Search Results (382)

Bitcoin option prices reflect terminal variance and the cost of managing convex exposure in a market with changing depth and execution quality. This paper asks whether a liquidity state can be separated from fractional rough volatility in Bitcoin option valuation. The contribution is a modelling combination: standard stochastic-calculus and rough-volatility tools are joined to a regime-switching hedging-cost reserve, producing a leading-order at-the-money implied-volatility lift. The empirical design tests a liquidity–IV association and its scale using a 700-contract Deribit snapshot, a 4513-trade 24-h window spanning two UTC dates, a 775,315-trade panel over 92 dates, Ether replication, and placebos. The association is strong in open-interest-weighted specifications and for puts, but is absent for calls; it remains after controlling for option premium. Leave-one-expiry-out validation improves open-interest-weighted RMSE but not unweighted RMSE. A realised-volatility HMM is only a market-stress diagnostic, not an estimated liquidity regime. An empirical one-step hedging exercise does not validate the model’s simulated hedging comparative static. Accordingly, the evidence is associational, put-side, and narrower than a causal or fully structural validation. Full article

The actuator-fault estimation problem is addressed for discrete-time semi-Markov jump neural networks subject to time-varying delays, external disturbances, and communication constraints induced by the weighted try-once-discard (WTOD) protocol. Under this protocol, only the measurement channel with the largest weighted error is transmitted at each sampling instant, while the unselected channels retain their previously stored measurements at the filter side. To estimate the actuator fault, a fault-weighting dynamic system is first introduced. Then, by incorporating the WTOD-induced held measurement into the state vector, an augmented estimation model is constructed to describe the fault-weighting dynamics and the protocol-induced data-holding behavior within a unified framework. Based on this model, a mode-dependent and channel-dependent reduced-order fault-estimation filter is designed. The distinctive feature of the proposed framework is that the reconstruction of selected state components and the estimation of the actuator fault are addressed within a unified reduced-order estimator whose parameters depend jointly on the semi-Markov mode and the active WTOD transmission channel. By employing a Lyapunov–Krasovskii functional and using the semi-Markov transition information together with the WTOD scheduling constraint, sufficient LMI-based conditions are derived to ensure mean-square exponential stability and strict $(T_1,T_2,T_3)-\delta$ dissipativity performance of the resulting estimation error system. Finally, two examples are provided to illustrate the numerical effectiveness of the proposed actuator-fault estimation method under different semi-Markov switching realizations. Full article

A mixed redundancy, in which some components of a subsystem operate under active redundancy while the others wait in cold standby, has recently been shown to achieve a higher system reliability than the traditional active and standby strategies within equivalent resources. Existing reliability models for the strategy, however, either provide only a lower bound of the subsystem reliability or restrict the components to be identical with exponential or Erlang lifetimes. This study proposes an exact reliability model for a mixed redundant system composed of heterogeneous components. The time to failure of every candidate component is described by a generalized phase-type distribution (PHD), and a structured continuous-time Markov chain (CTMC) integrates the active redundant module, the standby components—installed in a specified sequence recorded by an ordered component-index set—and an imperfect fault detector/switch. Because the model yields the infinitesimal generator of the subsystem lifetime, it provides not only the exact reliability but also the hazard function and the moments of the system lifetime. Building on a pre-computed database that enumerates every feasible subsystem structure, the redundancy allocation problem determining the component types, their number and sequence, and the number of active redundancies is formulated as a compact binary integer linear program and solved to optimality. Benchmark experiments show that the previous approximate function underestimates the achievable design by an average maximum possible improvement (MPI) of 15.6%, and that permitting heterogeneous components raises the optimal system reliability by a further 7.3% on average—up to 18.7% as the resource budget grows. Full article

