Search Results (917)

Crypto-asset markets have been rapidly evolving during the past years, being under the spotlight of a diverse set of actors in the financial ecosystem, including investors, financial institutions, regulators and academics. Their potential interconnections with the traditional financial markets are important, and identifying them can provide useful insight in a diversity of areas such as risk contagion and mitigation, price formation, portfolio management and regulatory framework design. In order to identify such interconnections, various lines of research are followed. Specifically, the correlation between prominent stock market indices and crypto-assets from 2018 to 2025 is examined, while their volatility is also evaluated. Furthermore, the relevant effect of news, events and announcements is explored. The results are based on both daily and high-frequency datasets, with the use of the latter focusing on intra-day variation. The analysis of the results identifies existing interconnections between 2020 and 2025, as well as the important respective impact of news and announcements. An additional generic outcome is the usefulness of high-frequency datasets in the crypto-asset context. The conclusions are useful for all actors in the financial ecosystem. Future work can focus on the extension of the research to additional markets or crypto-assets. Full article

This study employs a sample of 25 active and 22 passive AI ETFs to examine several issues surrounding their performance, risk, pricing efficiency, and persistence in pricing discrepancies and their impact on ETFs’ performance combined with the respective impact of intraday volatility. The relationship between AI ETFs’ performance and market factors concerning size, value, profitability, investment and momentum is evaluated too. The results indicate that the passive AI ETFs have outperformed active ones over their entire trade history, without, however, shouldering their investors with materially higher volatility. Moreover, both AI ETF groups trade at a persistent premium to their NAV. The concurrent premium positively affects return, while the one-period lagged premium is negatively related to return. In addition, a negative relationship between return and concurrent intraday volatility and a positive (but less strong) relationship between return and one-period lagged intraday volatility are found. Moreover, the majority of AI ETFs do not generate significant alphas. Finally, market factors effectively explain the performance of AI ETFs. Full article

This study frames next-day ETF market behavior as a binary regime classification problem—distinguishing between “oscillating” days, on which intraday price movements remain within a defined threshold, and “trending” days, on which movements exceed that threshold. This framing is economically motivated: active traders employing Martingale-style strategies and ETF options traders require precisely this type of regime prediction to manage risk and time positions. Using 25 years of daily data (2000–2024) for four major ETFs—IWM (Russell 2000), SPY (S&P 500), QQQ (Nasdaq-100), and DIA (Dow Jones)—the study trains and evaluates Random Forest and Neural Network classifiers enriched with macroeconomic announcement indicators and technical features (VIX, RSI, ATR) under a rolling window cross-validation framework. Oscillation is defined as daily intraday price movements within thresholds of 0.5%, 0.75%, and 1%; movements exceeding these levels constitute trending behavior. At the 0.5% threshold—the most practically relevant given typical ETF transaction costs—Neural Networks outperform a naive classifier by 13.4% for IWM, 15.4% for SPY, 4.7% for QQQ, and 3.2% for DIA. AUC values exceed 0.5 in most configurations, with stronger discrimination observed for SPY (AUC up to 0.74) and IWM (AUC up to 0.59) than for QQQ and DIA at some thresholds. Results are stronger for some ETFs and thresholds than others, and cases where AUC approaches 0.5 are explicitly acknowledged as reflecting limited discriminatory power. Full article

