joule was adopted as the unified quantitative value of ecological impact by converting exergy, material, and cost flows. The exergy-ecology balance was established for each scheme, and a series of indexes were proposed and calculated through two strategies. The results show that the objective exergy efficiency of Scheme 3 heat pump is the highest (53.87%) and that of Scheme 2 electric heater is the lowest (17.58%). Larger difference of energy grade between energy providing side (0.83 for gas fuel and 1 for electricity) and receiving side (0.18 for output water) connects with lower exergy efficiency. Scheme 3 heat pump has the highest eco-exergy efficiency under both assessment strategies (71.89% and 53.86%), followed by Schemes 4 solar collector (61.52% and 36.34%). This method proves that the thermodynamics perfectness of system is positively correlated with ecological sustainability and can be used to assess other energy systems.

Machine‐Learning Based Identification of the Critical Driving Factors Controlling Storm‐Time Outer Radiation Belt Electron Flux Dropouts

AbstractUnderstanding and forecasting outer radiation belt electron flux dropouts is one of the top concerns in space physics. By constructing Support Vector Machine (SVM) models to predict storm‐time dropouts for both relativistic and ultra‐relativistic electrons over L = 4.0–6.0, we investigate the nonlinear correlations between various driving factors (model inputs) and dropouts (model output) and rank their relative importance. Only time series of geomagnetic indices and solar wind parameters are adopted as model inputs. A comparison of the performance of the SVM models that uses only one driving factor at a time enables us to identify the most informative parameter and its optimal length of time history. Its accuracy and the ability to correctly predict dropouts identifies the SYM‐H index as the governing factor at L = 4.0–4.5, while solar wind parameters dominate the dropouts at higher L‐shells (L = 6.0). Our SVM model also gives good prediction of dropouts during completely out‐of‐sample storms.

Changes in extreme precipitation indices across Algeria climate zones

AbstractExtreme precipitation events can have a significant impact on the environment, agriculture, economy and safety, making close monitoring of their short‐ and long‐term trends essential for the development of effective mitigation and adaptation strategies. In this study, we analysed 16 in situ observation datasets from four different climate zones in Algeria, spanning from 1969 to 2021. The trend analysis was conducted using the original Mann–Kendall test and seven modified tests to eliminate the effects of short‐term persistence. Our findings reveal a significant increasing trend of extreme precipitation variability for most stations in the Warm Mediterranean climate zone, except for the Consecutive dry days index, which showed a negative trend for the same zone, while stations in the Cold/Warm semi‐arid climate and Cold desert climate (Bwk) zones showed a decreasing trend. Additionally, all index series with significant long‐term trends were affected by a significant shift in their means, which was confirmed by both the Lombard and Pettitt tests. However, when we used the modified MPT and the test eliminating the effects of long‐term persistence, the significance of the shifts and the trend decreased. Our results suggest that while extreme precipitation events have been increasing in some parts of Algeria; the trend may not be statistically significant in the long‐run, indicating the necessity of revisiting and refreshing the findings of previous studies for a more current perspective.

Making temporal dynamics relevant: The role of macrophytes in the phenological complexity of a shallow freshwater lake

AbstractA multidisciplinary approach demonstrates how submerged macrophytes generate high phenological variability in Hungary's Lake Balaton. A 239‐month time series of water chlorophyll indices derived from Landsat 7 imagery from 1999 to 2019 was used. These data facilitated the generation of area‐based phenological patterns, which allowed an assessment of phenological variability by correlating chlorophyll index sequences with spatially adjacent values. The results showed that phenological variability was consistently low (below 5%) at the farthest points from the shore, indicating uniform phenological processes in the pelagic zone of Lake Balaton. Conversely, the littoral zone showed almost eight times higher variability, indicating increased diversity in shallow water areas. In particular, extensive macrophyte biomass datasets revealed a direct relationship between increased phenological variability in the littoral zone and macrophyte biomass (Spearman rank correlation: 0.893). This research highlights contrasting phenological patterns between phytoplankton and macrophyte communities, driven by different life cycles, and the possibility of effectively using satellite data to delineate phenological separation within lakes.

