• Structural Engineering and Mechanics
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• v.74 no.1
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• pp.55-67
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• 2020

The common practice to predict the characteristic structural load effects (LEs) in long reference periods is to employ the extreme value theory (EVT) for building limit distributions. However, most applications ignore that LEs are driven by multiple loading events and thus do not have the identical distribution, a prerequisite for EVT. In this study, we propose the composite extreme value modeling approach using clustering to (a) cluster initial blended samples into finite identical distributed subsamples using the finite mixture model, expectation-maximization algorithm, and the Akaike information criterion; (b) combine limit distributions of subsamples into a composite prediction equation using the generalized Pareto distribution based on a joint threshold. The proposed approach was validated both through numerical examples with known solutions and engineering applications of bridge traffic LEs on a long-span bridge. The results indicate that a joint threshold largely benefits the composite extreme value modeling, many appropriate tail approaching models can be used, and the equation form is simply the sum of the weighted models. In numerical examples, the proposed approach using clustering generated accurate extrema prediction of any reference period compared with the known solutions, whereas the common practice of employing EVT without clustering on the mixture data showed large deviations. Real-world bridge traffic LEs are driven by multi-events and present multipeak distributions, and the proposed approach is more capable of capturing the tendency of tailed LEs than the conventional approach. The proposed approach is expected to have wide applications to general problems such as samples that are driven by multiple events and that do not have the identical distribution.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.249-261
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• 2021

To tranquilize the devastating impact of unnecessary risk-taking behavior of banks towards the economy for maximizing their profits that usually arises due to widely known 'moral-hazard' problem originating from market competition and intensified by bank's limited liability, the banking system is strongly monitored across all countries of the world. The goal of controlling would become more feasible if there exist some self-discipline and motivations which could safeguard the banks' charter value through the mechanism of market discipline. Therefore, our study is aimed to scrutinize the relation between market discipline and charter value of local commercial banks that are registered on the Pakistan Stock Exchange by analyzing a balanced panel data from the year 2007 to 2019. Deposit growth, interbank deposits, and subordinate debt are taken as proxies to measure market discipline whereas Tobin's Q theory is applied for calculating the charter value. Generalized Least Square Regression with Fixed Effect Model is used for evaluation. The outcomes reveal that in the existence of control variables, all proxies of market discipline have a significant positive impact on bank charter value. Our research has important policy implications for monitoring and supervising financial intermediaries for their stability and soundness by offsetting the complications of moral-hazard in the financial systems.

• Transactions of Materials Processing
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• v.26 no.5
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• pp.293-299
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• 2017

In this paper, finite element stamping analysis was carried out for the front lower arm to examine the applicability of solid element with damage theory to predict shear fracture phenomena induced by sheared edge as well as deformation mechanisms. Mechanical properties related to deformation and damage theory were determined from tensile test. Shear fracture was predicted by normalized Cockcroft-Latham model with initial imposition of the damage value along the sheared edge. Simulation results illustrated that the analysis with solid element and damage theory predicted edge profile, strain distribution, and forming load more accurately than the analysis with shell element. Simulation with solid element can also predict the shear fracture more exactly comparing to analysis with shell element and forming limit curve.

• Wind and Structures
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• v.9 no.3
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• pp.179-191
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• 2006

Parent wind data are often acknowledged to fit a Weibull probability distribution, implying that wind epoch maxima should fall into the domain of attraction of the Gumbel (Type I) extreme value distribution. However, observations of wind epoch maxima are not fitted well by this distribution and a Generalised Extreme Value (GEV) analysis leading to a Type III fit empirically appears to be better. Thus there is an apparent paradox. The reasons why advocates of the GEV approach seem to prefer it are briefly summarised. This paper gives a detailed analysis of the errors involved when the GEV is fitted to epoch maxima of Weibull origin. It is shown that the results in terms of the shape parameter are an artefact of these errors. The errors are unavoidable with the present sample sizes. If proper significance tests are applied, then the null hypothesis of a Type I fit, as predicted by theory, will almost always be retained. The GEV leads to an unacceptable ambiguity in defining design loads. For these reasons, it is concluded that the GEV approach does not seem to be a sensible option.

