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The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.

To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.

In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.

A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

A generalized distribution with wide range of skewness and elongation will be suitable for the data mining and compatible for the misspecification of the distribution. Hence, the purpose of this paper is to present a distribution-based approach for estimating degradation reliability considering these conditions.

Tukey’s g-and-h distribution with the quantile expression is introduced to fit the degradation paths of the population over time. The Newton–Raphson algorithm is used to approximately evaluate the reliability. Simulation verification for parameter estimation with particle swarm optimization (PSO) is carried out. The effectiveness and validity of the proposed approach for degradation reliability is verified by the two-stage verification and the comparison with others’ work.

Simulation studies have proved the effectiveness of PSO in the parameter estimation. Two degradation datasets of GaAs laser devices and crack growth are performed by the proposed approach. The results show that it can well match the initial failure time and be more compatible than the normal distribution and the Weibull distribution.

Tukey’s g-and-h distribution is first proposed to investigate the influence of the tail and the skewness on the degradation reliability. In addition, the parameters of the Tukey’s g-and-h distribution is estimated by PSO with root-mean-square error as the object function.

This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.

Many VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).

The results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.

An important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.

This paper examined the volatility models for exchange rate return, including Random Walk model, AR model, GARCH model and extensive GARCH model, with Normal and Student-t distribution assumption as well as nonparametric specification test of these models. We fit these models to Bangladesh foreign exchange rate index from January 1999 to December 31, 2012. The return series of Bangladesh foreign exchange rate are leptokurtic, significant skewness, deviation from normality as well as the returns series are volatility clustering as well. We found that student t distribution into GARCH model improves the better performance to forecast the volatility for Bangladesh foreign exchange market. The traditional likelihood comparison showed that the importance of GARCH model in modeling of Bangladesh foreign market, but the modern nonparametric specification test found that RW, AR and the model with GARCH effect are still grossly mis-specified. All these imply that there is still a long way before we reach the adequate specification for Bangladesh exchange rate dynamics.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

This paper analyzes the connectedness with network among the major cryptocurrencies, the G7 stock indexes and the gold price over the coronavirus disease 2019 (COVID-19) pandemic period, in 2020.

This study used a multivariate approach proposed by Diebold and Yilmaz (2009, 2012 and 2014).

For a stock index portfolio, the results of static connectedness showed a higher independence between the stock markets during the COVID-19 crisis. It is worth noting that in general, cryptocurrencies are diversifiers for a stock index portfolio, which enable to reduce volatility especially in the crisis period. Dynamic connectedness results do not significantly differ from those of the static connectedness, the authors just mention that the Bitcoin Gold becomes a net receiver. The scope of connectedness was maintained after the shock for most of the cryptocurrencies, except for the Dash and the Bitcoin Gold, which joined a previous level. In fact, the Bitcoin has always been the biggest net transmitter of volatility connectedness or spillovers during the crisis period. Maker is the biggest net-receiver of volatility from the global system. As for gold, the authors notice that it has remained a net receiver with a significant increase in the network reception during the crisis period, which confirms its safe haven.

Overall, the authors conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the coronavirus while the Bitcoin Gold and Litecoin are the least receivers, leading to the conclusion that they can be diversifiers.

本文分析於2020年2019冠狀病毒病肆虐期間、主要的加密貨幣、七國集團 (G7) 股價指數與黃金價格三者之間在網絡上的連通性。

分析使用迪博爾德和耶爾馬茲 (Diebold and Yilmaz (2009, 2012, 2014)) 提出的多變量分析法。

就一個股票指數投資組合而言,靜態連結的結果顯示、在2019冠狀病毒病肆虐期間,股票市場之間有更高的獨立性。值得我們注意的是:一般來說,加密貨幣在股票指數投資組合起著多元化投資作用,這可減低不穩定性,尤其是在危機時期。動態連結的結果與靜態連結的結果沒有顯著的分別。我們剛提到、比特幣黃金已成為純接收者。除了處於先前水平的達世幣和比特幣黃金外,就大部分的加密貨幣而言,連通的範圍在衝擊後都得以維持。事實上,在這危機時期,比特幣一直是波動性連結或溢出的最大淨傳播者。掛單者 (Maker) 是從全球系統中出現的最大波動淨接收者。至於黃金,我們注意到在危機時期、它仍然是在網絡接收方面擁有顯著增長的淨接收者,這確認其為安全的避難所。

總的來說,我們的結論是:連通性被確認為取決於標誌著受廣泛傳播的冠狀病毒影響下的經濟和金融欠缺穩定的程度,而比特幣黃金和萊特幣則是最小的接收者,這帶出一個結論、就是:比特幣黃金和萊特幣、可以成為多元化投資項目。

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

Literature repeatedly complains about the lack of empirical data on the costs of cyber incidents within organizations. Simultaneously, managers urgently require transparent and reliable data in order to make well-informed and cost-benefit optimized decisions. The purpose of this paper is to (1) provide managers with differentiated empirical data on costs, and (2) derive an activity plan for organizations, the government and academia to improve the information base on the costs of cyber incidents.

The authors analyze the benchmark potential of costs within existing literature and conduct a large-scale interview survey with 5,000 German organizations. These costs are directly assignable to the most severe incident within the last 12 months, further categorized into attack types, cost items, employee classes and industry types. Based on previous literature, expert interviews and the empirical results, the authors draft an activity plan containing further research questions and action items.

The findings indicate that the majority of organizations suffer little to no costs, whereas only a small proportion suffers high costs. However, organizations are not affected equally since prevalence rates and costs according to attack types, employee classes, and other variables tend to vary. Moreover, the findings indicate that board members and IS/IT-managers show partly different response behaviors.

The authors present differentiated insights into the direct costs of cyber incidents, based on the authors' knowledge, this is the largest empirical survey in continental Europe and one of the first surveys providing in-depth cost information on German organizations.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.