Bayesian Structure Learning for Climate Model Evaluation

AbstractA Bayesian structure learning approach is employed to compare and contrast interactions between the major climate teleconnections over the recent past as revealed in reanalyses and climate model simulations from leading Meteorological Centers. In a previous study, the authors demonstrated a general framework using homogeneous Dynamic Bayesian Network models constructed from reanalyzed time series of empirical climate indices to compare probabilistic graphical models. Reversible jump Markov Chain Monte Carlo is used to provide uncertainty quantification for selecting the respective network structures. The incorporation of confidence measures in structural features provided by the Bayesian approach is key to yielding informative measures of the differences between products if network‐based approaches are to be used for model evaluation, particularly as point estimates alone may understate the relevant uncertainties. Here we compare models fitted from the NCEP/NCAR and JRA‐55 reanalyses and Coupled Model Intercomparison Project version 5 (CMIP5) historical simulations in terms of associations for which there is high posterior confidence. Examination of differences in the posterior probabilities assigned to edges of the directed acyclic graph provides a quantitative summary of departures in the CMIP5 models from reanalyses. In general terms the climate model simulations are in better agreement with reanalyses where tropical processes dominate, and autocorrelation time scales are long. Seasonal effects are shown to be important when examining tropical‐extratropical interactions with the greatest discrepancies and largest uncertainties present for the Southern Hemisphere teleconnections.

Innovative Decision Fusion for Accurate Crop/Vegetation Classification with Multiple Classifiers and Multisource Remote Sensing Data

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.

Modelling The Volatility of Frankfurt Stock Exchange (DAX) Returns Using hybrid Models

Recently, the interest of researchers in the use of hybrid models in the process of analyzing model time series with fluctuations and forecasting fluctuations in financial time series has increased significantly. Hybrid ARMA-GARCH models were created for medium- and long-term forecasts of time series of financial market index prices: ARMA models are used to analyze their linear component, which is a combination of autoregressive models and moving average models, and GARCH models are used to analyze the nonlinear component. which are generalized autoregressive models that depend on the nonconstancy of variance models. Hybrid ARMA-GARCH models eliminate the weaknesses and gaps that exist in each group of models (ARMA and GARCH) separately, which increases their forecasting accuracy and reliability, so they have already been successfully applied to model and forecast daily stock returns for three standard indices in the USA. The purpose of this article is to investigate which of the hybrid ARMA-GARCH models is optimal for forecasting the return of the DAX index, which is the most important stock index of the German securities market. It is the German equivalent of the American Dow Jones Index, has been calculated since 1988 by Deutsche Börse AG and reflects the total return on capital of the largest stock companies listed on the Frankfurt Stock Exchange (currently 40; by 2021 – 30): calculated as a weighted average of capitalization of the value of Free Float share prices on the Xetra electronic exchange, and also takes into account dividends on shares, assuming that the dividend is reinvested in the share on which it was accrued. The database of this study consisted of the daily closing prices of the DAX index presented on the official website of the Frankfurt Stock Exchange during the period from 01.01.2018 to 09.30.2023 (altogether about 1,500 observations), the stability of the time series was assessed using Expanded Dickey Fuller Liquidity (ADF). The article proposes 7 hybrid models, from which the one that is best suited for modeling the volatility of the DAX index is selected. It is an ARMA (2,3)-EGARCH (1,1) model because it captures volatility and leverage effects on DAX returns and its expected returns more than other models. The selection of the best alternative from the developed array of hybrid models was carried out according to the following criteria: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), H-QIC (Hannan-Quinn Information Criterion).

