Search Results (336)

This study examines the dynamic interactions between precious metals (gold and silver) and cryptocurrencies (Bitcoin) in the context of banking systemic risk, by identifying the main banking factors of systemic vulnerability. The MES method was employed to estimate systemic risk, the DCC-GARCH model to assess dependency dynamics, and the VAR model to investigate causal relationships and impulse response functions. Empirical evidence shows that banking systemic risk has a unidirectional influence on gold and silver prices, reinforcing their role as safe-haven assets in times of financial stress. However, Bitcoin’s volatility dynamics amplify banking systemic risk, indicating that fluctuations in cryptocurrencies can increase financial uncertainty and affect the stability of the banking system. The findings of this study have several important implications for systemic risk management, portfolio diversification, and the assessment of the significant role of alternative assets in financial stability. Full article

This paper examines whether research-space analysis can support portfolio design informed by forecasts at the country–subfield level. Using a bibliographic catalog of scientific papers, authors and institutions, we construct a research space from country activity across subfields and test whether measures derived from that space help predict later changes in specialization. The study contributes an empirical framework that links three elements: historical validation of research-space measures, comparison of alternative specifications of relative relatedness, and portfolio selection under an explicit complexity target. We estimate reduced-form stepping-stone models for entry and exit and evaluate them using in-sample evidence, leave-one-year-out checks, and robustness comparisons across relatedness constructions. The results show that research-space measures help predict later specialization changes and that relative relatedness standardized over the opportunity set provides the most consistent specification, especially for entry into previously non-specialized subfields. Using this specification, we formulate a prospective portfolio problem in which candidate subfields are selected to increase portfolio complexity while minimizing predicted entry effort. In a 2024 comparison covering 164 countries, optimized portfolios differ markedly from relatedness rankings, with 92.7% of countries shifting toward lower auxiliary volume and higher predicted future sophistication. Full article

Periods of geopolitical uncertainty have increasingly shaped the performance of global financial markets, yet the extent to which these risks influence the co-movement of asset markets in South Africa remains unclear. Although co-movement has emerged as a crucial factor for investors seeking portfolio diversification, existing studies present mixed findings, with some suggesting that geopolitical risk strengthens financial integration, defined as the extent to which markets move together in response to global shocks, while others find that it weakens these linkages by triggering market segmentation. Against this backdrop, this study examines the impact of geopolitical risk’s influence on the co-movement of South African asset markets, focusing on how shifts in global uncertainty interact with local market dynamics. Using time-series monthly data from December 2004 to January 2025, the study applies a dual-method approach. The multivariate generalised autoregressive conditional heteroskedasticity asymmetric dynamic conditional correlation (MGARCH-ADCC) model is first employed to estimate time-varying correlations across the equity, bond, and property markets. Thereafter, the autoregressive distributed lag (ARDL) model is used to assess both the short- and long-run effects of geopolitical risk on these co-movement patterns. The results indicate that geopolitical risk significantly increases co-movement between South African asset markets in both the short and long run, thereby diminishing the traditional benefits of diversification. These findings reinforce the view that market participants respond collectively to uncertainty rather than fundamentals. Overall, the study contributes to the empirical understanding of market integration under geopolitical stress and highlights the need for investors and policymakers to incorporate geopolitical risk indicators into investment and policy frameworks to strengthen market resilience. Full article

Despite the growing importance of the Saudi capital market, sectoral-level volatility connectedness within Tadawul remains largely unexplored. This study contributes to the literature by applying the Diebold–Yılmaz framework to examine volatility connectedness across 16 Tadawul sectors over the period January 2017 to December 2024. Total, directional, and net pairwise volatility spillovers are quantified from daily closing prices using a VAR(4) model combined with generalized forecast error variance decomposition. The static analysis reveals a high overall connectedness of 80.49%, indicating that cross-sectoral spillovers account for the majority of volatility fluctuations. Materials, Transportation, and Real Estate Management and Development are identified as the dominant net transmitters of volatility, while Utilities and Telecommunication Services are persistent net receivers. The dynamic analysis shows that sectoral connectedness is highly time-varying, peaking at 93.70% during the COVID-19 period, with additional episodes of elevated spillovers during 2022–2023. The network analysis reveals that the strongest pairwise linkages exist among Materials, Transportation, Real Estate Management and Development, and Banks, forming the core of the spillover network. While block-bootstrap results reinforce the identification of dominant net transmitters and receivers, they reveal substantial uncertainty in the rank-order of intermediate sectors, necessitating a more nuanced interpretation. The results are robust to alternative rolling window sizes and forecast horizons. These findings have important implications for portfolio diversification, sectoral risk monitoring, and macroprudential policy in the Saudi capital market. Full article