This study provides a comprehensive evaluation of six volatility forecasting models applied to twelve dominant and less dominant cryptocurrencies across multiple time horizons using high-frequency intraday data. The exponential generalized autoregressive conditional heteroskedastic (EGARCH), integrated GARCH (IGARCH), standard GARCH, GJR-GARCH, lagged realized volatility (LRE), and heterogeneous autoregressive (HAR) models are systematically compared using 5 min computed return data from September 2018 to September 2020. Our analysis encompasses three forecast horizons (1-day, 7-day, and 30-day) to assess model performance under varying temporal constraints. Through univariate Mincer–Zarnowitz regressions, encompassing tests, and out-of-sample evaluation using root mean squared error (RMSE) and quasi-likelihood loss (QLIKE) functions, we identify significant performance heterogeneity across models and cryptocurrencies. The HAR model exhibits stronger predictive accuracy at short horizons, while EGARCH exhibits relatively stronger performance at longer horizons, although overall explanatory power declines as forecast horizon increases. Importantly, no single model consistently provides optimal forecasts across all cryptocurrencies. Consistent with prior evidence suggesting model performance varies across assets. Encompassing regressions reveal that combining HAR with EGARCH specifications significantly enhances explanatory power across all temporal frames. Out-of-sample Diebold–Mariano tests indicate that HAR generates the lowest forecast errors for most cryptocurrencies, though EGARCH performs exceptionally well for high-market-capitalization assets. These findings provide regime-conditional insights into horizon- and asset-specific volatility dynamics during the pre-institutionalization phase of cryptocurrency markets. The study contributes to emerging literature by incorporating less-dominant cryptocurrencies and offering robust empirical evidence on the asymmetric and persistent volatility characteristics unique to digital asset markets. These findings should be interpreted within the context of the 2018–2020 sample period, representing a pre-institutionalized phase of cryptocurrency markets, and may not fully generalize to structurally different market regimes characterized by increased institutional participation and regulatory development. Full article

(1) Background: Handgrip strength (HGS) is a widely used indicator of neuromuscular function, with predictive values for health and performance outcomes. The aim of this study was to evaluate the intraday and interday reliability of maximal and explosive handgrip force–time metrics using the Kinvent K-Grip handheld dynamometer. (2) Methods: Thirty-four participants performed three maximal voluntary isometric contractions per hand across two testing days. Force–time data were analysed for peak force (PF), mean force (MF), peak rate of force development (RFD), time-specific RFD, impulse, and forces at fixed time points. Reliability was assessed using intraclass correlation coefficients (ICCs), standard error of measurement (SEM), minimal detectable change (MDC), and coefficient of variation (CV%). (3) Results: The device demonstrated excellent relative and absolute reliability for PF and MF across both days (ICC > 0.97; CV < 6%; MDC ≈ 5 kg). Later-phase explosive metrics (F250 and Imp200) showed good-to-excellent relative reliability (ICC = 0.88-0.99; CV = 4–14%), although with variable absolute reliability (MDC F250 ≈ 4–8 kg, MDC Imp200 ≈ 1 kg·s). For early-phase metrics, relative reliability was only moderate to good (ICC = 0.67–0.88) and characterised by a high degree of variability (CV = 15–22%). (4) Conclusions: The K-Grip handheld dynamometer is a reliable tool for cross-sectional assessments and for tracking larger maximal strength and later-phase force improvements at fixed time points. Early-phase explosive metrics are less suitable for monitoring intervention effects due to high measurement error and fatigue sensitivity. Full article

With the increasing penetration of renewable energy in power systems, the effective utilization of pumped storage power plant (PSP) clusters for peak shaving has become an important issue in system operation. In this study, an intraday scheduling model for PSP clusters is formulated to minimize the variance of the system net load, while accounting for operational constraints, including power balance, unit operation, and reservoir energy evolution. The resulting model is a mixed-integer nonlinear programming (MINLP) problem, which is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Case studies are conducted on an improved IEEE 39-bus system under both conventional scenarios and extreme renewable energy conditions. The results show that, under a unified peak-shaving objective, PSP clusters exhibit a stable structure of role differentiation even in conventional operating conditions. As the system peak-shaving pressure increases, this differentiation is progressively reinforced along existing functional roles, shifting from renewable energy absorption to peak-period generation support. It tends to converge under high operational stress due to the coupling between load and renewable variability. Further analysis indicates that when capacity differences among PSPs are eliminated, the differentiation structure is significantly weakened, suggesting that physical capability differences constitute an important foundation for the formation of role differentiation. Full article