Forecasting World Food Price Volatility: Performance of the GARCH Model with Different Distributions Assumptions

This study examines the performance of the GARCH model with two different error distribution assumptions in forecasting the volatility of the global food price indices. For this purpose, it uses the monthly series of the world food price index (FPI), Meat Price Index (MPI), Dairy Price Index (DPI), Cereals Price Index (CPI), and vegetable Oil Price Index (OPI) sourced from the FAO database over a period from 1990M1 to 2022M3. We found that the model selected by the Akaike information criteria (AIC), the Schwarz criteria (SC), and the Hannan-Quinn (HQ) criteria performs well in the prediction of the volatility of all five food price indices. Further, the in-sample and out-of-sample performance analysis revealed that the GARCH models selected by the information criteria, both the normal and student-t distributions perform equally well in forecasting volatility. Finally, the analysis of the volatility series extracted from the best-fit GRACH models shows that the world food price has witnessed unusually large and sustained volatility during the periods 1994-95, 2007-08, and 2011-12. However, the magnitude of the fluctuations in the world food price observed during the recent Covid19 pandemic period was relatively mild, with the exception of vegetable oil prices.

Оценка сезонной динамики фотосинтетических пигментов в листьях кленов с помощью вегетационных индексов, рассчитанных по данным гиперспектральной съемки

The article presents the results of studies on seasonal changes in the content of photosynthetic pigments in maple leaves. As a result, time series of pigment concentrations and values of 80 vegetation indices (VI) were obtained for three growing seasons (2021-23). It was found that the time series of a large group of VI (CARI, Carter4, Datt3, DD, Maccioni, MTCI, mSR2, NDVI2, OSAVI2, REP_Li, SR6, TCARI_OSAVI, Vogelmann2, Vogelmann et al. - in total, more than 25 VI) have a well-defined seasonal dynamics, coherent with the seasonal dynamics of chlorophyll concentrations. According to the test results, the models with CORE/OS AVI as an independent variable are the best for approximating the values of the chlorophyll sum. The NDVI index proved to be ineffective as a proxy metric for chlorophyll concentration. The seasonal dynamics of chlorophyll content and values in And is species-specific. Keywords: ACER, CHLOROPHYLL, CAROTENOIDS, TIME SERIES OF VEGETATION INDICES, SEASONAL DYNAMICS

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Impact of corporate social responsibility on bank performance in emerging markets

PurposeThis study investigates the relationship between corporate social responsibility (CSR) and financial performance of the banking sector from the prospective of emerging countries.Design/methodology/approachThis study obtained balance sheet and income statement data for 173 banks in 20 emerging countries from the Bankscope database from 2005–2018. The CSR-related data were taken from the Thomson Reuters ASSET4 database. Moreover, macroeconomic controls such as GDP per capita, inflation, and financial development are attained from the GFDD. The series of institutional quality indices (Political Stability, Rule of Law, Control of Corruption, Government Effectiveness, and Regulatory Quality) is obtained from the WGI. At the same time, national culture and bank regulation are attained from Hofstede Insights and Barth et al. (2013). We used the panel fixed-effects model in our baseline estimations, while 2SLS and GMM were applied to control for endogeneity.FindingsThe finding shows that CSR activities significantly improve bank performance, but the effect varies across the bank. Only environmentally friendly activities have shown a significant positive relationship with banking performance for CSR dimensions. However, the social and government dimensions did not significantly affect bank performance. Moreover, a sound institutional and regulatory environment and national norms play an important role in the nexus of CSR activities and bank performance.Originality/valueThis study provides empirical evidence that sheds light on CSR and bank performance in an emerging market context.

Cross-country comparisons in health price growth over time.

