Markov Chain Model Research Articles

Latin American Equities, Volatility Regimes, and the US Economic Policy Uncertainty

We investigate how the volatility of the iShares Latin America 40 ETF (ILF) responds to key economic and market sentiment indicators associated with economic uncertainty. Specifically, we explore the regime-dependent nature of ILF volatility in relation to Economic Policy Uncertainty (EPU), U.S. Economic Uncertainty (ECU), Global Economic Policy Uncertainty (GEPU), and implied risk, as captured by the Chicago Board Options Exchange's VIX (CBOE VIX), from 2001 to 2023. Our findings highlight that the connection between market volatility and economic/market sentiment is influenced by distinct volatility regimes. Utilizing a two-covariate GARCH-MIDAS (GM) model, a regime-switching Markov Chain (MSR) model, and quantile regressions (QR), we reveal that the impact of sentiment on realized volatility varies depending on the prevailing volatility regime, reflecting investors’ differing responses to market uncertainty. Additionally, our results show a significant linkage between ILF’s short and long-term volatility and economic uncertainty/sentiment indicators, suggesting that these factors shape ILF volatility across different market conditions and quantiles of the volatility distribution. Overall, our findings indicate that investor sentiment and economic uncertainty extend beyond their domestic origins, influencing volatility patterns in U.S., global, and Latin American markets. JEL classification: G12, G14, G38. Keywords: Volatility, GARCH-MIDAS, VIX, Economic policy uncertainty, Global economic policy uncertainty, Quantile regression, Regime switching Markov Chain regression.

The Coordinated Relationship Between the Tourism Economy System and the Tourism Governance System: Empirical Evidence from China

The development of tourism governance (TG) is influenced by the tourism economy (TE), and the development of TE is guaranteed by tourism governance. This study investigates the development levels of the tourism economy and tourism governance, as well as their interactive coordination in 31 Chinese provinces (including municipalities and autonomous regions) from 2012 to 2021. First, the vertical and horizontal differentiation method was employed to measure tourism economy and tourism governance development levels. Second, the Panel Vector Autoregression (PVAR) model was adopted to examine the Granger causality and the interactive effects between the tourism economy and tourism governance. Third, the coupled coordination model, kernel density estimation, and Markov chain model were combined to explore the degree of coordinated development and the spatio-temporal evolutionary trend of TE-TG. The findings reveal the following: (1) The development level of the tourism economy exhibits a fluctuating upward trend, with its spatial distribution pattern demonstrating a distinct coastal-to-inland decreasing gradient. Meanwhile, tourism governance shows a steady improvement trajectory marked by significant regional disparities. (2) A long-term equilibrium relationship has been established between the tourism economy and tourism governance, with bidirectional Granger causality observed between the two systems. (3) The coupled coordination between the tourism economy and tourism governance has progressively increased. However, the development level of tourism governance still lags behind that of the tourism economy. The eastern and central regions demonstrate significantly higher TE-TG coordination levels compared to the western and northeastern regions.

Driving Forces of Agricultural Land Abandonment: A Lithuanian Case

The abandonment of agricultural land is now considered one of the primary land use changes driven by complex interactions between social, economic, and environmental factors. To understand and manage this process, a holistic approach that integrates multidimensional methodologies and interactions is essential. This study examines the key driving factors behind agricultural land abandonment in Lithuania using two methodological approaches. First, seventeen highly qualified land management experts were surveyed, and their insights were analysed using in-depth qualitative interviews, focusing on agricultural land abandonment and its underlying factors. Second, the development of agricultural land abandonment in a representative Lithuanian municipality was modelled using Markov chain models, incorporating freely available geographic data as factors influencing land use transformation. Actual areas of abandoned agricultural land were mapped using orthophotos from 2012, 2018, and 2021, for both model development and validation. The importance of predictors in the model was then assessed in relation to their significance as drivers of agricultural land abandonment. The findings indicate that natural factors, such as the proximity of forests and topographical constraints, play a significant role in explaining land abandonment processes. Additionally, agricultural land abandonment is influenced by social, economic, and legal factors, including land ownership structures, migration, and infrastructure accessibility. The importance of soil quality, productivity, and the presence of nearby arable land was found to vary depending on data accuracy and local environmental conditions, highlighting the complexity of agricultural land use patterns. The chosen mixed-method approach, combining qualitative surveys with numerical spatial modelling, demonstrates potential for identifying critical land use areas and providing insights to improve land management policies and decision making.

