Search Results (332)

We consider the problem of Bayesian parameter inference in the Merton structural credit risk model, where the posterior is induced by a jump-diffusion likelihood and the marginal evidence is not available in closed form. To approximate this posterior, we construct a variational family based on triangular sum-of-squares (SOS) polynomial flows, in which each component map is monotone by construction: its diagonal derivative is a positive definite quadratic form on a monomial basis, yielding a closed-form log-Jacobian and explicit gradients with respect to all flow parameters. The symmetric positive definite matrices parametrizing the flow are optimized by intrinsic Riemannian gradient ascent on the positive definite cone equipped with the affine-invariant metric, which preserves feasibility at every iterate without projection. We show that the rank-one Jacobian gradients produced by the SOS structure have unit norm in the affine-invariant metric, establishing a direct algebraic coupling between the transport family and the optimization geometry and implying a universal 1-Lipschitz bound for the log-Jacobian along geodesics. On the likelihood side, we derive exact score identities for all five structural parameters of the Merton model—drift, volatility, jump intensity, jump mean, and jump volatility—through both the Poisson log-normal mixture and the Fourier inversion representations. Strictly positive parameters are handled via exponential reparametrization, and the resulting gradients propagate end-to-end through the flow. We establish uniform truncation bounds on compact parameter sets for the infinite mixture and its associated score series, providing rigorous control over the finite approximations used in practice. The base distribution is chosen to be uniform on ${[0,1]}^5$, whose bounded support ensures uniform control of the monomial basis and stabilizes the polynomial calculus. These ingredients are assembled into a fully explicit modified ELBO with implementable gradients, combining Euclidean updates for vector parameters and intrinsic manifold updates for matrix parameters. Full article

This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR₁%) and 67.5% (ETH VaR₁%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR₁% fell by 22.0% and ETH VaR₁% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ⁻ = 0.847 vs. ρ⁺ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ⁺ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets. Full article

Optimal dividend policy remains a key topic of debate in corporate finance, particularly in emerging markets where investor preferences and macroeconomic volatility affect decision making. This study therefore examines the relationship between dividend policy and the Johannesburg Stock Exchange (JSE) market index over the period 2000 to 2020. The study uses firm-level dividend data to construct a market-capitalization-weighted aggregate dividend index. The paper further employs an Autoregressive Distributed Lag (ARDL) and error correction model to assess the long-run equilibrium relationship and short-run adjustments. The results show evidence of a long-run relationship between dividends and the JSE index price. In the short run, dividend payments exhibit negative effect on index prices while lagged dividends have a significant positive effect on index implying delayed market response. These findings suggest that South African investors place more confidence and emphasis on capital gains rather than dividend distributions. This study contributes evidence on the aggregate dividend dynamics within the context of an emerging market and offers practical insights for managers, investors and policy makers. Full article

We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on ${(0,1)}^2$. To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. Full article

Exogenous shocks can affect equity markets by changing volatility and cross-market co-movement. This study examines how two U.S.-centred events, treated as different shock types, influence time-varying conditional correlations between the U.S. stock market and other G7 markets. The uncertainty shock is proxied by the U.S. presidential election of 5 November 2024, while the action shock is proxied by President Trump’s 2 April 2025 announcement of reciprocal tariffs. Using daily log returns for the S&P 500 and leading indices for Canada, France, Germany, Italy, Japan and the United Kingdom, we cover January 2010 to July 2025 and assess event effects using correlation paths for June 2024–June 2025 and symmetric ±30-day windows. We employ a DCC-GARCH model to jointly estimate conditional variances and dynamic correlations for six USA-G7 pairs. The results indicate persistent correlation dynamics, with Canada/USA the highest and Japan/USA the lowest. Election-related uncertainty is associated with declines in correlation for European pairs, suggesting temporary decoupling, while Canada and Japan show only small changes. By contrast, the tariff action shock significantly increases conditional correlations across all country/USA pairs, implying stronger market synchronisation, with the largest increases in North America and parts of Europe, and the smallest adjustment in Japan. Full article