To examine how the United States compares in terms of health price growth relative to four other countries - Australia, Canada, France, and the Netherlands. Secondary data on health expenditure were extracted from international and national agencies spanning the years 2000-2020. International price indices specific to health were constructed using available international expenditure data and compared to existing health-specific national and general international price indices. Health expenditure data were extracted from the Organization for Economic Cooperation and Development (OECD) database. We obtained a time series of health price indices from the national agencies in each of the study countries. We find meaningful variation across countries in the rate at which health prices grow relative to general prices. The United States had the highest cumulative health price growth compared to general price growth over the years 2000-2020 at 14%, followed by Canada and the Netherlands. Unlike the other study countries, health prices in France grew consistently in line with general prices. Price growth for health care paid for by public funds and households grew at different rates across countries, where price growth was higher for public payers. US households faced the greatest mean annual price growth. The choice of price index has major implications for comparative analysis. Despite their widespread use internationally, general price indices likely underestimate the contribution of price growth to overall health expenditure growth. We find that in addition to its reputation for having high health price levels compared to other high-income countries, the United States also faces health price growth for goods and services paid for by government and households in excess of general price growth. Furthermore, US households are exposed to greater health price growth than households in comparator countries.

ЧИСЛЕННОЕ МОДЕЛИРОВАНИЕ ВРЕМЕННЫХ РЯДОВ БИОКЛИМАТИЧЕСКИХ ИНДЕКСОВ В АРКТИЧЕСКОЙ ЗОНЕ РОССИЙСКОЙ ФЕДЕРАЦИИ НА ОСНОВЕ СТОХАСТИЧЕСКОГО “ГЕНЕРАТОРА ПОГОДЫ”

The paper proposes an approach to the numerical stochastic modeling of the time series of the wind chill index and equivalent effective temperature at weather stations located in the Arctic zone of the Russian Federation. The approach is based on the use of a specially designed stochastic weather generator. It is shown that the approach allows developing the models of the time series of bioclimatic indices that very accurately reproduce various statistical properties of real processes related, in particular, to their daily variations and specific features of the study area.

Singularity Analysis of Actuation Coordination and New Indices for Optimal Design of Redundantly Actuated Parallel Manipulators

Abstract This paper focuses on the singularity of actuation coordination of redundantly actuated parallel manipulators and proposes a series of novel indices to evaluate the actuation coordination. The singularity is analyzed based on the coordinated dynamics model and divided into two categories: input singularity and output singularity. Influences of the singularity on the control performance of redundantly actuated parallel manipulators are studied with mechanisms 2RPU + 2UPR + RPR and 3RPR. Local and global performance indices of redundantly actuated parallel manipulators with actuation coordination are put forward, and the dimension optimization method considering the proposed indices is studied based on mechanism 6RPS. The research of this article possesses good reference significance for the performance analysis and control application of redundantly actuated parallel manipulators.

Can Growth Increase of Small Trees after Drought Compensate for Large Trees’ Growth Loss?

Large trees dominate stand-level biomass but their growth suffers more from droughts, while sheltering small trees during droughts. Under a warmer and drier climate, whether the growth decline of large trees could be compensated by prompted small trees’ growth remains unknown. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) series, drought characteristics were determined, and two drought events were selected. We reconstructed historical diameters at breast height (DBH) and the aboveground biomass of Larix gmelinii through tree ring data allometric equations. To clarify the difference in the responses of tree size to drought, we calculated resistance, recovery, and resilience in each diameter class. We used a growth dominance coefficient (GDC) to exhibit the contributions of different-sized individuals to stand growth and demonstrated the growth dynamics of both the individual and stand level. The results proved that large trees were more vulnerable to local droughts, the resilience of larch had a negative relationship with the DBH (p &lt; 0.05), and small trees could recover to even exceed their pre-drought growth level. Most plots had a negative GDC and small trees contributed more to stand growth compared with their size, but their AGB growth was far less than that of large trees, which made it difficult to compensate for stand growth decline. Our results indicate that tree resilience has a negative relationship with their pre-drought sizes, as large trees in the stand fail to regain their growth level before drought. Even with a larger relative contribution and higher resilience, small trees cannot cover deficits in large trees’ growth. Under more frequent droughts, the total aboveground biomass growth of larches would decline.

