• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.631-639
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• 2016

China has a long history of sheep (Ovis aries [O. aries]) breeding and an abundance of sheep genetic resources. Knowledge of the complete O. aries mitogenome should facilitate the study of the evolutionary history of the species. Therefore, the complete mitogenome of O. aries was sequenced and annotated. In order to characterize the mitogenomes of 3 Chinese sheep breeds (Altay sheep [AL], Shandong large-tailed sheep [SD], and small-tailed Hulun Buir sheep [sHL]), 19 sets of primers were employed to amplify contiguous, overlapping segments of the complete mitochondrial DNA (mtDNA) sequence of each breed. The sizes of the complete mitochondrial genomes of the sHL, AL, and SD breeds were 16,617 bp, 16,613 bp, and 16,613 bp, respectively. The mitochondrial genomes were deposited in the GenBank database with accession numbers KP702285 (AL sheep), KP981378 (SD sheep), and KP981380 (sHL sheep) respectively. The organization of the 3 analyzed sheep mitochondrial genomes was similar, with each consisting of 22 tRNA genes, 2 rRNA genes (12S rRNA and 16S rRNA), 13 protein-coding genes, and 1 control region (D-loop). The NADH dehydrogenase subunit 6 (ND6) and 8 tRNA genes were encoded on the light strand, whereas the rest of the mitochondrial genes were encoded on the heavy strand. The nucleotide skewness of the coding strands of the 3 analyzed mitogenomes was biased toward A and T. We constructed a phylogenetic tree using the complete mitogenomes of each type of sheep to allow us to understand the genetic relationships between Chinese breeds of O. aries and those developed and utilized in other countries. Our findings provide important information regarding the O. aries mitogenome and the evolutionary history of O. aries inside and outside China. In addition, our results provide a foundation for further exploration of the taxonomic status of O. aries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1194-1201
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• 2003

Measurements of network traffic have shown that self-similarity is a ubiquitous phenomenon spanning across diverse network environments. We have advance the framework of multiple time scale congestion control and show its effectiveness at enhancing performance for fast TCP prototype control. In this paper, we extend the fast TCP prototype control framework to window-based congestion control, in particular, TCP. This is performed by interfacing TCP with a large time scale control module which adjusts the aggressiveness of bandwidth consumption behavior exhibited by TCP as a function of "large time scale" network state. i.e., conformation that exceeds the horizon of the feedback loop as determined by RTT. Performance evaluation of fast TCP prototype is facilitated by a simulation bench-mark environment which is based on physical modeling of self-similar traffic. We explicate out methodology for discerning and evaluating the impact of changes in transport protocols in the protocol stack under self-similar traffic conditions. We discuss issues arising in comparative performance evaluation under heavy-tailed workload. workload.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6B
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• pp.575-583
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• 2004

In this paper, we extend the multiple time scale control framework to the window-based congestion control, in particular, such as the TCP. This is performed by interfacing the TCP with a large time scale control module which adjusts the aggressiveness of the bandwidth consumption behavior exhibited by the TCP as a function of large time scale Self-Similar network state. i.e., conformation that exceeds the horizon of the feedback loop as determined by the RTT. How to effectively utilize such an information-due to its probabilistic nature, dispersion over the multiple time scales, and affection on the top of the existing window-based congestion controls-is a non-trivial problem. The evaluation performance of the multiple time scale TCP is facilitated by a simulation of the bench-mark environment which is based on the physical modeling of a self-similar traffic. We explicate our methodology for discerning and evaluating the impact of changes in transport protocols in the protocol stack under the self-similar traffic conditions. We discuss issues arising in the comparative performance evaluation under heavy-tailed workloads.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.357-365
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• 2009

To effectively exploit the underlying network bandwidth while maximizing user perceivable QoS, mandatory to make proper estimation on packet loss and queuing delay of the underling network. This issue is further emphasized in wireless network environment where network bandwidth is scarce resource. In this work, we focus our effort on developing performance model for wireless network. We collect packet trace from actually wireless network environment. We find that packet count process and bandwidth process in wireless environment exhibits long range property. We extract key performance parameters of the underlying network traffic. We develop an analytical model for buffer overflow probability and waiting time. We obtain the tail probability of the queueing system using Fractional Brown Motion (FBM). We represent average queuing delay from queue length model. Through our study based upon empirical data, it is found that our performance model well represent the physical characteristics of the IEEE 802.11b network traffic.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.871-883
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• 2015