Integrated Approach to Marine Engine Maintenance Optimization: Weibull Analysis, Markov Chains, and DEA Model

This study addresses the growing need for predictive maintenance in the maritime industry by proposing an optimized strategy for ship engine maintenance. The aim is to reduce unplanned failures that cause significant financial losses and disrupt global logistics flows. The methodology integrates Weibull reliability analysis, Markov chains, and Data Envelopment Analysis (DEA). A dataset of 512 diesel engine components from container ships was analysed, where the Weibull distribution (β = 1.8; α = 18,500 h) accurately modelled failure patterns, and Markov chains captured transitions between operational states (normal, degraded, failure). DEA was used to evaluate the efficiency of different maintenance strategies. Results indicate that targeting interventions in the degraded state significantly reduces downtime and improves component reliability, particularly for high-pressure fuel pumps and turbochargers. Optimizing maintenance extended the Mean Time to Failure (MTTF) up to 22,000 h and reduced the proportion of failures in critical components from 64.3% to 40%. These findings support a transition from reactive to proactive maintenance models, contributing to enhanced fleet availability, safety, and cost-effectiveness. The approach provides a quantitative foundation for predictive maintenance planning, with potential application in fleet management systems and smart ship platforms.

Predicting gene expression changes from chromatin structure modification

Spatial organization of chromatin plays a critical role in gene transcription, but connecting population-averaged HiC data to functional outcomes remains a challenge. We present a computational framework linking HiC contact map to gene transcription. Utilizing a bead-spring polymer model informed by HiC contact maps, we generate an ensemble of 3D conformations for a given genomic locus. These conformations are then coupled to gene transcription levels through a Markov chain model, with transition rates derived from molecular dynamics simulations. The efficacy of this framework is demonstrated by simulating the perturbation of a CTCF-mediated TAD boundary, impacting the expression of sox9 and kcnj2. Our model quantitatively reproduces experimentally observed changes in gene expression, revealing that the increased kcnj2 transcription is a consequence of enhancers within the sox9 TAD becoming accessible upon boundary disruption. Quantifying enhancer impact, our model can also identify functional enhancers. This framework enhances our understanding of the relationship between chromosome spatial architecture and gene regulation.

Reliability analysis of fault-tolerant reconfigurable non-isolated dc-dc converter for dc microgrid

ABSTRACT In power electronic converters, capacitors and switches are the most vulnerable components prone to failure. This letter presents a topology with reconfigurable fault-tolerance capability, along with a reliability analysis. The proposed converter can be reconfigured in the event of faults in the input or output capacitor without altering the topology’s voltage gain. Fuses and relays are used to switch from normal operating mode to reconfigurable mode. To demonstrate the superiority of the proposed Fault-Tolerant Reconfigurable Quadratic Boost (FTRQB) converter over the conventional quadratic boost converter, a reliability analysis is conducted using the Markov chain model. The performance of the FTRQB converter is validated through a 1-kW hardware implementation, tested under input and output capacitor fault conditions.

Statistics for Continuous Time Markov Chains, a Short Review

This review article is concerned to provide a global context to several works on the fitting of continuous time nonhomogeneous Markov chains with finite state space and also to point out some selected aspects of two techniques previously introduced—estimation and calibration—relevant for applications and used to fit a continuous time Markov chain model to data by the adequate selection of parameters. The denomination estimation suits the procedure better when statistical techniques—e.g., maximum likelihood estimators—are employed, while calibration covers the case where, for instance, some optimisation technique finds a best approximation parameter to ensure good model fitting. For completeness, we provide a short summary of well-known important notions and results formulated for nonhomogeneous Markov chains that, in general, can be transferred to the homogeneous case. Then, as an illustration for the homogeneous case, we present a selected Billingsley’s result on parameter estimation for irreducible chains with finite state space. In the nonhomogeneous case, we quote two recent results, one of the calibration type and the other with more of a statistical flavour. We provide an ample set of bibliographic references so that the reader wanting to pursue her/his studies will be able to do so more easily and productively.

