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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Applied Statistics
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 29, Issue 5 - Aug 2016
Volume 29, Issue 4 - Jun 2016
Volume 29, Issue 3 - Apr 2016
Volume 29, Issue 2 - Feb 2016
Volume 29, Issue 1 - Feb 2016
Selecting the target year
A recent overview on financial and special time series models
Hwang, S.Y. ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 1~12
DOI : 10.5351/KJAS.2016.29.1.001
Contrasted with the standard linear ARMA models, financial time series exhibits non-standard features such as fat-tails, non-normality, volatility clustering and asymmetries which are usually referred to as "stylized facts" in financial time series context (Terasvirta, 2009). We are accordingly led to ad hoc models (apart from ARMA) to accommodate stylized facts (Andersen et al., 2009). The paper aims to give a contemporary overview on financial and special time series models based on the recent literature and on the author`s publications. Various models are illustrated including asymmetric models, integer valued models, multivariate models and high frequency models. Selected statistical issues on the models are discussed, bringing some perspectives to the future works in this area.
The impacts of high speed train on the regional economy of Korea
Park, Mi Suk ; Kim, Yongku ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 13~25
DOI : 10.5351/KJAS.2016.29.1.013
High-speed railway (Korea Train Express) has had a deep impact on the regional economy of Korea. Current high-speed rail research is mostly theoretical, there is a lack of quantitative research using a precise algorithm to study the effect of high-speed railway on the regional economy. This paper analyses the influence of high-speed rail on the regional economy, with a focus on the Daegu area. Quantitative analysis using department store indexes and regional medical records is performed to calculate the economic influence of high-speed rail. The result shows that high-speed railway effects the regional economy though regional consumption growth and medical care trends.
Adaptive lasso in sparse vector autoregressive models
Lee, Sl Gi ; Baek, Changryong ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 27~39
DOI : 10.5351/KJAS.2016.29.1.027
This paper considers variable selection in the sparse vector autoregressive (sVAR) model where sparsity comes from setting small coefficients to exact zeros. In the estimation perspective, Davis et al. (2015) showed that the lasso type of regularization method is successful because it provides a simultaneous variable selection and parameter estimation even for time series data. However, their simulations study reports that the regular lasso overestimates the number of non-zero coefficients, hence its finite sample performance needs improvements. In this article, we show that the adaptive lasso significantly improves the performance where the adaptive lasso finds the sparsity patterns superior to the regular lasso. Some tuning parameter selections in the adaptive lasso are also discussed from the simulations study.
A modified Lee-Carter model based on the projection of the skewness of the mortality
Lee, Hangsuck ; Baek, Changryong ; Kim, Jihyeon ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 41~59
DOI : 10.5351/KJAS.2016.29.1.041
There have been continuous improvements in human life expectancy. Life expectancy is as a key factor in an aging population and can wreak severe damage on the financial integrity of pension providers. Hence, the projection of the accurate future mortality is a critical point to prevent possible losses to pension providers. However, improvements in future mortality would be overestimated by a typical mortality projection method using the Lee-Carter model since it underestimates the mortality index
. This paper suggests a mortality projection based on the projection of the skewness of the mortality versus the typical mortality projection of the Lee-Carter model based on the projection of the mortality index,
. The paper shows how to indirectly estimate future t trend with the skewness of the mortality and compares the results under each estimation method of the mortality index,
. The analysis of the results shows that mortality projection based on the skewness presents less improved mortality at an elderly ages than the original projection.
Wild bootstrap Ljung-Box test for autocorrelation in vector autoregressive and error correction models
Lee, Myeongwoo ; Lee, Taewook ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 61~73
DOI : 10.5351/KJAS.2016.29.1.061
We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.