Macroeconomic forecasting has become increasingly challenging in environments characterized by structural breaks, nonlinear dynamics, and elevated economic uncertainty. Traditional econometric forecasting models frequently experience substantial predictive deterioration during periods of financial crises, geopolitical instability, and rapidly evolving macroeconomic conditions due to their reliance on assumptions of parameter stability and linear economic relationships. In response to these limitations, this study proposes an integrated causal machine learning framework designed to improve macroeconomic forecasting performance under structural instability and uncertainty. The proposed framework combines structural break detection techniques, machine learning algorithms, causal inference methodologies, and Explainable Artificial Intelligence (XAI) tools within a unified empirical architecture. More specifically, the study integrates Bai–Perron structural break analysis, Markov-Switching regime identification, Double Machine Learning (DML), Causal Forest estimation procedures, and SHAP-based explainability techniques. The empirical analysis employs a U.S. macroeconomic time-series dataset covering major crisis episodes, including the 2008 Global Financial Crisis, the COVID-19 pandemic, and the 2022 inflation shock. The dataset combines inflation, monetary, financial, energy-market, and uncertainty indicators obtained from publicly available U.S. macroeconomic databases. The empirical findings demonstrate that causal machine learning models significantly outperform conventional econometric frameworks such as VAR and TVP-VAR models, as well as standard machine learning algorithms including Random Forest (RF), XGBoost, and LSTM networks. The Double Machine Learning framework generates the strongest forecasting performance across all forecasting horizons, economic regimes, and robustness specifications. The results further reveal that macroeconomic relationships are highly regime-dependent and strongly influenced by uncertainty indicators, financial volatility, oil price shocks, and monetary policy dynamics. Explainability analysis additionally shows that uncertainty measures and energy market variables become dominant drivers of inflation forecasts during crisis periods characterized by elevated instability. The study contributes to the growing literature on macroeconomic forecasting by bridging econometric forecasting theory, causal inference methodologies, machine learning techniques, and explainable artificial intelligence within a unified forecasting framework. The findings provide important implications for central banks, policymakers, and financial institutions seeking more adaptive, transparent, and robust forecasting systems under uncertain macroeconomic environments. Full article

This study extends analytical barrier-option pricing models by jointly incorporating Markov-switching liquidity risk, asymmetric double-exponential jump risk, and a state-dependent Heath–Jarrow–Morton interest-rate structure. The underlying stock price dynamics under imperfect liquidity are driven by a Markovian regime-switching liquidity-adjusted double-exponential jump-diffusion model, and the risk-neutral valuation is obtained through an Esscher transform. Compared with existing DEJD barrier-option, liquidity-adjusted option-pricing, and Markov-modulated stochastic-interest-rate models, the proposed models highlight the joint effects of liquidity conditions, jump risk, regime switching, and state-dependent forward rates on European-style barrier option prices. Numerical illustrations based on Monte Carlo simulation are provided to examine model implications and benchmark special cases. Full article

This paper presents a local tracking control approach for DC–DC synchronous buck converters operating under randomly varying load resistance. The load variation is represented by a discrete-mode switching mechanism governed by a Markov stochastic process, allowing the converter dynamics to be modeled as a Markovian jump system. A mode-dependent state-feedback controller is developed based on linear matrix inequality (LMI)-based conditions that explicitly incorporate transition-rate information. By incorporating the transition characteristics into the controller design, the proposed method ensures stochastic local stability together with satisfactory tracking performance within a prescribed local stability region, while reducing the conservatism of conventional global-stability-based approaches. Simulation studies demonstrate that the proposed controller effectively regulates the converter under stochastic load variations. Full article