Behavioral based threat detection

Insider threats pose a significant risk to organizations as they exploit legitimate access to bypass traditional security measures, making them harder to detect than external attacks. This study addresses the challenge by utilizing deep learning to analyze user behavior and identify malicious activities through a carefully selected set of event- based features. By training on the CMU CERT r4.2 dataset, the proposed model effectively learns patterns of adversarial behavior, reducing false positives while maintaining high detection accuracy. The paper presents a deep learning-based approach for insider threat detection, emphasizing behavioral analysis to distinguish between normal and malicious user activities. By leveraging a rich event-based feature set, including logon/logoff events, user roles, and functional units, the model is trained on the CMU CERT r4.2 dataset to identify adversarial behavior with high accuracy and a low false positive rate. The proposed method outperforms several established techniques, including LSTM-CNN, random forest, LSTMRNN, one-class SVM, Markov chain models, multi-state LSTM-CNN, and GRU-Skipgram. Experimental results demonstrate the effectiveness of this approach, achieving 90.60% accuracy, 97% precision, and a 94% F1- score, making it a promising solution for mitigating insider threats in organizations. Key Words:Insider threats, Legitimate access, Bypass security measures, Detection, Deep learning-based approach, User behavior analysis, Event-based features, Logon/logoff events, User roles, Functional units, CMU CERT r4.2 dataset, Adversarial behavior, False positives, LSTM-CNN, Random forest, LSTM-RNN, One-class SVM, Markov chain models, Multi-state LSTM-CNN, GRU-skipgram, Accuracy(90.60%), Precision(97%), F1score(94%), Cybersecurity defenses.

Application of the One-Step Second-Derivative Method for Solving the Transient Distribution in Markov Chain

Markov chains are an application of stochastic models in operation research, helping the analysis and optimization of processes with random events and transitions. The method that will be deployed to obtain the transient solution to a Markov chain problem is an important part of this process. The present paper introduces a novel Ordinary Differential Equation (ODE) approach to solve the Markov chain problem. The probability distribution of a continuous-time Markov chain with an infinitesimal generator at a given time is considered, which is a resulting solution of the Chapman-Kolmogorov differential equation. This study presents a one-step second-derivative method with better accuracy in solving the first-order Initial Value Problems (IVPs) compared to other approaches found in the literature, which is verified by the obtained solutions. The determination of the transient solutions for Markov chains is presented using the proposed method. The results show better accuracy in solving the transient distribution in Markov chains, which implies that there is an improved assurance in adopting this approach in future studies of the Markov chain modeling process for predicting future events based on the current state of a process. Future studies on Markov chain modeling could adopt the introduced method to predict future events based on the current state of a process.

The operation optimization of multipurpose reservoir between ARIMA, Continuous, and Markov Chain models on Jatigede reservoir, Indonesia

The operation optimization of multipurpose reservoir between ARIMA, Continuous, and Markov Chain models on Jatigede reservoir, Indonesia

A Copula Duration Model with Dependent States and Spells

A nested Archimedean copula model for dependent states and spells is introduced and the link to a classical survival model with frailties is established. The model relaxes an important restriction of classical survival models as unobservable heterogeneities are permitted to be correlated with the observable covariates. Its modular structure has practical advantages as the different components can be separately specified and estimation can be done sequentially or separately. This makes the model versatile and adaptable in empirical work. An application to labour market transitions with linked administrative data supports the need for a flexible specification of the dependence structure and the model for the marginal survivals. The conventional Markov Chain Model is shown to give sizeably biased results in the application.

Spatial and temporal analysis of dry spell variability in Aceh, Indonesia: Implications for drought mitigation and agricultural planning

Abstract Understanding the spatial and temporal variability of dry spells is critical for effective agricultural planning and water resource management, particularly in regions prone to climate variability. This study analyses the spatial distribution and seasonal patterns of dry spell probabilities in Aceh, Indonesia, using a first-order Markov chain model applied to 20 years of high-resolution satellite precipitation data (2001–2020). The results reveal a distinct north-south gradient in dry spell probabilities, with the northern regions experiencing higher frequencies of consecutive dry days, especially during the dry season (June-August). The return period analysis indicates that the northern and eastern parts of Aceh are more susceptible to prolonged dry spells, posing significant risks to rain-fed agriculture, particularly paddy cultivation. These findings underscore the importance of targeted drought mitigation strategies, such as developing drought-resistant crops and enhancing irrigation infrastructure, to ensure agricultural sustainability in the region. The study also highlights the critical role of topography in shaping local climate patterns, with implications for regional climate adaptation strategies. This research contributes valuable insights into the climatic challenges faced by tropical regions and offers practical guidance for improving resilience to drought conditions.