Non-timber forest products (NTFPs) constitute an important component of forest-based production systems and biomass supply chains in Türkiye. Despite their growing economic and ecological significance, the long-term structural dynamics of NTFP production remain insufficiently understood. This study examines temporal and structural changes in NTFP production in Türkiye during the period 1988–2024 using official production statistics and production support data. The analysis applies a quantitative framework that combines linear trend analysis, Shannon diversity and Herfindahl–Hirschman concentration indices, volatility measures based on the coefficient of variation, and regression models to evaluate production trends, structural transformations, stabilization patterns, and the effectiveness of production support mechanisms. The findings reveal a non-linear and multi-phase development pattern characterized by diversification and production growth after 2000, followed by increasing concentration and greater production volatility after 2018. Although total production volume increased substantially, portfolio diversity declined over time, and dependence on a limited number of high-volume products intensified, indicating growing structural vulnerability within the system. In addition, production support mechanisms showed a weak and heterogeneous relationship with production outcomes. A limited contextual comparison with Lithuania’s multifunctional NTFP system is also included to position the findings within a broader European context. Overall, the results suggest that increasing production alone is insufficient to ensure long-term system stability. Instead, diversification-oriented and risk-sensitive resource management strategies that account for production risks, regional disparities, and product heterogeneity are essential for developing sustainable and resilient NTFP production systems. Full article

The rapid expansion of clean energy markets, coupled with the growing importance of non-ferrous metals and technological innovation, has created a highly interconnected financial and economic system. Understanding the dynamics of these interdependencies is essential for assessing market resilience, investment diversification, and the sustainability of the global energy transition. This paper investigates the dynamic dependence and connectedness between clean energy, non-ferrous metals, and technological innovation indices, with particular attention to the impact of the COVID-19 pandemic as a global stress event. Using daily data from December 2004 to July 2020, we employ a comprehensive empirical framework that combines copula-based dependence modeling with a dynamic connectedness approach. This methodology allows us to capture nonlinear relationships, tail dependencies, and volatility spillovers across markets. The results reveal that the dependence structure between clean energy and the other sectors is symmetric and time-varying, with stronger linkages observed between clean energy and technological innovation than with non-ferrous metals. The connectedness analysis indicates a moderate level of total spillovers, with clean energy acting as the main transmitter of shocks and technological innovation as the primary receiver. Focusing on the COVID-19 period, we find a significant increase in both dependence and connectedness, suggesting that these markets become more severely integrated during periods of extreme uncertainty. These findings support the presence of contagion effects and highlight the reduced effectiveness of diversification strategies during crisis episodes. The results offer forward-looking implications for investors and policymakers regarding risk transmission, portfolio management, and the resilience of markets supporting the global transition toward sustainable energy. Full article

Construction firms in emerging markets operate in highly volatile environments that threaten business continuity and sector-wide resilience. This study provides a novel, integrated framework that links multiple strategic survival models to construction business resilience and development in Ghana’s construction industry, with particular emphasis on the evolving role of digitalisation. Four survival models are conceptualised as strategic portfolios: Innovation and Digital Transformation, Diversification and Growth, Lean and Resilience, and Strategic Risk and Partnerships. A quantitative research design was employed, using structured questionnaires administered to 128 construction industry stakeholders. Data were analysed using Partial Least Squares Structural Equation Modelling to assess direct, indirect, and mediating effects among survival models, construction business resilience, and construction business development. All four survival models have significant positive effects on construction business resilience, with Diversification and Growth (β = 0.404) and Innovation and Digital Transformation (β = 0.377) exerting the strongest influence, followed by Strategic Risk and Partnerships (β = 0.265) and Lean and Resilience (β = 0.207). The structural model explains 55.7% of the variance in construction business resilience, while construction business resilience is positively and strongly related to construction business development (β = 0.439), accounting for 19.3% of its variance. The findings show, for the first time in this context, that construction business resilience systematically mediates the relationship between distinct strategic survival portfolios and business growth in an emerging-market construction sector. This study advances the resilience and construction management literature by empirically demonstrating the hierarchical effectiveness of different survival models and by positioning construction business resilience as both a defensive capability and a strategic engine of sustainable development for construction firms in volatile markets. This paper recommends that firms develop composite resilience portfolios that integrate these strategies, while policymakers foster enabling regulations, digitalisation incentives, and joint risk-sharing arrangements that amplify sector-wide resilience. It offers a portfolio-based perspective on how to combine diversification, digital transformation, lean management, and strategic partnerships to build resilient, growth-oriented construction businesses. Convenience sampling and a cross-sectional design in a single national context highlight the need for longitudinal and cross-country research to validate and extend the proposed framework. Full article

