In the present investigation, the phenomena of multi-scale volatility spillovers and dynamic hedging within the Chinese stock market are scrutinized, with particular emphasis on the implications of structural breaks. The decomposition of the returns from the CSI 300 and Hang sheng index’ spot and futures is achieved through the application of the Maximum Overlap Discrete Wavelet Transform (MODWT), categorizing the data into three distinct temporal scales: short-term, medium-term, and long-term. An enhancement upon the conventional VAR-BEKK-GARCH (Vector Autoregressive - Baba, Engle, Kraft, and Kroner - Generalized Autoregressive Conditional Heteroskedasticity) model is proposed, yielding the asymmetric VAR-BEKK-GARCH Model (VAR-BEKK-AGARCH), which adeptly integrates the structural break of return volatility. A comprehensive analysis is conducted to elucidate the interactions and spillovers between the CSI 300 and Hang Seng Index, as well as their respective spot and futures markets, across the various identified time scales. Concurrently, a dynamic hedging portfolio, comprised of index spot and futures, is meticulously constructed, with its performance rigorously evaluated under the influence of the different time scales. To ensure the robustness and validity of the findings, wavelet coherence and phase difference methodologies are employed as verification tools. The results unequivocally reveal a heterogeneity in the behavior of mean spillover, volatility spillover, and asymmetric spillovers between the spot and futures markets of the CSI 300 and Hang Seng Index across the diverse scales. The inclusion of a structural break in the dynamic hedge portfolio is demonstrated to confer a marked advantage over its counterpart that omits this critical factor. Particularly in the short and medium-term scenarios, the dynamically hedged portfolio, enriched by the consideration of the structural break, exhibits superior performance in comparison to the static hedge portfolio. Additionally, it is discerned that the CSI 300 Index and Hang Seng Index, along with their spot and futures components, predominantly manifest in synchrony, with no clear indication of a consistent leader-lag relationship. An intensification of correlation is observed in the long-term analysis, underscoring the utility of the spot and futures of the two indices as efficacious hedging tools.
In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.
In contemporary military contexts, the determination of an optimal course of action (COA) in combat operations emerges as a critical challenge. This study delineates a decision support methodology for military applications, employing sophisticated decision analysis techniques. The initial phase entails the identification of pivotal criteria for assessing and ranking COAs, followed by the assignment of weight coefficients to each criterion via the full consistency method (FUCOM). Subsequently, the Einstein weighted arithmetic average operator (EWAA) was utilized for the aggregation of expert opinions, ensuring a consensual evaluation of these criteria and culminating in the final values of their weight coefficients. The ensuing phase focuses on the selection of an optimal COA, incorporating the grey complex proportional assessment (COPRAS-G) method. This method addresses uncertainties and varying criterion values. Expert ratings were again aggregated using the EWAA operator. The findings from this phase are designed to provide military commanders with precise, data-driven guidance for decision-making. To validate and verify the stability of the proposed model, a series of tests were conducted, including a rank reversal test, sensitivity analysis regarding changes in weight coefficients, and a comparative analysis with alternative methods. These assessments uniformly indicated the model's consistency, stability, and validity as a military decision support tool. Emphasizing a high degree of confidence in COA selection, the methodology advocated herein is applicable to decision-making processes in the planning and execution of military operations. The uniform application of professional terms, consistent with the broader context of this research, ensures clarity and coherence in its presentation. The approach outlined in this study stands as a testament to rigorous analytical methodologies in the realm of military strategic planning, offering a robust framework for decision-making under conditions of uncertainty and complexity.
In the realm of emergency response, where time and information constraints are paramount, and scenarios often involve high levels of toxicity and uncertainty, the effective management of industrial control systems (ICS) is critical. This study introduces novel methodologies for enhancing decision-making processes in emergency situations, specifically focusing on ICS-security. Central to this research is the employment of spherical hesitant fuzzy soft set (SHFSS), a concept that thrives in the presence of ambiguity and incomplete information. The research adopts and extends the parametric families of t-norms and t-conorms, as introduced by Yager, to analyze these sets. This approach is instrumental in addressing multi-attribute decision-making (MADM) problems within the ICS-security domain. To this end, four distinct aggregation operators (AOs) are proposed: spherical hesitant fuzzy soft yager weighted averaging aggregation, spherical hesitant fuzzy soft yager ordered weighted averaging aggregation, spherical hesitant fuzzy soft yager weighted geometric aggregation, and spherical hesitant fuzzy soft yager ordered weighted geometric aggregation. These operators are tailored to harness the operational benefits of Yager's parametric families, thereby offering a robust framework for dealing with decision-making problems under uncertainty. Further, an algorithm specifically designed for MADM is presented, which integrates these AOs. The efficacy and precision of the proposed methodology are demonstrated through a numerical example, applied in the context of an ICS security supplier. This example serves as a testament to the superiority of the approach in handling complex decision-making scenarios inherent in ICS-security management.
In the realm of epidemiological modeling, the intricacies of epidemic dynamics are elucidated through the lens of compartmental models, with the SIR (Susceptible-Infectious-Recovered) and its variant, the SIS (Susceptible-Infectious-Susceptible) model, being pivotal. This investigation delves into both deterministic and stochastic frameworks, casting the SIR model as a continuous-time Markov chain (CTMC) in stochastic settings. Such an approach facilitates simulations via Gillespie's algorithm and integration of stochastic differential equations. The latter are formulated through a bivariate Fokker-Planck equation, originating from the continuous limit of the master equation. A focal point of this study is the distribution of extinction time, specifically, the duration until recovery in a population with an initial count of infected individuals. This distribution adheres to a Gumbel distribution, viewed through the prism of a birth and death process. The stochastic analysis reveals several insights: firstly, the SIR model as a CTMC encapsulates random fluctuations in epidemic dynamics. Secondly, stochastic simulation methods, either through Gillespie's algorithm or stochastic differential equations, offer a robust exploration of disease spread variability. Thirdly, the precision of modeling is enhanced by the incorporation of a bivariate Fokker-Planck equation. Fourthly, understanding the Gumbel distribution of extinction time is crucial for gauging recovery periods. Lastly, the non-linear nature of the SIR model, when analyzed stochastically, enriches the comprehension of epidemic dynamics. These findings bear significant implications for epidemic mitigation and recovery strategies, informing healthcare resource planning, vaccine deployment optimization, implementation of social distancing measures, public communication strategies, and swift responses to epidemic resurgences.
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