Vector autoregressive (VAR) models in high dimension suffer from noisy estimates, unstable predictions and hard interpretation. Consequently, the sparse vector autoregressive (sVAR) model, which forces many small coefficients in VAR to exactly zero, has been suggested and proven effective for the modeling of high dimensional time series data. This paper studies coupling measures to select non-zero coefficients in sVAR. The basic idea based on the simulation study reveals that removing the effect of other variables greatly improves the performance of coupling measures. sVAR model coefficients are asymmetric; therefore, asymmetric coupling measures such as Granger causality improve computational costs. We propose two asymmetric coupling measures, filtered-cross-correlation and filtered-Granger-causality, based on the filtered residuals series. Our proposed coupling measures are proven adequate for heavy-tailed and high order sVAR models in the simulation study.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.9-18
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• 2022

This research examines the power of bootstrap two-sample test, and compares it with the powers of two-sample t-test and Wilcoxon rank sum test, through simulation. For simulation work, a positively skewed and heavy tailed distribution was selected as a population distribution, the chi-square distributions with three degrees of freedom, χ²₃. For two independent samples, the fist sample was selected from χ²₃. The second sample was selected independently from the same χ²₃ as the first sample, and calculated d+ax for each sampled value x, a randomly selected value from χ²₃. The d in d+ax has from 0 to 5 by 0.5 interval, and the a has from 1.0 to 1.5 by 0.1 interval. The powers of three methods were evaluated for the sample sizes 10,20,30,40,50. The null hypothesis was the two population medians being equal for Bootstrap two-sample test and Wilcoxon rank sum test, and the two population means being equal for the two-sample t-test. The powers were obtained using r program language; wilcox.test() in r base package for Wilcoxon rank sum test, t.test() in r base package for the two-sample t-test, boot.two.bca() in r wBoot pacakge for the bootstrap two-sample test. Simulation results show that the power of Wilcoxon rank sum test is the best for all 330 (n,a,d) combinations and the power of two-sample t-test comes next, and the power of bootstrap two-sample comes last. As the results, it can be recommended to use the classic inference methods if there are widely accepted and used methods, in terms of time, costs, sometimes power.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1054-1061
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• 2015

Navigation accuracy, integrity, and safety of commercial Unmanned Aerial Vehicle (UAV) is becoming crucial as utilization of UAV in commercial applications is expected to increase. Recently, the concept of Local-Area Differential GNSS (LADGNSS) which can provide navigation accuracy and integrity of UAV was proposed. LADGNSS can provide differential corrections and separation distances for precise and safe operation of the UAV. In order to derive separation distances between UAVs, modeling of Flight Technical Error (FTE) is required. In most cases, FTE for civil aircraft has been assumed to be zero-mean normal distribution. However, this assumption can cause overconservatism especially for UAV, because UAV may use control and navigation equipments in wider performance range and follow more diverse path than standard airway for civil aircraft. In this research, flight experiments were carried out to understand the characteristics of FTE distribution. Also, this paper proposes to use Johnson distribution which can better describe heavy-tailed and skewed FTE data. Futhermore, Kolmogorov-Smirnov and Anderson-Darling tests were conducted to evaluate the goodness of fit of Johnson model.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.5
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• pp.37-45
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• 2004

As Internet technologies are mature, many new applications that are different characteristics are emerging. Recently we see wide use of P2P(Peer to Peer) applications of which traffic shows different statistical characteristics compared with traditional application such as web(HTTP) and FTP(File Transfer Protocol). In this paper, we measured subscriber network of KT(Korea Telecom) to analyze P2P traffic characteristics. We show flow characteristics of measured traffic. We also estimate Hurst parameter of P2P traffic and compare self-similarity with web traffic. Analysis results indicate that P2P traffic is much bustier than web traffic and makes both upstream traffic and downstream traffic be symmetric. To predict parameters related QoS such as packet loss and delays we model P2P traffic using two self-similar traffic models and predict both loss probability and mm delay then compare their accuracies. With simulation we show that the self-similar traffic models we derive predict the performance of P2P traffic accurately and thus when we design a network or evaluate its performance, we can use the P2P traffic model as reference input traffic.