Search Results (181)

We compare three modern Bayesian approaches, Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and Integrated Nested Laplace Approximation (INLA), for two classic spatial econometric specifications: the spatial lag model and spatial error model. Our Monte Carlo experiments span a range of sample sizes and spatial neighborhood structures to assess accuracy and computational efficiency. Overall, posterior means exhibit minimal bias for most parameters, with precision improving as sample size grows. VB and INLA deliver substantial computational gains over HMC, with VB typically fastest at small and moderate samples and INLA showing excellent scalability at larger samples. However, INLA can be sensitive to dense spatial weight matrices, showing elevated bias and error dispersion for variance and some regression parameters. Two empirical illustrations underscore these findings: a municipal expenditure reaction function for Île-de-France and a hedonic price for housing in Ames, Iowa. Our results yield actionable guidance. HMC remains a gold standard for accuracy when computation permits; VB is a strong, scalable default; and INLA is attractive for large samples provided the weight matrix is not overly dense. These insights help practitioners select Bayesian tools aligned with data size, spatial neighborhood structure, and time constraints. Full article

Time series models are considered among the most intricate models in machine learning. Due to sharp temporal variations, time series models normally fall short in predicting the peaks or local minima accurately. To overcome this challenge, we proposed a novel custom loss function, Enhanced Peak (EP) loss, specifically designed to pinpoint peaks and troughs in time series models, to address underestimations and overestimations in the forecasting process. EP loss applies an adaptive penalty when prediction errors exceed a specified threshold, encouraging the model to focus more effectively on these regions. To evaluate the effectiveness and versatility of EP loss, the loss function was tested on three highly variable datasets: NO_x emissions, streamflow measurements, and gold price, implementing Gated Recurrent Unit and Transformer-based models. The results consistently demonstrated that EP loss significantly mitigates peak prediction errors compared to conventional loss functions, highlighting its potential for highly variable time series applications. Full article

Chaos reveals a fundamental paradox in the scientific understanding of Complex Systems. Although chaotic models may be mathematically deterministic, they are practically non-determinable due to the finite precision that is inherent in all computational machines. Beyond the horizon of predictability, numerical computations accumulate errors, often undetectable. We investigate the possibility of reliable (error-free) time series of chaos. We prove that this is feasible for two well-studied isomorphic chaotic maps, namely the Tent map and the Logistic map. The generated chaotic time series have an unlimited horizon of predictability. A new linear formula for the horizon of predictability of the Analytic Computation of the Logistic map, for any given precision and acceptable error, is obtained. Reliable (error-free) time series of chaos serve as the “gold standard” for chaos applications. The practical significance of our findings include: (i) the ability to compare the performance of neural networks that predict chaotic time series; (ii) the reliability and numerical accuracy of chaotic orbit computations in encryption, maintaining high cryptographic strength; and (iii) the reliable forecasting of future prices in chaotic economic and financial models. Full article

This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques. Full article

In recent years, the correlation mechanisms between geopolitical risks and financial markets have drawn considerable attention from both academic circles and investment communities. However, their multiscale, nonlinear interactive characteristics still require further investigation. To address this, this paper proposes a dynamic nonlinear causal information network combined with a wavelet transform model and the transfer entropy method. We select the geopolitical risk index, the US dollar index, Brent and WTI crude oil prices, COMEX gold futures, and London gold prices time series as the research objects. The results suggest that the network’s structure changes with time at different time scales. On the one hand, COMEX gold (London gold) acts as the major causal information transmitter (receiver) at all scales; both of their highest values appear at the mid-scale. The US dollar index plays a bridging role in information transmission, and this mediating ability decreases with increasing time scales. On the other hand, the fastest speed of causal information transmission is at the short scale, and the slowest speed is at the mid-scale. The complexity and systematic risk of causal network decrease with increasing time scales. Importantly, at the short-scale (D1), the information transmission speed slowed during the Russian–Ukrainian conflict and further decreased after the start of the Israel–Hamas conflict. Systematic risk has increased annually since 2018. This study provides a multiscale perspective to study the nonlinear causal relationship between geopolitical risk and financial markets and serves as a reference for policy-makers and investors. Full article

This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAI—against a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance. Full article

This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series models often fail to capture the complex, non-linear dynamics of financial markets, whereas technical indicators combined with machine learning enhance predictive accuracy. Using daily gold prices from January–October 2020, the PSO-ELM model demonstrated superior performance in filtering false signals, achieving high precision, recall, and overall accuracy. The results highlight the effectiveness of combining technical analysis with machine learning for robust signal validation, providing a practical framework for traders and investors. While focused on gold, this methodology can be extended to other financial assets and market conditions. The integration of machine learning and blockchain enhances both predictive reliability and operational trust, offering traders, investors, and institutions a robust framework for decision support in dynamic financial environments. Full article

