• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.267-277
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• 2016

This paper considers we consider the estimation of copula parameters based on residuals in stochastic regression models. We prove that a semiparametric estimator using residual empirical distributions is consistent under some conditions and apply the results to the copula-ARMA model. We provide simulation results for illustration.

• Wind and Structures
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• v.25 no.3
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• pp.261-282
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• 2017

The probabilistic information of directional extreme wind speeds is important for precisely estimating the design wind loads on structures. A new joint probability distribution model of directional extreme wind speeds is established based on observed wind-speed data using multivariate extreme value theory with the t-Copula function in the present study. At first, the theoretical deficiencies of the Gaussian-Copula and Gumbel-Copula models proposed by previous researchers for the joint probability distribution of directional extreme wind speeds are analysed. Then, the t-Copula model is adopted to solve this deficiency. Next, these three types of Copula models are discussed and evaluated with Spearman's rho, the parametric bootstrap test and the selection criteria based on the empirical Copula. Finally, the extreme wind speeds for a given return period are predicted by the t-Copula model with observed wind-speed records from several areas and the influence of dependence among directional extreme wind speeds on the predicted results is discussed.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.875-887
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• 2021

Value at Risk (VaR) is the most popular measure for market risk. In this paper, we consider the VaR estimation of portfolio consisting of a variety of assets based on multivariate copula model known as vine copula. In particular, sparse vine copula which penalizes too many parameters is considered. We show in the simulation study that sparsity indeed improves out-of-sample forecasting of VaR. Empirical analysis on 60 KOSPI stocks during the last 5 years also demonstrates that sparse vine copula outperforms regular copula model.

• Wind and Structures
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• v.30 no.2
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• pp.141-153
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• 2020

Probabilistic information regarding directional extreme wind speeds is important for the precise estimation of the design wind loads on structures. A joint probability distribution model of directional extreme typhoon wind speeds is established using Monte Carlo simulation and empirical copula function to fully consider the correlations of extreme typhoon wind speeds among the different directions. With this model, a procedure for estimating directional extreme wind speeds for given return periods, which ensures that the overall risk is distributed uniformly by direction, is established. Taking 5 typhoon-prone cities in China as examples, the directional extreme typhoon wind speeds for given return periods estimated by the present method are compared with those estimated by the method proposed by Cook and Miller (1999). Two types of directional factors are obtained based on Cook and Miller (1999) and the UK standard's drafting committee (Standard B, 1997), and the directional risks for the given overall risks are discussed. The influences of the extreme wind speed correlations in the different directions and the simulated typhoon wind speed sample sizes on the estimated extreme wind speeds for a given return period are also discussed.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.445-459
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• 2014

This paper investigates the dependence structure of Korean financial markets (stock, foreign exchange (FX) rates and bond) using copula-GARCH and dynamic conditional correlation (DCC) models. We examine GJR-GARCH with skewed elliptical distributions and four copulas (Gaussian, Student's t, Clayton and Gumbel) to model dependence among returns, and then employ DCC model to describe system-wide correlation dynamics. We analyze the daily returns of KOSPI, FX (WON/USD) and KRX bond index (Gross Price Index) from $2^{nd}$ May 2006 to $30^{th}$ June 2014 with 2,063 observations. Empirical result shows that there is significant asymmetry and fat-tail of individual return, and strong tail-dependence among returns, especially between KOSPI and FX returns, during the 2008 Global Financial Crisis period. Focused only on recent 30 months, we find that the correlation between stock and bond markets shows dramatic increase, and system-wide correlation wanders around zero, which possibly indicates market tranquility from a systemic perspective.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1615-1625
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• 2013

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio-temporal correlations using the theory of copula. The sampling approach captures the complex spatio-temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.

• Journal of Korea Port Economic Association
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• v.39 no.1
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• pp.115-129
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• 2023

This study analyzed the risk spillover of BDI on shipping company stock prices through the Copula-CoVaR method based on daily data from January 4, 2010, to October 31, 2022. The main empirical analysis results and policy implications are as follows. First, copula results showed that there was a weak dependence between BDI and shipping company stock prices, and PAN, KOR, and YEN were selected as the most fitting model for dynamic Student-t copula, HMM was selected as the rotated Gumbel copula, and KSS was selected as the best model. Second, in the results of CoVaR, it was confirmed that the upside (downside) CoVaR was significantly different from the upside (downside) VaR in all shipping companies. This means that BDI has a significant risk spillover on shipping companies. In addition, as for the risk spillover, the downside risk is generally lower than the upside risk, so the downside and upside risk spillover were found to be asymmetrical. Therefore, policymakers should strengthen external risk supervision and establish differentiated policies suitable for domestic conditions to prevent systematic risks from BDI shocks. And investors should reflect external risks from BDI fluctuations in their investment decisions and construct optimal investment portfolios to avoid risks. On the other hand, investors propose that the investment portfolio should be adjusted in consideration of the asymmetric characteristics of up and down risks when making investment decisions.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.193-225
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• 2006

The purpose of this paper is to analyze contagion in Asian foreign exchange markets using Extreme Value Theory and Copula. Our application deals with asymptotic dependence of daily exchange rate return for a sample of eight countries over period 1997.1.1-2005.4.13. The empirical results are summarized as follows. Firstly, Gumbel Copula is a good model to our data according to the value of AIC. Secondly, the extremal dependence between East Asian crisis countries became lower in the post crisis period than the crisis period. Thirdly, It seemed that high extremal dependence exists between East Asian countries with Singapore. Fourthly, the tail dependence between Indonesia, Malaysia, Thailand, Philippine became higher in the crisis period than the total period and post crisis period. Fifthly, the fact that the extremal dependence between Korea and Indonesia, Malaysia, Thailand, Philippine did not increase during the Asian Financial Crisis showed that the contagion effect was not the reason of the Korea's Fiancial Crisis. Sixthly, the extremal dependence between Asian exchange markets was not very high while comparing with the European exchange markets.