Dynamic analysis of financial market contagion
Lee, Hee Soo ; Kim, Tae Yoon ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 75~83
DOI : 10.5351/KJAS.2016.29.1.075
We propose methodology to analyze the dynamic mechanisms of financial market contagion under market integration using a biological contagion analytical approach. We employ U-statistic to measure market integration, and a dynamic model based on an error correction mechanism (single equation error correction model) and latent factor model to examine market contagion. We also use quantile regression and Wald-Wolfowitz runs test to test market contagion. This methodology is designed to effectively handle heteroscedasticity and correlated errors. Our simulation results show that the single equation error correction model fits well with the linear regression model with a stationary predictor and correlated errors.
Comparison of realized volatilities reflecting overnight returns
Cho, Soojin ; Kim, Doyeon ; Shin, Dong Wan ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 85~98
DOI : 10.5351/KJAS.2016.29.1.085
This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.
Bayesian inference on multivariate asymmetric jump-diffusion models
Lee, Youngeun ; Park, Taeyoung ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 99~112
DOI : 10.5351/KJAS.2016.29.1.099
Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.
Joint model of longitudinal data with informative observation time and competing risk
Kim, Yang-Jin ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 113~122
DOI : 10.5351/KJAS.2016.29.1.113
Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients` data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.
Comparison between homogeneity test statistics for panel AR(1) model
Lee, Sung Duck ; Kim, Sun Woo ; Jo, Na Rae ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 123~132
DOI : 10.5351/KJAS.2016.29.1.123
We can achieve the principle of parsimony and efficiency if homogeneity for panel time series model is satisfied. We suggest a Rao test statistic and a Wald test statistic for the test of homogeneity for panel AR(1) and derived the limit distribution. We performed a simulation to examine statistics with the same chisquare distribution when number of the individual is small and in common with large. We also simulated to compare the empirical power of the statistics in a small panel. In application, we fit panel AR(1) model using regional monthly economical active population data and test homogeneity for panel AR(1). It is satisfied homogeneity, so it could be fitted AR(1) using the sample mean at the time point. We also compare the power of prediction between each individual and pooled model.
A study on parsimonious periodic autoregressive model
Lee, Jiho ; Seong, Byeongchan ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 133~144
DOI : 10.5351/KJAS.2016.29.1.133
This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.
Comparison of forecasting models of disease occurrence due to the weather in elderly patients
Lee, Seonjae ; Yeo, In-Kwon ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 145~155
DOI : 10.5351/KJAS.2016.29.1.145
In this paper, we compare forecasting models for disease occurrences in elderly patients due to the weather. For the analysis, the medical data of aged patients released from Health Insurance Review and the weather data of the Korea Meteorological Administration are weekly and regionally merged. The ARMAX model, the VARMAX model and the TSCS regression model are considered to analyze the number of weekly occurrences of some diseases attributable to climate conditions. These models are compared with MSE, MAPE, and MAE criteria.
A comparison study on regression with stationary nonparametric autoregressive errors
Yu, Kyusang ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 157~169
DOI : 10.5351/KJAS.2016.29.1.157
We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.
Comparison of semiparametric methods to estimate VaR and ES
Kim, Minjo ; Lee, Sangyeol ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 171~180
DOI : 10.5351/KJAS.2016.29.1.171
Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.
Comparison of methods of approximating option prices with Variance gamma processes
Lee, Jaejoong ; Song, Seongjoo ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 181~192
DOI : 10.5351/KJAS.2016.29.1.181
We consider several methods to approximate option prices with correction terms to the Black-Scholes option price. These methods are able to compute option prices from various risk-neutral distributions using relatively small data and simple computation. In this paper, we compare the performance of Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method of using Normal inverse gaussian distribution, and an asymptotic method of using nonlinear regression through simulation experiments and real KOSPI200 option data. We assume the variance gamma model in the simulation experiment, which has a closed-form solution for the option price among the pure jump
processes. As a result, we found that methods to approximate an option price directly from the approximate price formula are better than methods to approximate option prices through the approximate risk-neutral density function. The method to approximate option prices by nonlinear regression showed relatively better performance among those compared.
A study on electricity demand forecasting based on time series clustering in smart grid
Sohn, Hueng-Goo ; Jung, Sang-Wook ; Kim, Sahm ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 193~203
DOI : 10.5351/KJAS.2016.29.1.193
This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).