With the rapid expansion of renewable energy and the formal operation of provincial electricity spot markets in China, smart aggregators that coordinate flexible loads, storage resources, demand-response portfolios, and distributed energy resources increasingly face imbalance-settlement risk across the day-ahead and real-time segments of the electricity spot market. Existing studies often focus on average prices or point forecasts, which may overlook regime persistence, negative-price clustering, and tail exposure in high-frequency price spreads. This paper develops a regime-switching and tail-risk signal framework to characterize and forecast day-ahead–real-time price spreads in the Shandong electricity spot market and to translate these forecasts into risk-aware trading signals for representative smart aggregators. Using 35,136 non-public observations at 15 min resolution provided by State Grid Shandong Electric Power Company for 2024, the spread is analyzed using descriptive statistics, Markov regime-switching models, quantile regression, out-of-sample forecasting, trading-signal backtesting, component ablation, and robustness checks. The spread, defined as real-time price minus day-ahead price, has a mean of −7.50 Chinese yuan per megawatt-hour (CNY/MWh), a median of −0.005 CNY/MWh, 5% and 95% quantiles of −196.84 and 137.65 CNY/MWh, and 1% and 99% quantiles of −372.68 and 338.04 CNY/MWh, respectively. A three-state Markov model identifies negative-deviation high-volatility, near-zero low-volatility, and positive-deviation regimes with multi-hour persistence. In the December out-of-sample test, the upper- and lower-tail quantile signals achieve recall rates of 0.872 and 0.841, respectively, and removing lagged spreads increases mean absolute error (MAE) from 24.015 to 54.278 CNY/MWh. The framework provides risk-warning signals rather than causal identification or realized-profit evaluation. Full article

The preconditioned Crank–Nicolson (pCN) Markov chain Monte Carlo method is a standard tool for high-dimensional Bayesian inverse problems because its proposals preserve the prior and its performance degrades gracefully under mesh refinement. Yet its efficiency depends heavily on the step-size parameter $\beta$, which practitioners must tune manually for each problem. To remove this burden, we propose an acceptance rate-adaptive pCN algorithm that calibrates $\beta$ automatically during a finite burn-in phase via a Robbins–Monro stochastic approximation driven by cumulative acceptance rate feedback. We also studied a dual-signal extension that supplements acceptance rate adaptation with an auxiliary effective sample size (ESS) feedback term, activated gradually through a smooth logistic switch. To contextualize the proposed methods, we conducted a systematic comparison with the adaptive Metropolis (AM) and the Metropolis-adjusted Langevin algorithm (MALA). In both adaptive pCN variants, adaptation is confined to the burn-in and the step size is then frozen, so that the post-burn-in samples are produced by a time-homogeneous Metropolis–Hastings kernel targeting the correct posterior. Numerical experiments on five linear-Gaussian benchmarks with a controlled spectral structure (dimensions: 10 to 100, condition numbers: 2 to 200, SNR from $-9$ dB to $+8$ dB) and a nonlinear PDE-constrained inverse problem governed by the cubic–quintic nonlinear Schrödinger equation showed that the acceptance rate-adaptive pCN yields consistent improvements over vanilla pCN, reaching approximately an $1.8\times$ higher ESS in ill-conditioned regimes with low inter-seed variance. The AM achieved a superior ESS on well-conditioned and low-dimensional problems, while the MALA excelled in low-SNR regimes, but degraded under sharp posteriors. A sensitivity analysis confirmed the robustness to hyperparameter choices, and multi-seed experiments validated the reproducibility of all findings. These results indicate that automatic step-size adaptation can substantially improve the practical efficiency of pCN for challenging moderate-dimensional Bayesian inverse problems without manual tuning. Full article

High-resolution measurement data from photovoltaic–building systems enable analyses that extend beyond static energy balances toward the temporal structure of system operation. This study proposes a window-based analytical pipeline to identify operating regimes and their dynamic transitions in an integrated PV–building energy system. The approach combines energy flow decomposition, sliding temporal windows, and hidden Markov modeling to capture short-term behavioral patterns, regime persistence, and transition pathways. The results indicate that system operation is dominated by a limited number of persistent regimes with a pronounced diurnal organization. Regime transitions are structured and directional, typically occurring through intermediate operating states rather than via abrupt switching between energetically extreme conditions. The analysis of regime residence times reveals distinct characteristic temporal scales associated with different operating modes. Overall, the proposed framework provides a process-oriented representation of PV–building system dynamics that enhances interpretability and supports advanced monitoring and analysis of integrated energy systems. Full article