Biogeochemical processes, including the oxygen cycle, were investigated in Lake Baikal during the spring thermal bar using a coupled numerical model that takes into account the intraday variability of atmospheric parameters and contains the following variables: nitrate, ammonium, phosphate, oxygen, chlorophyll a, phytoplankton, zooplankton, and small and large detritus. Nitrification, photosynthesis, remineralization, and respiration processes describe the biochemical dynamics of oxygen in the model. As a study area, the deep-water cross-section of Lake Baikal, Boldakov River–Maloye More Strait, was considered using meteorological data for June 2024 at the lake surface. Numerical results show that the thermal bar can contribute to the transport of dissolved oxygen and phyto- and zooplankton to the deeper layers of the lake. Full article

GARCH models play a fundamental role in modeling time-varying volatility in financial return series. In practice, financial returns are also well known to exhibit heavy-tailed distributions, which naturally motivates the use of quasi-maximum exponential likelihood estimation (QMELE) for accurately capturing tail behavior and risk measures such as Value-at-Risk. At the same time, the increasing availability of intraday high-frequency data has led to the development of high-frequency augmented GARCH models, which incorporate intraday information into conventional low-frequency volatility frameworks. By exploiting transaction-level data recorded at very fine time scales, these models are able to capture intraday volatility dynamics and market microstructure effects that are not reflected in standard low-frequency observations. Against this background, this paper studies conditional quantile estimation for high-frequency augmented GARCH models. We develop QMELE-based estimators for both model parameters and conditional quantiles, and construct an adjusted test statistic for assessing model adequacy. The asymptotic properties of the proposed estimators and test statistic are established, and their finite-sample performance is examined through extensive simulation studies. Empirical applications to three major stock indices demonstrate that augmenting GARCH models with high-frequency information leads to substantial improvements in conditional quantile estimation compared with traditional low-frequency approaches. Full article

Accurate electricity load forecasting has become increasingly challenging in Türkiye due to rapid structural changes in the power system driven by renewable energy expansion. Between 2016 and 2022, solar capacity increased by 130% and wind generation by 83%, resulting in renewable-induced variability exceeding 160%. To assess how different forecasting approaches respond to this evolving environment, Facebook Prophet, XGBoost, and Deep Neural Networks (DNNs) were evaluated using more than 55,000 hourly load observations under a strictly chronological out-of-sample validation framework. The comparative analysis reveals substantial differences in model performance. XGBoost achieved the highest forecasting accuracy, with a Mean Absolute Error of 981.48 MWh, a Root Mean Squared Error of 1344.15 MWh, and a Mean Absolute Percentage Error of 2.72%, while effectively capturing rapid intraday variations and maintaining peak deviations within ±1100 MWh. DNN models delivered competitive overall accuracy (MAE: 997.82 MWh; MAPE: 2.77%) but exhibited a tendency to smooth temporal variations, leading to an underestimation of extreme winter peaks by up to 4100 MWh. In contrast, Prophet showed limited adaptability to the observed structural volatility, producing errors nearly seven times higher than XGBoost (MAE: 7041.79 MWh; RMSE: 8718.14 MWh). Based on these findings, a layered forecasting framework is proposed, employing XGBoost for short-term operational dispatch and reserving statistical models for long-term planning and policy analysis. Full article

In this paper, we propose a Lagrange multiplier (LM)-type unit root test for functional time series. The key novelty lies not in introducing a new LM principle but in establishing the asymptotic validity of such a test under the functional random walk null hypothesis without relying on functional principal component analysis (FPCA) or finite-dimensional unit root subspace assumptions. We derive the limit distribution of our proposed test statistics under the null hypothesis of a random walk and its asymptotic behavior of alternative hypotheses of trend stationary, weakly dependent stationary, and autoregressive stationary models. Specifically, we establish the theoretical consistency of the test under all aforementioned alternative hypotheses. Simulation studies corroborate these theoretical findings and demonstrate the desirable finite-sample performance of the proposed functional unit root test. The proposed test is also applied to real data of intraday stock price curves, and the test results are plausible. Full article