This study examines the dynamic interactions between oil price shocks, monetary policy, and non-oil output in Saudi Arabia using Vector Autoregressive Model (VAR), and quarterly data spanning 2010: Q1–2025: Q3. The study aims to provide policy-relevant insights through which external oil price shocks and domestic monetary policy shocks affect inflation and non-oil economic activity in the context of Saudi Arabia’s structural transformation under Vision 2030. The results show that global oil prices behave largely as exogenous shocks, with limited feedback from domestic monetary conditions, implying that monetary policy effectiveness operates primarily through inflation and domestic demand channels rather than through oil prices directly. The findings underscore the importance of gradual and predictable monetary tightening, coordinated with fiscal and macroprudential policies, to mitigate the indirect spillovers of oil price volatility on the non-oil sector. While monetary policy plays a stabilizing role by containing inflation and supporting macroeconomic balance, sustaining diversification and non-oil growth under Vision 2030 requires complementary measures, including targeted credit support, financial market deepening, and structural reforms that enhance productivity and private-sector investment. Full article

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. Full article

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to 2023, we apply several established statistical techniques—including unit root tests, variance ratio analysis, Hurst exponent estimation, and GARCH modeling—to quantify the presence and strength of mean reversion in volatility. To assess the accuracy and practical usability of volatility metrics for option valuation, we compare realized volatility, GARCH-based forecasts, range-based estimators, and widely used implied volatility measures such as the VIX and daily implied volatility averages, benchmarking each against contract-specific implied volatility. The results indicate that more than 65% of the analyzed tickers exhibit statistically significant mean-reverting behavior, and that the 30-day average implied volatility consistently provides the most reliable predictive performance among the tested metrics, while range-based estimators perform poorly when applied to end-of-day data. Finally, backtests of six delta-neutral option strategies informed by these findings did not yield consistent profitability or statistically significant outperformance, suggesting that although volatility mean reversion is measurable, its direct application to systematic trading remains challenging. Full article

This study examined the ‘too central to fail’ concept in the South African equity sector. We employed the Granger causality framework and PageRank algorithm to generate the centrality scores of the sectors on the Johannesburg Stock Exchange under extreme market conditions. Using the realized volatilities of sectoral returns for the full sample period (3 January 2006–31 December 2021), as well as during the global financial crisis (GFC), European debt crisis (EDC), COVID-19 pandemic, and US–China trade war sub-periods, we analyzed the sectors’ interconnections and calculated each sector’s centrality score across the entire sample and under different extreme market conditions. This allowed us to rank sectors relative to their centrality scores. The results indicate that, in the full sample, the insurance sector has the highest PageRank centrality score, suggesting it is too central to fail. This implies that the insurance sector acts as a systemic receiver of risks and provides stability within the network of sectors. However, the sub-period analyses reveal that General Industrial and Automobiles emerged as the key sectors with the highest PageRank centrality scores, and shocks from other sectors can disproportionately affect these industries during crisis periods. Underperformance in these sectors could have destabilizing effects on the South African economy. The findings have significant implications for regulators and policymakers, portfolio and fund managers, local and international investors, and researchers in the field of finance. Full article

The origin of Mesozoic granites associated with the Dahutang W-Cu-Mo orefield in northern Jiangxi, which hosts the world’s second-largest tungsten deposit, remains a compelling subject despite extensive geochemical and geochronological studies. In this contribution, we present wolframite mineral and whole-rock geochemistry, as well as monazite and zircon U-Pb ages, for the Mesozoic granites to constrain our understanding of the petrogenesis of these granites and their coupling relationship with the mineralization. The following two magmatic phases and four types of rocks in the study area are identified: the early stage (152–147 Ma) biotite (G1) granites and the late stage (144–130 Ma) two-mica (G2),muscovite (G3), and albite (G4) granite series. These two magmatic phases are temporally coincident with two mineralization stages (~150 Ma and 144–139 Ma). All the Mesozoic granites share the characteristics of high silica content, peraluminosity (A/CNK > 1.1), and low Zr + Nb + Ce + Y values (<200 ppm); they are derived from the partial melting of a Proterozoic crustal source and classified as S-type granites. Specifically, the G1 granites are characterized by relatively high MgO (~0.5%), CaO (~1%), and low P₂O₅ (0.13%–0.20%). They formed through a relatively high degree of partial melting at approximately 766 °C (zircon saturation temperatures), a process influenced by biotite dehydration reactions, with minor contributions from mantle-derived materials. In contrast, the G2–G4 granite series exhibits more typical peraluminous S-type granite features, such as high Al₂O₃, Na₂O, and P₂O₅ (mostly > 0.2%) contents, and low Sr and Ba contents. They are products of low-degree partial melting that occurred under conditions close to muscovite breakdown at ~726 °C. Additionally, fluid–melt interaction is recorded in both granites by distinctive geochemical signatures, including enrichment in Sn (>30 ppm), Cs (>35 ppm), Li (>250 ppm), F (>0.4%), and W (10–1000 ppm), coupled with low K/Rb (<150) and Nb/Ta (<5) ratios. The near-chondritic Zr/Hf (22.6–34.1) and Y/Ho (24.5–31.5) ratios of the G1 granites imply a relatively limited role of magmatic fluid–melt interaction during its evolution. For the G2–G4 granites, however, intense crystal fractionation and late-stage fluid–melt interaction are well-documented by their highly variable and low ratios of Y/Ho (14.8–41.4), Nb/Ta (0.89–5.57), Zr/Hf (8.84–41.67), and K/Rb (13.96–128.29). In the long-lived, reduced, and volatile-rich aqueous environment of the G2–G4 magmas, fractional crystallization and albitization collectively enhanced the solubility and hydrothermal transport capacity of W, Sn, Li, Nb, and Ta by multiple orders of magnitude. In contrast, in the earlier, more oxidized G1 magmas (which incorporated mantle materials), the exsolution and hydrothermal transport of Cu and Mo were associated with localized greisenization, but their capacity diminished with fractional crystallization. Historically, mineral exploration in the Dahutang mining area has focused primarily on W, Cu, and Mo. Based on this research, we conclude that there is significant mineral potential for rare metals (particularly Sn, Li, and Ta), and future exploration should prioritize areas adjacent to the evolved G2–G4 peraluminous leucogranites to search for new concealed mineral occurrences. Full article