The potential for hydrogen production from heavy oil reservoirs has gained significant attention as a dual-benefit process for both enhanced oil recovery and low-carbon energy generation. This study investigates the technical and economic feasibility of producing hydrogen from heavy oil reservoirs using two primary in situ combustion gasification strategies: cyclic steam/air and CO₂ + O₂ injection. Through a comprehensive analysis of technical barriers, economic drivers, and market conditions, we assess the hydrogen production potential of each method. While both strategies show promise, they face considerable challenges: the high energy demands associated with steam generation in the steam/air strategy, and the complexities of CO₂ procurement, capture, and storage in the CO₂ + O₂ method. The novelty of this work lies in combining CMG-STARS reservoir simulations with GoldSim techno-economic modeling to quantify hydrogen yields, production costs, and oil–hydrogen revenue trade-offs under realistic field conditions. The analysis reveals that under current technological and market conditions, the cost of hydrogen production significantly exceeds the market price, rendering the process economically uncompetitive. Furthermore, the dominance of oil production as the primary revenue source in both methods limits the economic viability of hydrogen production. Unless substantial advancements are made in technology or a more cost-efficient production strategy is developed, hydrogen production from heavy oil reservoirs is unlikely to become commercially viable in the near term. This study provides crucial insights into the challenges that must be addressed for hydrogen production from heavy oil reservoirs to be considered a competitive energy source. Full article

The Hurst parameter H plays a critical role in modeling long-memory behavior in financial time series, particularly within the framework of the fractional Black–Scholes model (fBSM). While the Method of Moments (MOM) provides a fast, closed-form estimator for H, it suffers from increasing negative bias, especially as H grows beyond 0.6. This paper proposes a bias-corrected version of the MOM estimator based on a quadratic regression fit derived from simulation data. The corrected estimator substantially reduces estimation error while retaining computational efficiency. Through extensive simulations, we quantify the impact of MOM bias on option pricing and demonstrate how our correction method leads to more accurate pricing under the fBSM. We apply the methodology to real financial assets—including Natural Gas, Apple, Gold, and Crude Oil—and show that the corrected Hurst estimates reduce option pricing error by up to USD 0.47 per contract relative to the uncorrected estimator, depending on the asset’s volatility structure. These results underscore the importance of accurate Hurst parameter estimation for derivative pricing, particularly in volatile markets such as energy and commodities, while also remaining relevant to equities and precious metals. The corrected estimator thus offers practitioners a simple yet effective tool to improve financial decision-making. Full article

We analyze extreme gold price movements between 1975 and 2025 using Extreme Value Theory (EVT). Using both the Block-Maxima and Peaks-over-Threshold approaches on a daily return basis, we estimate Value-at-Risk (VaR) and Expected Shortfall (ES) for the entire distribution focusing on a long-term view. Our results demonstrate that models based on the standard normal distribution systematically underestimate extreme risks, whereas EVT provides more reliable measures. In particular, EVT captures not only rare losses, but also sudden positive rallies, highlighting gold’s dual function as a risk and opportunity asset. Asymmetries emerge in the analysis: at the 0.99 quantile, losses appear larger in absolute value than gains. At the 0.995 quantile, in some episodes, upside extremes dominate. Furthermore, we find that geopolitical and economic shocks, including the oil crises, the 2008 financial crisis, and the COVD-19 pandemic, leave distinct signatures in the extremes. By covering five decades, our study provides the most extensive EVT-based assessment of gold risks to date. Our findings contribute to debates on financial stability and provide practical guidance for investors seeking to manage tail risks while recognizing gold’s potential as both a safe haven and a speculative asset. Full article

This study investigates the multifractal characteristics and nonlinear cross-correlations between two major carbon credit indices—S&P Global Carbon Index and EEX Global Carbon Index—and key global financial assets: the Euro/US Dollar exchange rate, Dow Jones Industrial Average, gold, Western Texas Intermediate, and Bitcoin. Using daily data from August 2020 to June 2025, we apply the Asymmetric Multifractal Detrended Cross-Correlation Analysis framework to examine the strength, asymmetry, and persistence of interdependencies across varying fluctuation magnitudes. Our findings reveal consistent multifractality in all asset pairs, with stronger multifractal spectra observed in those linked to Bitcoin and Western Texas Intermediate Crude Oil price. The analysis of generalized Hurst exponents indicates higher persistence for small fluctuations and antipersistent behavior for large fluctuations, particularly in pairs involving the S&P Global Carbon Index. We also detect significant asymmetry in the cross-correlations, especially under bearish trends in Bitcoin and Western Texas Intermediate. Surrogate data tests confirm that multifractality largely stems from fat-tailed distributions and temporal correlations, with genuine multifractality identified in the S&P Global Carbon Index–Dow Jones Industrial average pair. These results highlight the complex and nonlinear dynamics governing carbon markets, offering critical insights for investors, policymakers, and regulators navigating the intersection of environmental and financial systems. Full article