This study examines the network dynamics and shock transmission in the relationship between BRICS property market returns and geopolitical risk indicators, applying a time-varying parameter vector autoregression (TVP-VAR) method. The goal of this study is to investigate the dynamic connectedness and shock transmission between geopolitical risk and property returns in BRICS countries, with further insight into how geopolitical events lead to risk transmission. Using monthly data from February 2011 through June 2025 and isolating two tension periods after COVID-19, 2022 and 2024, we investigate geopolitical events and their shock transmissions. The findings illustrates the complexity of shifting geopolitical tensions and their effects on cross-market spillovers. That being, there exists moderate but economically significant systemic interconnectedness, with approximately half of the forecast error variance explained by cross-market shocks. This study further provides robust empirical evidence on the direct effects of geopolitical risk on BRICS property markets and their dynamic interconnectedness. Geopolitical risk especially originating from Russia and China, is found to be the key net transmitter of shocks to the region, whereas Brazil, India, and South Africa are the main net receivers. The results add to the evidence of regime-dependent spillovers, magnified by major geopolitical episodes such as the Russia–Ukraine war and the 2024 expansion of BRICS. Property markets are more vulnerable to geopolitical instability, showing their susceptibility to external risk spread. This study has implications for the sustainability and financial stability literature by emphasising the systemic nature of geopolitical risk in property markets, and it provides practical guidance for portfolio diversification, risk management and policy coordination in the BRICS bloc. Full article

Bond ETF rebalancing is difficult to describe with return and risk objectives alone, because a portfolio that looks attractive on paper may still be impractical if it requires large and unstable trades. This paper proposes a topology-aware multi-objective particle swarm optimization framework for bond ETF allocation under credit-risk-related constraints. The method jointly considers annualized return, CVaR, and diversification, while enforcing long-only, exposure, and hard maximum-step turnover constraints. The central idea is to treat the swarm as a communication graph: particles exchange information through an explicit topology, and this topology affects how feasible regions are explored and how leaders are selected. When a candidate portfolio update violates the turnover budget, it is repaired toward the feasible set before evaluation, so that the search remains tied to tradable rebalancing decisions. We test the framework in a walk-forward out-of-sample backtest on U.S. bond ETFs from 2008 to 2024. The empirical analysis compares stronger classical and evolutionary baselines, four communication topologies, hard-versus-soft turnover control, stress-period behavior, and a synthetic scalability proxy. The results suggest that hard turnover repair is effective in truncating extreme rebalancing events, while communication topology changes the return–risk–turnover profile. In our experiments, the ring topology gives the most stable default behavior. Overall, the evidence suggests that topology is not just an implementation detail in swarm-based portfolio search, but a design choice that affects constrained multi-objective allocation. Full article

An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have proposed models for forecasting and portfolio optimization, most rely mainly on traditional techniques and metaheuristic approaches. This work introduces ESIPO (Ensemble Strategies for Investment Portfolio Optimization), a methodology that integrates deep learning and metaheuristic algorithms to perform asset forecasting and investment portfolio optimization. The dataset is obtained from the S&P 500 index, one of the main stock markets. To enhance forecasting accuracy, ESIPO combines five methods from the top-performing models of the international M4 competition: (a) ARIMA (AutoRegressive Integrated Moving Average) and ETS (the statistical exponential-smoothing state-space), which represent classical statistical approaches; (b) FFORMA (Feature-based FORecast Model Averaging) and JAGANATHAN, two ensemble-based methods; (c) CNN (Convolutional Neural Network), which is one of the most common deep learning models. ESIPO improves the forecast performance of the portfolio by applying the TAIPO (Threshold Accepting Investment Portfolio Optimization) metaheuristic to select the best assets and optimize portfolio composition. The results obtained 45% of improvement according to the Sharpe Ratio metric. Full article

This study examines sustainability investments under stressful and constrained scenarios. The study exploits a wide range of indices, ranging from sustainability, ESG, to financial. The Diebold and Yilmaz framework and the DCC-GARCH were employed. The analysis covered 2382 observations with results capturing aggregate cross-market connectedness. The total volatility index, 76.95% based on static analysis, indicates the markets are highly integrated. The predominant net volatility transmitters are the SPX, TSX, EURUSA, and S&P500, while ESG50, ESG30, CAC, and Midi are net volatility receivers. The dynamics significantly vary, reaffirming the fact that the ESG50 and ESG30 are shock absorbers with an inverse behavioral pattern seen in the S&P500. Meaningful results based on the modern portfolio theory weights and DCC-GARCH hedge ratios based on the index pairs provide substantial diversification opportunities with hedge ratios ranging from 0.23 to 0.90. Robustness checks based on sensitivity checks with respect to varying rolling windows and lag specifications confirm the stability and validity of the findings. These empirical results are relevant for the establishment of sustainable portfolio construction and portfolio risk management in the global markets. Full article