This paper examines tail risk spillovers and cross-market volatility forecasting between the U.S. equity market and the crude oil market. Using realized and implied volatility within a heterogeneous autoregressive (HAR) framework, we document asymmetric and time-varying tail risk transmission across the two markets. Motivated by these findings, we propose several cross-market volatility forecasting strategies, including direct information augmentation, threshold-based designs, forecast averaging, and transfer learning. The results show that incorporating cross-market information improves volatility forecasts primarily at medium and longer horizons, consistent with the forward-looking nature of implied volatility. Moreover, the relative effectiveness of different transmission mechanisms varies across markets, with transfer learning performing particularly well in the crude oil market. Overall, the findings highlight the importance of linking tail risk spillovers to volatility forecasting and demonstrate that flexible cross-market information transmission can enhance predictive performance across markets and horizons. Full article

In an increasingly volatile global financial environment, strong institutions and sound governance are essential for safeguarding banking stability and mitigating systemic risks in emerging economies. Across the 11 emerging economies examined, weaknesses in institutional quality and inconsistencies in governance frameworks continue to elevate credit risk and undermine financial resilience. This study investigates the effects of institutional quality (IQ) and corporate governance (CGG) on bank stability, drawing on the Financial Stability and Risk Management (FSRM) theory, which highlights robust institutions, effective risk oversight, and sound governance as core determinants of financial system strength. Using dynamic panel data from 2011–2024, the study applies the generalized method of moments (GMM) approach to assess bank performance through non-performing loans (NPLs) and Z-Score as key dependent variables. The model incorporates IQ, CGG, bank-specific characteristics (bank assets, capital adequacy, cost-to-income ratio), and macroeconomic indicators (GDP, inflation, exchange rate, real interest rate) as explanatory factors, addressing endogeneity, unobserved heterogeneity, and persistence in banking outcomes. The results reveal strong persistence in NPLs (lag = 0.965, p < 0.01) and Z-Score (lag = 0.920, p < 0.01), indicating notable path dependence in bank performance. Institutional quality significantly enhances bank stability (Z-Score coefficient = 0.073, p = 0.040), while BA shows a negative but insignificant effect (coefficient = 0.005, p = 0.432), implying that rapid asset growth without prudent risk management may weaken resilience. CGG shows negative but insignificant effects, while macroeconomic factors also appear insignificant, indicating limited short-term impact. Countries with stronger institutions, such as South Korea, display lower NPLs and higher stability, whereas weaker institutional environments like Iran, Pakistan, and Bangladesh face higher credit risk and reduced stability. Overall, the study highlights IQ and prudent balance sheet management as key to stronger bank stability, urging policymakers to reinforce institutional frameworks, tighten regulatory discipline, and ensure controlled asset growth to reduce systemic vulnerabilities. Full article