Differential vulnerability of key threatened mammals to climate and land cover changes in the Central Himalayas

AbstractAnthropogenic climate change affects biological diversity by altering their suitable habitat ranges. The Himalayan region is one of the world's most sensitive biodiversity hotspots to global climate change. The Chitwan Annapurna Landscape (CHAL) in the central Himalayas serves as a vital north–south linkage among the protected areas in central Nepal and provides suitable habitats for threatened mammals in different ecological zones, such as snow leopards (in the alpine zone), Himalayan red panda (in the temperate zone), and one‐horned rhinoceros (in the lowland tropical zone). The biodiversity of CHAL is threatened by climate change and land use alterations. This study assessed the potential impacts of climate and land cover changes on the above three key threatened mammals in CHAL by employing maximum entropy (MaxEnt) modeling to predict the current potential habitat and project it for future climate change scenarios under different greenhouse gas concentrations. Further, we used the cellular automata and Markov Chain models to simulate and predict the temporal and spatial changes in land cover of CHAL. Our results indicate that the snow leopard and Himalayan red panda will experience more significant vulnerability than the one‐horned rhinoceros in all future climate scenarios. Approximately 36.3% and 41.8% of the suitable habitat of the snow leopard and 32.5% and 56% of the Himalayan red panda in CHAL are projected to be lost in 2050 and 2070, respectively, under representative concentration pathway (RCP6.0). Climate refugia, representing areas of suitable habitat for 2070 (under the RCP6.0) in CHAL, are projected to cover 958 km2 (80.37% of the current range), 1052 km2 (43.73% of the current range), and 2375 km2 (58.21% of the current range) for one‐horned rhinoceros, Himalayan red panda, and snow leopard, respectively. Among the land cover attributes in CHAL, snow cover is predicted to decrease by 24% in 2070. Our findings indicate that species inhabiting alpine and temperate environments are more susceptible to human‐induced climate change than those inhabiting lowland tropical areas. These findings will help to implement the adaptation actions that are crucial to addressing future conservation challenges arising from climate and land cover change.

Optimal Crop Planting Based on the Monte Carlo-Simulated Annealing Algorithm

This article elucidates the critical importance of sustainable land resource management in safeguarding human well-being amid globalization and urbanization, with a particular emphasis on the optimal utilization of cultivated land resources in the mountainous regions of North China. The study focuses on the cultivated land resources within a specific village in these areas, encompassing open-air cropland, conventional greenhouses, and smart greenhouses. Data is sourced from actual plot classifications and crop cultivation conditions. A Markov chain model is employed to predict planting plot types, integrated with linear programming techniques and Monte Carlo simulated annealing algorithms to maximize total revenue while minimizing unsold waste. The findings demonstrate that a multi-objective optimization model incorporating competitive and cooperative game theory can effectively propose scientifically grounded planting structures that enhance both economic returns and environmental sustainability. This model offers valuable insights for the conservation and utilization of cultivated land resources not only in North China's mountainous regions but also globally, thereby advancing agricultural practices towards greater efficiency, ecological protection, and sustainability.

Rainfall variability and probability analysis in Tarai and mid Himalayan regions of Uttarakhand

The rainfall analysis is helpful for proper crop planning under changing environment in any region. Therefore in this study an attempt has been made to analyze long term rainfall data, i.e., 40 years from 1981-2020 (Tarai region) and 36 years from 1985-2020 (mid Himalayan region) as per availability and comparison is being done on the type of crop to be grown, sowing time, irrigation scheduling etc. The initial and conditional probability of rainfall on weekly basis was analyzed at different levels, i.e., >5mm, >10mm, >20mm, >30mm, >40mm and >50mm using Markov chain model. In addition to this, incomplete gamma distribution was also used to find out the occurrence of rainfall events at different probability levels, i.e., 10, 25, 50, 75 & 90 per cent. The results revealed that though the amount of rainfall in the Tarai region is higher when compared with mid Himalayan region but most of them is limited to southwest monsoon season only. The crops can be raised easily throughout the year in the mid Himalayan region because of uniform distribution of rainfall in all the seasons except in the post monsoon season year and by arranging lifesaving irrigation to growing crops during light/moderate/severe dry spells conditions only from available natural springs/perennial streams/harvesting run off water during the water stress weeks. On the other hand, in the Tarai region proper irrigation facilities must be ensured during all the seasons except in the southwest monsoon season in order to have productive yield.

Impact of Menstrual Cycles or Combined Oral Contraception on Training Loads Assessed Using Latent Effort States in Female Elite Cyclists.