Background/Objectives: The rapid identification of carbapenemase genes directly from positive blood culture (BC) samples shortens the time needed to initiate optimal antimicrobial therapy for Carbapenemase-Producing Enterobacterales (CPE) infections. Several commercial automated PCR systems are available for detecting CPE resistance genes but are expensive. The Xpert^® Carba-R assay (Cepheid GeneXpert System) has high sensitivity and specificity for the detection of carbapenamase genes from bacterial colonies or rectal swabs, with an affordable price. This assay was not used for positive BC testing of CPE resistance genes. Whole-Genome Sequencing (WGS) for resistance genes can be used as the gold standard at a research level. In this study, we evaluated the performance of the Xpert^® Carba-R assay for the early detection of carbapenamase genes directly from positive BCs, using WGS as the gold standard. Methods: A prospective observational study was conducted at Children’s Cancer Hospital-Egypt (CCHE-57357). All positive BCs underwent direct gram staining and conventional cultures. A total of 590 positive BCs containing Gram-negative rods (GNRs) were identified. The Xpert^® Carba-R assay was used to detect carbapenemase genes directly from the positive BC bottle compared with WGS results. Results: Among the 590 GNR specimens, 178 were found to carry carbapenemase genes using the Xpert^® Carba-R assay, with results obtained in approximately one hour. The main genotypes detected were bla_NDM, bla_OXA-48-like, and dual bla_NDM/bla_OXA-48-like at 27%, 29%, and 33%, respectively. The agreement between Xpert^® Carba-R assay and WGS results was almost perfect for the genotype resistance pattern of isolates and individual gene detection. Conclusions: The use of the Xpert^® Carba-R assay directly from BC bottles was an easy-to-use, time-saving, affordable tool with high accuracy in identifying carbapenemase genes and, thus, shortens the time needed to initiate optimal antimicrobial therapy for CPE infections. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article

This study examined the factor structure of Green, Grey, and Red EU securities using extended asset pricing models built on the Fama–French and Carhart frameworks. The findings show improved return predictability and consistently negative risk-adjusted alpha across categories post-Global Financial Crisis (GFC), suggesting systematic overestimation of expected returns. All environmental asset types are positively linked to the MKTRF, SMB, HML, and HML^Devil factors, indicating exposure to core risk premia. Green securities exhibit elevated currency risk and persistent negative momentum, while Red assets transition from positive to negative momentum. Green and Red securities show stronger gold associations post-GFC, signaling a hedging role. Grey assets shift away from safe-haven behavior, becoming more sensitive to volatility. FEAR factor exposure and QML results suggest evolving sensitivity and declining quality, particularly in Grey assets. These findings underscore the need for enriched asset pricing models to capture dynamic risk characteristics in environmental assets within the EU financial markets. Full article

The effectiveness of fiscal and monetary policy in sustaining growth and facilitating recovery from economic crises is increasingly considered to be significantly influenced by the quality of a country’s institutions. Strong institutions may determine how well macroeconomic policies perform under both stable and turbulent circumstances. This study examines how institutional quality (IQ) moderates the effects of fiscal and monetary policies on economic growth in Thailand from Q1:2003 to Q4:2023. Using a combination of BART and BASAD models, we find that voice and accountability and control of corruption are key institutional factors. Among macroeconomic indicators, exports, household debt, gold prices, and electricity generation emerge as the most important drivers of growth during the study period. The findings showed that IQ stabilizes and enhances the impact of policy interest rates and export growth while mitigating negative shocks from household debt and energy infrastructure challenges. Monetary policy effectiveness varies and depends on governmental institutions. Fiscal policy remains mostly neutral but shifts with institutional conditions. These results highlight that strong institutions improve the efficacy of macroeconomic policies and support sustainable growth. This study empirically examines the moderating role of IQ in economic resilience and policy design in an emerging economy using microdata from Thailand as a focus and the Time-varying Seemingly Unrelated Regression Equation (tvSURE) model. Full article