Measuring the International Influence of the RMB and its Regional Heterogeneity: Theory and Evidence from the Global FX Market Network

ABSTRACT This study assesses the global and cross-regional influence of China’s currency, the renminbi (RMB). We first develop a theoretical framework to determine the channel through which progress in RMB internationalization can be mapped onto the market influence of the RMB exchange rate on that of other currencies. Using a volatility connectedness approach, we then construct a series of spillover indices reflecting the international influence of the RMB. We find that the RMB’s influence is worldwide but is smaller than that of major international currencies, such as the US dollar. Overall, the RMB has basically become a peripheral currency in Greater China, Central Asia, Eastern Europe, and some South Asian economies. Because of the Belt and Road initiative, regionalization of the RMB has also progressed in developing economies; however, China still has far to go in fully achieving internationalization of its currency.

Comparing ARIMA and various deep learning models for long-term water quality index forecasting in Dez River, Iran.

Water scarcity poses a significant global challenge, particularly in developing nations like Iran. Consequently, there is a pressing requirement for ongoing monitoring and prediction of water quality, utilizing advanced techniques characterized by low implementation costs, shorter timeframes, and high accuracy. In the present study, the investigation and forecasting of the monthly time series of a single-variable river water quality index have been addressed using ten water quality parameters. Daily monitoring data from four stations in the Dez River from 2010 to 2020 have been utilized to obtain the river water quality index value from the dataset. The Shannon entropy method has been employed to assign weights to each water quality parameter. Utilizing the integrated autoregressive integrated moving average (ARIMA) model, which ranks among the most extensively employed models for time series forecasting, and five deep learning models including Simple_RNN, LSTM, CNN, GRU, and MLP, the water quality index for the following year is predicted. The performance of the prediction models is evaluated using RMSE, MAE, MSE, and MAPE as evaluation metrics. The results indicate that the ARIMA model performs worse than the deep learning models, with the MSE, RMSE, MAE, and MAPE values for this model being 81.66, 9.037, 6.376, and 6.749, respectively. The deep learning models show results close to each other, demonstrating similar statistical index values. The outcomes of this study assist relevant decision-makers in planning and implementing necessary actions to enhance water quality, particularly freshwater resources in rivers.

Short-Term Tree-Ring Series of Pinus hartwegii Lindl. Taken at Ground Level Correlate to Normalized Difference Vegetation Index Series

In this work, we propose that small wood core samples (≤10 cm length), taken from 0.3 m above the soil, represent an accurate means of correlating forest growth with remote sensing data. Short-term correlations between the Normalized Difference Vegetation Index (NDVI) and tree-ring components of the last three to four decades were tested, using 210 Pinus hartwegii wood cores collected at two locations, Tláloc (TLA) and Jocotitlán (JOCO) in central Mexico. The NDVI time series were generated with the Google Earth Engine (GEE) using Landsat 8 images. Also, seasonal trends in NDVI (e.g., spring, summer, autumn, winter) were analyzed through longitudinal analysis. The results showed more statistically significant dendrochronological indices in TLA than in JOCO, but both locations consistently showed an NDVI decrease in 2018 and 2020, indicating a reduction in vegetation vigor. At the two locations, the minimum and maximum NDVI occurred in April and October, respectively. Seasonal NDVI changes for spring were mainly seen at TLA with a decreasing trend, which may be related to a less defined dry season. The significant correlations (p &lt; 0.05) between tree-ring components and the NDVI occurred in the dry season, indicating that the productivity of a given year is defined by the tree vigor shown in April and May, in the case of TLA, and between January and March, for JOCO. Although the NDVI values of JOCO were higher than those of TLA, tree growth, expressed by tree-ring indices, was lower. Our proposed field method to correlate tree-ring information and the NDVI is reliable and can be used in other coniferous forests.