Background/Objectives: To evaluate the intraday and interday reliability of seated horizontal upper body (UB) isometric push and pull tests performed with the VALD DynaMo Max dynamometer. Methods: Fifty-two recreationally active individuals (41 men, 11 women; 25.0 ± 6.1 years) completed two sessions 48 h apart, each comprising three maximal-effort push and pull trials at 90° elbow flexion using a custom-built rig with the attached dynamometer. Peak force (PF), peak rate of force development (RFD), impulse, and time-to-PF were extracted from 1200 Hz force–time data. Reliability was assessed using the intraclass correlation coefficient (ICC), coefficient of variation (CV%), standard error of measure (SEM) and minimal detectable change (MDC). Results: PF demonstrated excellent reliability (ICC = 0.97–0.99) with low absolute error (CV < 6%; MDC = 128–149 N). Impulse showed good-to-excellent reliability (ICC = 0.90–0.94; CV < 10%; MDC ≈ 755–790 N·s), whereas RFD displayed good reliability but greater variability (ICC = 0.80–0.81; CV < 20%; MDC = 2574–2925 N·s⁻¹). Time-to-PF was the least reliable (ICC = 0.68–0.71; CV > 24%; MDC = 1.5–1.7 s). Conclusions: Horizontal isometric push and pull tests using the VALD DynaMo Max dynamometer provide reliable measures of PF and impulse for athlete profiling and tracking substantial longitudinal changes. Peak RFD may be cautiously used for broad cross-sectional comparisons, although its higher variability limits precision in distinguishing smaller inter-individual differences and appears less sensitive to within-individual changes. Time-to-PF demonstrated insufficient reliability for practical application. Full article

In order to cope with the challenge of large-scale integration of renewable energy to the balance of power supply and demand, and give full play to the potential of flexible regulation of iron and steel enterprises, a source load coordination optimization scheduling model considering the flexible coordination of iron and steel production and energy storage is proposed. Firstly, the multi-unit coupling adjustable capacity model of electric arc furnace (EAF), air separation unit (ASU), rolling mill and captive power plant is established, and the regulation characteristics and coupling relationship between different production units are clarified. Secondly, a day-ahead and intra-day two-stage scheduling framework is proposed. In the intra-day stage, the energy storage system is introduced to mitigate the fluctuation in wind power, and the mixed integer linear programming method is adopted to minimize the total operating cost of the system. Finally, an example is given to verify the effectiveness of the model. Case studies demonstrate that the proposed approach effectively reduces load variability and enhances operational stability. After the introduction of energy storage, the power standard deviation of EAFs and ASUs decreases by 29.6% and 28%, respectively, and the operational continuity of the rolling process is improved. Although the initial wind curtailment level in the test system is relatively low, the proposed strategy further mitigates peak curtailment and improves renewable accommodation capability. In addition, moderate operational cost savings are achieved. Full article

Phytic acid (PA), owing to its strong acidity and multidentate metal-chelating properties, readily forms multiple adduct/complex ions in mass spectrometry and is prone to pronounced matrix effects, resulting in complicated spectra and compromised sensitivity and quantitative robustness, which poses a major challenge for rapid and accurate PA quantification. Herein, we developed a rapid quantitative method for PA based on trimethylsilyldiazomethane (TMSD) methyl-ester derivatization coupled with paper spray mass spectrometry (PS–MS). PA was derivatized with TMSD to yield the methylated product (PA-Me), and the derivative solution was purified via “post-derivatization nitrogen blow-down followed by water reconstitution”, thereby markedly reducing background interference. In positive-ion mode, the stable sodium adduct ion [PA-Me+Na]⁺ (m/z 851.04) was used as the quantifier, enabling fast quantification with selected ion monitoring (SIM). PS–MS was performed with a 15 μL spotting volume and methanol/water (90/10, v/v, containing 0.1% formic acid) as the spray solvent, allowing rapid analysis without chromatographic separation. The method exhibited good linearity over 0.125–30 μg/mL (R² ≥ 0.9965), with a limit of detection (LOD, S/N = 3) of 0.080 μg/mL and a limit of quantification (LOQ, S/N = 10) of 0.270 μg/mL. The intra-day and inter-day precision values were both < 10% (RSD), and recoveries ranged from 87.2% to 122.4%. This LC-free strategy features low solvent consumption and high analytical throughput, and was validated using rice bran protein and rice bran polysaccharide samples, providing technical support for rapid screening and quality control of PA in complex food/plant matrices. Full article