To quantify the impact of the regular or irregular menstrual cycle (MC) or combined oral contraception (OC) on the time spent at the maximum effort exertion per training, assessed using latent effort states through a Hidden Markov chain Model (HMM). 6303 training sessions with heart rate (HR) and power output (PO) recorded every second were used to train HMM in order to determine latent effort states of 12 elite French cyclists followed up over 30 months. A total of 101 MC/OCs full cycles were analyzed. A calendar method was used to estimate regular MC phases (menstruation, estimated follicular phase, estimated luteal phase). Irregular MC was divided into menstruations/no menstruations and OC into break/active pill taking. Four latent effort states were identified: high, medium+, medium-, and low. Focused on high intensity-oriented training sessions, the proportion of time spent in high intensity effort state was significantly lower during menstruation (34.5%) compared to estimated follicular (65.2%, p = 0.0009) and luteal (55.4% p = 0.024) phases for regular MC, and during pills' break (43.7%) compared to active pill taking phase (62.6% p = 0.031) for OC cycles. During the high intensity-oriented training sessions, the proportion of time spent in high effort state is almost twice higher in mid-regular cycles whereas is lower during menstruation or pill's break in elite cyclists. These findings rely on repeated assessment of training loads in a real-world context, analyzed using novel machine learning techniques that objectively quantify both internal and external training loads in elite female cyclists.

Pavement Performance Prediction of Semarang–Solo Toll Road, Indonesia, using Markov Chain Model

Roads are one of the infrastructures that significantly impact a country's economic growth. The condition of the roads usually decreases due to increasing vehicle volumes. Predicting road conditions is essential for planning future maintenance. This study aims to evaluate the pavement conditions over five years using Markov chain model for five sections of the Semarang-Solo toll road, Indonesia. Two scenarios are selected in the simulation, without-handling and with-handling programs. The results show that the historical data used to compile the transition probability matrix (TPM) from the Markov model greatly influences the simulation results in both scenarios. In addition, the simulation results also indicate that the inner lane for all segments of Semarang - Solo direction is the most crucial because these segments have a relatively high rate of decline in road steadiness and a shorter cycle time for implementing the handling program.

Changepoint detection in Gaussian sequential data with copula-based Markov chain models in an online environment

Online changepoint detection involves determining if there are changes in a sequential data structure over time. Addressing the dependence structure is crucial in analysing time series data. To address this, we propose a copula-based Markov chain model that uses the Clayton copula and the Gaussian distribution as the marginal distribution. To overcome the limitations of the independence-based conjugate structure, we incorporate the Markov chain Monte Carlo (MCMC) method for online changepoint detection. Simulation experiments demonstrate that our proposed model outperforms competitors in terms of the Precision, F1 Score, and Mean Absolute Error (MAE) across various scenarios. In empirical studies, we apply our model to analyse the daily log returns of the S&P 500 Index during the significant events like the 2008 financial crisis and the 2020 COVID-19 pandemic. The results successfully capture the structural changes in these real-world applications.

Projecting Future Wetland Dynamics Under Climate Change and Land Use Pressure: A Machine Learning Approach Using Remote Sensing and Markov Chain Modeling

Wetlands in the Yellow River Watershed of Inner Mongolia face significant reductions under future climate and land use scenarios, threatening vital ecosystem services and water security. This study employs high-resolution projections from NASA’s Global Daily Downscaled Projections (GDDP) and the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), combined with a machine learning and Cellular Automata–Markov (CA–Markov) framework to forecast the land cover transitions to 2040. Statistically downscaled temperature and precipitation data for two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) are integrated with satellite-based land cover (Landsat, Sentinel-1) from 2007 and 2023, achieving a high classification accuracy (over 85% overall, Kappa > 0.8). A Maximum Entropy (MaxEnt) analysis indicates that rising temperatures, increased precipitation variability, and urban–agricultural expansion will exacerbate hydrological stress, driving substantial wetland contraction. Although certain areas may retain or slightly expand their wetlands, the dominant trend underscores the urgency of spatially targeted conservation. By synthesizing downscaled climate data, multi-temporal land cover transitions, and ecological modeling, this study provides high-resolution insights for adaptive water resource planning and wetland management in ecologically sensitive regions.

Data fusion for pavement performance modelling using the Bayesian approach: case study in Afghanistan

In Afghanistan, the pavement condition data is lacking leading to the infeasibility of developing a pavement performance model. This study aims to apply the Bayesian approach to fuse regression and the Markov Chain model to enhance the accuracy of the performance model under the limitation of high-quality data. For this purpose, more than 6000 km of Afghanistan national highways are monitored and the surface distresses are detected and measured resulting in a calculation of the pavement condition index. Then, the regression and Markov Chain models are built with R2 of 0.68 and 0.88, respectively, and fused to come up with the Bayesian model. Finally, the Bayesian model is successfully validated with an R2 of 0.98. It is concluded that the Bayesian model is a flexible approach that can be applied in such countries suffering from a lack of pavement condition data and can be enhanced as more data is available.