Although coordinating fiscal and monetary policy is essential for stabilizing small and globally exposed economies, researchers frequently oversimplify by treating the Visegrad Group (V4) as a uniform entity. This study investigates whether the Czech Republic, Hungary, Poland, and Slovakia are converging toward a unified macroeconomic standard or diverging due to institutional heterogeneity. Employing a hybrid framework combining Markov Regime-Switching Vector Autoregressive (MS-VAR) and single-regime robust OLS models alongside dynamic Impulse Response Functions (IRFs) on quarterly data from 2010 to 2024, the research estimates policy reaction functions to identify active and passive stances across the region. Empirical results suggest considerable regime heterogeneity. The Czech Republic and Poland demonstrate stable, single-regime monetary dominance grounded in strict Ricardian fiscal discipline. Conversely, Hungary exhibits structural fiscal dominance and severe institutional conflict, resulting in forced, high-volatility monetary tightening. Slovakia demonstrates persistent fiscal dominance but avoids macroeconomic destabilization because the market discipline provided by its Eurozone membership is economically negligible. The findings indicate that a country’s institutional setup and approach to monetary integration dictate its resilience to external shocks. This confirms that the V4 nations possess deep, underlying macroeconomic divergence. Full article

This paper considers stochastic optimal control of stochastic differential delay equations (SDDEs) with jumps and Markov switching, and its economical applications. The Hamilton–Jacobi–Bellman (HJB) equation and the inverse HJB-equation are derived by applying the Dynkin’s formula and solution of the Dirichlet–Poisson problem for those SDDEs. Three cases of modified Ramsey stochastic models in economics are studied; namely, a Ramsey diffusion model with jumps, a Ramsey diffusion model with Markov switching, and a Ramsey diffusion model with jumps and Markov switching. We also present numerical examples for all three cases. The contributions of the paper are four-fold: introducing and studying three new models for SDDEs: with jumps, Markov switching and both; studying optimal control for those models; applications of these results to economics with Ramsey’s stochastic models; and numerical examples for these different models. Full article

In this paper, we investigate the stability analysis for a class of novel variable-order Caputo fractional-order dual switching systems. First, the short memory principle is adopted to construct the studied system model, where the Caputo fractional order is randomly time-varying, and the outer deterministic switching signal governs the overall dwell-time scheduling of subsystems. Under the designed event-triggered deterministic switching strategy, each fractional-order subsystem is characterized by an internal Markov random jumping processing. Secondly, combining the multiple Lyapunov functions method, fractional-order comparison lemma and average dwell time (ADT) technique, the corresponding sufficient stability criteria are established to guarantee the globally asymptotic stability almost surely (GAS a.s.) and the global Mittag–Leffler stability almost surely (GMLS a.s.). Finally, a numerical simulation example is presented to verify the feasibility and effectiveness of the derived theoretical results. Full article

Vertically linked commodity markets—global futures, regional spot, and farm-gate prices—transmit information through directed cross-market channels whose strength varies with latent volatility regimes. Standard deep learning forecasters absorb both the directed cross-market dependence and the regime dependence of intrinsic-mode-aligned latent components into shared model weights, with no explicit architectural mechanism that exposes either as an inspectable structure. This paper proposes HVB-RA, a modular framework that combines two such mechanisms with a per-tier Variational Mode Decomposition and bidirectional LSTM backbone: (i) a directed cross-market attention layer in which the upstream-to-downstream topology is supplied from domain knowledge and the time-varying upstream-source attention intensities at the farm-gate tier (the regional-spot tier, with a single upstream key, reduces algebraically to a fixed residual upstream fusion) are extracted from data, and (ii) a regime-informed modal-weighting layer that mixes two trainable softmax weight profiles over IMF-aligned latent components through a filtered Markov-switching state probability fitted in a separate stage. An auxiliary post hoc projection enforces an exact linear constraint defined by long-run sample-mean ratios across tiers; the paper does not claim that these descriptive ratios are cointegrating relations or equilibrium coefficients. The framework is evaluated on three tiers of daily natural-rubber prices spanning 2038 trading days, against three external benchmarks (random walk, ARIMA(2,0,2), and an exogenous-only LSTM) and a contemporary neural hierarchical-interpolation forecaster (NHITS). Root mean squared error is reported per tier-horizon cell; a decision-aware income-smoothing metric quantifies the operational value of $h=5$ farm-gate forecasts under a 5-day selling rule; and a within-method comparison evaluates the marginal contribution of the auxiliary constraint projection. On the present single-regime test window, HVB-RA attains a lower point error than the contemporary NHITS baseline at every tier-horizon cell, while no method—including HVB-RA—improves on the random-walk floor at most cells; the regime-conditional components of the architecture are not identifiable because every calibration and test origin is classified as a high-volatility regime by the trained Markov-switching model. The paper contributes to machine learning and knowledge extraction by demonstrating how time-varying upstream-source attention intensities at the farm-gate tier and regime-dependent latent-component-weight profiles—two forms of latent structure typically absorbed into model weights—can be exposed as explicit, inspectable, and individually testable components of a multi-tier forecasting architecture, and by providing a reproducibility package documenting the conditions under which each component is expected to be identifiable. Full article