Seasonal adjustment in Korean economic statistics and major issues
Lee, Geung-Hee ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 205~220
DOI : 10.5351/KJAS.2016.29.1.205
Seasonal adjustment is useful to provide a better understanding of underlying trends in Korean economic statistics. The seasonal component also includes calendar effects such as Seol and Chuseok. Most popular seasonal adjustment methods are X-12-ARIMA of the U.S. Bureau of the Census and TRAMO-SEATS of the Bank of Spain. Statistics Korea and the Bank of Korea compile seasonally adjusted series of several Korean economic statistics. This paper illustrates basic principles for seasonal adjustment and the current status of seasonal adjustment in Korea based on previous research. In addition, several issues on seasonal adjustment are addressed.
Stock return volatility based on intraday high frequency data: double-threshold ACD-GARCH model
Chung, Sunah ; Hwang, S.Y. ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 221~230
DOI : 10.5351/KJAS.2016.29.1.221
This paper investigates volatilities of stock returns based on high frequency data from stock market. Incorporating the price duration as one of the factors in volatility, we employ the autoregressive conditional duration (ACD) model for the price duration in addition to the GARCH model to analyze stock volatilities. A combined ACD-GARCH model is analyzed in which a double-threshold is introduced to accommodate asymmetric features on stock volatilities.
New seasonal moving average filters for X-13-ARIMA
Shim, Kyuho ; Kang, Gunseog ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 231~242
DOI : 10.5351/KJAS.2016.29.1.231
X-13-ARIMA (a popular time series analysis software) provides
moving average filters for seasonal adjustment. However, there has been questions on their performance and the need for new filters is a constant topic due to Korean economic time series often containing higher irregularity and more various seasonality than other countries. In this study, two newly developed seasonal moving average filters,
, are introduced. New filters were implemented in X-13-ARIMA and applied to 15 economic time series to demonstrate their suitability and reliability. The result shows that some series are more stable when using new seasonal moving average filters. More accurate time series analyses would be possible if newly proposed filters are used together with existing filters.
A study on composite precedence indices focusing on Jeju
Kim, Kye Chul ; Kim, Myung Joon ; Kim, Yeong-Hwa ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 243~255
DOI : 10.5351/KJAS.2016.29.1.243
The developed composite index has limits to estimate and predict economic status due to economic pattern change and the response change of explanatory variables. A higher precedence individual indicators should be selected to predict the future accurately. In this study, effectiveness of Jeju Island precedence indicators consists of constituents in the area, the consumer price index, services production index, mining and manufacturing production index. The average temperature of Seogwipo and credit card purchase amount is reviewed as an economic turning point consideration and time lag correlation analysis with real data. In addition, we suggest the proper reference cycle in Jeju composite precedence index and evaluate the configuration in leading indicators for Jeju by comparing national economic indicators. Based on the derived results, the current problems of Jeju Island precedence indicators will be illustrated and the improvement methods to estimate a regional composite index will be suggested.
Estimation for random coefficient autoregressive model
Kim, Ju Sung ; Lee, Sung Duck ; Jo, Na Rae ; Ham, In Suk ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 257~266
DOI : 10.5351/KJAS.2016.29.1.257
Random Coefficient Autoregressive models (RCA) have attracted increased interest due to the wide range of applications in biology, economics, meteorology and finance. We consider an RCA as an appropriate model for non-linear properties and better than an AR model for linear properties. We study the methods of RCA parameter estimation. Especially we proposed the special case that an random coefficient
has the initial value
in the RCA model. In practical study, we estimated the parameters and compared Prediction Error Sum of Squares (PRESS) criterion between AR and RCA using Korean Mumps data.
Residual-based copula parameter estimation
Na, Okyoung ; Kwon, Sunghoon ;
Korean Journal of Applied Statistics, volume 29, issue 1, 2016, Pages 267~277
DOI : 10.5351/KJAS.2016.29.1.267
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.