This paper investigates the nonlinear and state-dependent relationship between inflation surprises and real equity returns across G7 economies. Using monthly data from January 1998 to May 2025, we employ nonlinear local projection models to estimate the dynamic responses of the equity market to domestic inflation shocks. While linear estimates reveal modest but persistent average losses, once regime dependence and sign asymmetry are jointly considered, three critical findings emerge. First, equity responses are strongly regime-dependent: inflation shocks occurring in high-inflation environments produce losses two to four times larger than those in low-inflation regimes. Second, the direction of the shock matters: positive inflation surprises are associated with deeper and longer-lasting equity declines than the gains generated by negative surprises. Third, these effects exhibit pronounced cross-country heterogeneity, with distinct vulnerability profiles that remain invisible in linear or pooled models. To systematically assess these differences, we develop a Sensitivity–Volatility–Vulnerability (SVV) assessment that synthesizes regime-dependent and sign-asymmetric responses into market vulnerability profiles. Our results underscore that inflation risk in equity markets is not only nonlinear and regime-dependent but also fundamentally country-specific, implying that conventional linear models materially understate downside equity exposure. These findings carry important implications for monetary policy, financial regulation, and international portfolio diversification. Full article

In the absence of ex-post empirical evidence on the implementation effects of the EU-Mercosur agreement, assessments of expected risks and benefits for the agri-food sector must rely on ex-ante reasoning rather than statistical identification. This paper develops a deductive ex-ante framework to assess how partial market integration under EU–Mercosur may affect the prices and profitability of two groups: agri-food processors and agricultural producers. Methodologically, we formalize a two-market setting (final food products and agricultural raw materials) and derive comparative-statics implications for microeconomic profitability indicators that guide agents’ choices. The main propositions are as follows. First, the integration of the sourcing base for processors is likely to increase the relative profitability of processing by improving the ratio of output to raw-material inputs and, crucially, by widening the price wedge between final food prices and agricultural input prices. Second, the same mechanism implies that agricultural producers in the EU face greater downside risk, as increased competition on the raw-material market tends to depress farm-gate prices; the resulting revenue effect is unlikely to be fully offset by higher sales volumes in the short run. Third, these asymmetric effects rationalize the divergence of perceived risks and benefits across processors and farmers, even when both operate within the same integrated market environment. In addition, we highlight a complementary risk channel: market integration can affect not only price levels but also price volatility in raw-material markets, which may further increase downside risk for farms. The proposed framework provides a disciplined basis for scenario and simulation analyses relevant to agricultural and trade policy, and yields testable predictions for future ex-post evaluation. Full article

Water-scarce river basins face the dual challenge of sustaining development progress while maintaining water resources carrying capacity (WRCC), yet city-scale evidence remains limited on how New Quality Productive Force (NQPF)-driven high-quality development reshapes WRCC through coupled coordination and development–pressure decoupling processes. Using a balanced panel of 15 cities in the Weihe River Basin (WRB) during 2014–2023, an integrated analytical framework was implemented by combining composite index evaluation (WRCC and the high-quality development index (HQDI)), the Coupling Coordination Degree (CCD) model, Tapio decoupling diagnosis between HQDI and total water use (TWU), and logarithmic mean Divisia index (LMDI) decomposition. The results indicate that: (1) both the HQD index and WRCC exhibited sustained growth, with their CCD improving significantly from mild imbalance to primary coordination, while a distinct spatial pattern of “Guanzhong leading, northern Shaanxi improving, and eastern Gansu stabilizing” emerged; (2) the HQDI–WRCC linkage was further supported by pooled statistical tests and a two-way fixed effects specification with city-clustered robust standard errors, confirming a significant positive association (Pearson = 0.517, p < 0.01; Spearman = 0.183, p < 0.05) and a stable positive effect of HQDI on WRCC (β = 0.194, p = 0.0088); (3) Tapio results reveal an overall transition from earlier volatility toward a later-period regime dominated by Weak Decoupling (WD) and Strong Decoupling (SD), implying that development progress became less dependent on rising TWU, although pronounced inter-city heterogeneity persisted; (4) LMDI decomposition further identified water use intensity and industrial structure as primary inhibitors of water consumption, whereas the R&D scale effect increased nearly 60-fold, emerging as a major driver of water demand. This study provides a mechanistic basis for coordinating ecological protection and high-quality development under rigid water constraints in water-scarce basins. Full article