• Journal of Distribution Science
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• v.13 no.8
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• pp.5-14
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• 2015

Purpose - This study undertakes to examine the automotive trade structure between Korea and Russia to accelerate economic cooperation and pinpoint trade discrepancies to increase trade volume through improved policies, eventually finding ways for trade expansion. Research design, data, and methodology - To analyze trade decision factors for both countries, the Index of trade specialization invented by trade specialization theory, is used. Although specific factors should materialize in the trade decision analysis, realistically, concrete explanations are difficult as many unsolved factors are involved as well as their complexities Results - First, to assess comparative market competitiveness, the Index describes A value/B value, representing the Korean versus the Russia market share and the Korean market share versus the world. Second, the index shows that Korea is taking comparative advantage of its export specialization. Third, the RCA indices show considerable improvement compared to 2000. Conclusions - This research used a quantitative approach to examine trade specialization and examined a comparative advantage index of market share to see how inter-trade relations have changed over the past 10 years.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.75-94
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• 2023

The growing trend of cyber risk has put forward the importance of cyber risk management. Cyber risk is defined as an accidental or intentional risk related to information and technology assets. Although cyber risk is a subset of operational risk, it is reported to be handled differently from operational risk due to its different features of the loss distribution. In this study, we aim to detect the characteristics of cyber loss and find a suitable model by measuring value at risk (VaR). We use the loss distribution approach (LDA) and the time series model to describe cyber losses of financial and non-financial business sectors, provided in SAS^® OpRisk Global Data. Peaks over threshold (POT) method is also incorporated to improve the risk measurement. For the financial sector, the LDA and GARCH model with POT perform better than those without POT, respectively. The same result is obtained for the non-financial sector, although the differences are not significant. We also build a two-dimensional model reflecting the dependence structure between financial and non-financial sectors through a bivariate copula and check the model adequacy through VaR.

• Journal of Astronomy and Space Sciences
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• v.19 no.1
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• pp.1-6
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• 2002

Recently, one interesting possibility is proposed that a magnetar can be a progenitor of short and hard gamma-ray bursts (GRBs). If this is true, one may expect that the short and hard GRBs, at least some of GRBs in this class, are distributed in the Euclidean space and that the angular position of these GRBs is correlated with galaxy clusters. Even though it is reported that the correlation is statistically marginal, the observed value of < $V/V_{max}$ > deviates from the Euclidean value. The latter fact is often used as evidence against a local extragalactic origin for short GRB class. We demonstrate that GRB sample of which the value of < $V/V_{max}$ > deviates from the Euclidean value can be spatially confined within the low value of z. We select very short bursts (TgO < 0.3 sec) from the BATSE 4B catalog. The value of < $V/V_{max}$ > of the short bursts is 0.4459. Considering a conic-beam and a cylindrical beam for the luminosity function, we deduce the corresponding spatial distribution of the GRB sources. We also calculate the fraction of bursts whose redshifts are larger than a certain redshift z', i.e. f>z'. We find that GRBs may be distributed near to us, despite the non-Euclidean value of < $V/V_{max}$ >. A broad and uniform beam pattern seems compatible with the magnetar model in that the magnetar model requires a small $z_{max}$.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.753-771
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• 2016

Value at Risk (VaR) is widely used as an important tool for risk management of financial institutions. In this paper we discuss estimation and back testing for VaR of the portfolio composed of KOSPI, Dow Jones, Shanghai, Nikkei indexes. The copula functions are adopted to construct the multivariate distributions of portfolio components from marginal distributions that combine extreme value theory and GARCH models. Volatility models with t distribution of the error terms using Gaussian, t, Clayton and Frank copula functions are shown to be more appropriate than the other models, in particular the model using the Frank copula is shown to be the best.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.305-315
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• 2021

This article aims to explore the contribution of social media value to a company's financial performance in a digital environment economy since the awareness of companies and investors in the use of social media opens up new mechanisms for disseminating information. Quantitative method is used in this study with Multivariate Analysis of Variance as the analysis tool. The data used is secondary data gathered from Indonesia Stock Exchange (IDX) using 308 companies as samples. In the multivariate test, four kinds of multivariate significance tests were carried out, namely Pillai Trace, Wilk Lambda, Hotelling's Trace, and Roy's Largest Root. It was found that social media value has a small contribution in the difference of the level of profitability and the value of the company in Indonesia, but it doesn't have a contribution to the difference of the level of liquidity. The contribution was an implication of online Word of Mouth (WOM) motives which are interrelated with signal theory and as additional information for investors in relation to single-person decision theory. This study provides an insight into the importance of social media management considering that the world of digital economy will continue to develop, so companies in Indonesia need to take advantage of these opportunities.

• The Journal of Information Systems
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• v.29 no.2
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• pp.131-147
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• 2020

Purpose The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city. Design/methodology/approach The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance. Findings This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.