Recent advances in large language models (LLMs) have raised questions about their potential role in portfolio allocation beyond traditional sentiment analyses. This study investigated whether LLMs, when prompted directly, can autonomously generate portfolio weights that compete with classical optimization and AI-enhanced strategies. We evaluated seven medium-sized open-source LLMs—Gemma-7B, Mistral-7B, Jansen Adapt-Finance-Llama2-7B, DeepSeek-R1-8B, QuantFactory Llama-3-8B-Instruct-Finance, Qwen-7B, and Llama2-7B—using systematic prompt engineering and temperature tuning. Portfolios were constructed from financial news headlines for S&P 500 equities and benchmarked against mean–variance optimization (MVO), the Black–Litterman model, AI-driven optimizers, and naive diversification strategies. The results show that, while LLM-generated portfolios outperformed naive diversification (Sharpe ratio up to 0.741), they lagged behind AI-optimized benchmarks (Sharpe ratio up to 1.361). A transaction cost analysis revealed that low-turnover LLM strategies retain their competitiveness post-costs, surpassing cap-weighted benchmarks. Statistical tests confirmed significant performance differences ($p\le 0.01$). These findings highlight the ability of LLMs to extract signals from unstructured text, but also their limitations without explicit optimization. Future research should explore hybrid frameworks that combine LLM reasoning with quantitative optimization for cost-sensitive environments. Full article

Tourism development in the Yucatán Peninsula has long been dominated by coastal mass tourism, resulting in environmental pressure and pronounced spatial imbalances. In response, increasing attention has been directed toward diversification strategies based on inland and nature-based attractions. Among these, cenotes—karst sinkholes connected to regional groundwater systems—have emerged as a distinctive tourism resource. This paper introduces the concept of cenotourism as a form of nature-based and geoculturally embedded tourism centred on cenotes and their associated karst environments. The analysis combines conceptual development with empirical evidence from a large-scale tourism survey conducted in Yucatán (n ≈ 2800). The findings suggest that cenotes constitute a meaningful component of tourists’ activity portfolios, with 24.6% of respondents declaring an intention to visit them. Cenotourism contributes to diversification and appears to support the redistribution of tourist flows toward inland areas, while simultaneously increasing exposure to highly sensitive groundwater systems. These results point to a clear sustainability trade-off, although its magnitude may vary depending on local governance conditions. While cenotourism may strengthen local economies and reduce pressure on coastal destinations, it also introduces risks related to groundwater contamination, cultural commodification, and uneven benefit distribution. Such outcomes depend strongly on governance conditions, including visitor management, environmental monitoring, and community participation. By conceptualizing cenotourism as an integrative framework linking tourism demand, environmental vulnerability, and governance processes, the study contributes to understanding tourism development in groundwater-dependent systems. The findings emphasize the need for context-specific management approaches and situate cenotourism within broader water-sensitive tourism planning. Full article

This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditions as compared with the normal and bearish market conditions. We employ the novel quantile vector auto-regression (QVAR)-based connectivity framework. Overall findings suggested that CPR and CTR transmitted greater shocks towards cryptocurrency classes during extremely high and lower quantiles as compared with the median quantile. This U-shaped and non-linear climate risks shock transmission indicates that Sharia-compliant, energy-related and gold-backed cryptocurrencies become more vulnerable during extreme market conditions (higher and lower quantiles) and may not consistently serve as reliable hedging or diversification instruments, particularly during periods of heightened climate uncertainty. Overall findings suggested that both the CPR and CTR transmitted greater shocks towards energy-related, gold-backed, and Sharia-compliant cryptocurrencies as compared with the sustainable cryptocurrencies, across all the quantiles. Therefore, sustainable cryptocurrencies, particularly those with energy-efficient consensus mechanisms such as Stellar, Cardano and Ripple, exhibited resilience to climate risks and can therefore function as stabilizing core holdings in diversified portfolios. Fund managers should incorporate a rebalancing strategy that increases allocation to these climate-resilient, sustainable digital assets during periods of elevated climate risk. Fund managers should integrate CPR and CTR into the quantile-domain forecasting frameworks for predicting digital asset market returns to enhance financial stability. Portfolio managers should undertake dynamic and quantile-contingent climate risk hedging strategies that account for tail-risk exposure rather than relying on average market behavior. Full article

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption. Full article