This study develops a leakage-free hybrid Markov–Random Forest framework for cryptocurrency price forecasting and evaluates it on Bitcoin and Ethereum. Daily OHLCV features are lagged by one trading day to prevent look-ahead bias, while regime labels are assigned from observed price changes using a two-state Markov chain with increasing and decreasing states. Regime-specific Random Forest models are then tuned independently via time-series cross-validation, allowing the predictive structure to adapt to regime-specific market conditions. The empirical results exhibit clear asset dependence. For Ethereum, the hybrid model outperforms the standalone Random Forest on magnitude-based metrics, attaining lower MAE and RMSE while also delivering a modest improvement in directional accuracy. Regime-specific tuning further identifies distinct optimal hyperparameter configurations across the increasing and decreasing states, suggesting that Ethereum’s upward and downward dynamics are structurally heterogeneous and can be better captured through regime-aware learning. By contrast, for Bitcoin, the standalone Random Forest delivers superior magnitude forecasting performance, while the regime-specific models differ only in tree depth and share the remaining tuning parameters, indicating that regime conditioning adds limited incremental value in a more persistent market. Statistical tests reinforce these findings. For Ethereum, Diebold–Mariano tests show that the hybrid significantly outperforms the standalone Random Forest under squared loss, while the absolute-loss comparison is only marginal. Across both assets, directional accuracy remains close to random chance, confirming the limited predictability of next-day price direction from lagged OHLCV features. Overall, the hybrid framework is most valuable when regime-specific dynamics are sufficiently distinct, offering improved forecasting performance and greater interpretability than a single global model. Full article

This study investigates whether FinTech thematic assets achieve dynamic variance reduction for regional ESG market risk under clean-energy equity stress regimes, using daily data from March 2019 to August 2024 across five S&P ESG LargeMidCap markets spanning developed and emerging economies (North America, Europe, Asia Pacific Developed, Latin America Emerging, and Asia Pacific Emerging). Employing a DCC-GARCH framework with GJR-GARCH univariate specifications across four S&P Kensho FinTech channels—Democratized Banking, Alternative Finance, Future Payments, and Distributed Ledger—we estimate time-varying correlations and hedging effectiveness, and assess safe-haven properties via the Baur–Lucey framework. The most robust finding is that North America ESG shows the strongest dynamic variance reduction (59–76%), improving further during high clean-energy equity stress regimes (p < 0.01, bootstrap permutation test); Asia Pacific Developed ESG shows the weakest (7–9%) despite its developed-market status, while Latin America Emerging ESG’s comparatively high variance reduction (28–40%) is tempered by residual ARCH effects that point to incompletely modeled volatility rather than structural hedging capacity. All FinTech channels remain positive diversifiers rather than safe havens across every market and regime. Hedging capacity thus tracks market-specific volatility and correlation dynamics rather than a simple developed–emerging divide. The analysis is bounded by a single five-year sample window and two transition-risk proxies, warranting continued monitoring as FinTech and ESG regulatory frameworks evolve. Full article