Although deep learning has achieved remarkable success in time-series prediction, intraday algorithmic trading is characterized by frequent regime shifts (concept drift), which can rapidly render models trained on historical data obsolete in real applications. This motivates on-device adaptation at edge trading terminals. However, practical deployment is constrained by a tripartite bottleneck: real-time samples are scarce, hardware resources on edge are limited, and communication overhead between cloud and edge must be kept low to satisfy stringent latency requirements. To address these challenges, we develop a hardware-aware edge learning framework that combines federated learning (FL) and meta-learning to enable rapid few-shot personalization without exposing local data. Importantly, the framework incorporates our proposed Sleep Node Algorithm (SNA), which turns the “FL + meta-learning” combination into a practical and efficient edge solution. Specifically, SNA dynamically deactivates “inertial” (insensitive) network components during adaptation: it provides a structural regularizer that stabilizes few-shot updates and mitigates overfitting under concept drift, while inducing sparsity that reduces both on-device computation and cloud-edge communication. To efficiently leverage these unstructured zero nodes introduced by SNA, we further design a dedicated accelerator, EPAST (Energy-efficient Pipelined Accelerator for Sparse Training). EPAST adopts a heterogeneous architecture and introduces a dedicated Backward Pipeline (BPIP) dataflow that overlaps backpropagation stages, thereby improving hardware utilization under irregular sparse workloads. Experimental results demonstrate that our system consistently outperforms strong baselines, including DQN, GARCH-XGBoost, and LRU, in terms of Pearson IC. A 55 nm CMOS ASIC implementation further validates robust learning under an extreme 5-shot setting (IC = 0.1176), achieving an end-to-end training speed-up of $11.35\times$ and an energy efficiency of 45.78 TOPS/W. Full article

β-agonists are prohibited antibiotics that have raised concerns due to their illegal use in the livestock industry, posing potential toxicity risks to human health. For ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis of β-agonists, effective sample pretreatment is a crucial and challenging process that dictates the overall reliability and sensitivity of the method. Thus, this study developed a reliable method utilizing dispersive solid-phase extraction (d-SPE) with NH₂-UiO-66 as a superior adsorbent, coupled with UPLC-MS/MS, to extract and quantify β-agonists in environmental water, swine urine, and milk. The synthesized NH₂-UiO-66 exhibited outstanding adsorption capacities (146.06–358.00 mg/g) towards the target analytes. The optimized method demonstrated excellent performance: low matrix effects (−13.10–15.30%), wide linearity (0.1–50 μg/L), low limits of detection (0.04–0.09 μg/L), and satisfactory recoveries (81.48–106.67%) with good precision (intra-day RSDs 1.51–6.24%; inter-day RSDs 2.06–10.96%). Adsorption mechanism studies revealed that the extraction process, which followed the Langmuir isotherm and pseudo-second-order kinetic models, was driven primarily by electrostatic interactions, π-π stacking, and hydrogen bonding. Moreover, the material could be reused up to 10 times, with satisfactory recoveries of 81.30% to 116.10%. The proposed NH₂-UiO-66-d-SPE-UPLC-MS/MS protocol is generic and provides a robust and practical solution for monitoring trace β-agonists in animal-derived foods and environmental samples, ensuring food safety and environmental health. Full article