• KDI Journal of Economic Policy
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• v.32 no.3
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• pp.71-99
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• 2010

The concept of CoVaR introduced by Adrian and Brunnermeier (2009) is a useful tool to measure the risk spillover effect. It can capture the risk contribution of each institution to overall systemic risk. While Adrian and Brunnermeier rely on the quantile regression method in the estimation of CoVaR, we propose a new estimation method using parametric distribution functions such as bivariate normal and $S_U$-normal distribution functions. Based on our estimates of CoVaR for Korean banking industry, we investigate the practical usefulness of CoVaR for a systemic risk measure, and compare the estimation performance of each model. Empirical results show that bank makes a positive contribution to system risk. We also find that quantile regression and normal distribution models tend to considerably underestimate the CoVaR (in absolute value) compared to $S_U$-normal distribution model, and this underestimation becomes serious when the crisis in a financial system is assumed.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1531-1541
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• 2013

This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.

• Women's Health Nursing
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• v.28 no.3
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• pp.187-196
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• 2022

Purpose: The purpose of this study was to investigate factors related to the levels of health-related quality of life (HRQoL) in elderly women based on Wilson and Cleary's HRQoL model. Methods: This study analyzed data from the eighth Korea National Health and Nutrition Examination Survey 2019 on 868 women over the age of 65 years. Based on the HRQoL model, parameters were categorized as personal, environmental, and physiological characteristics; symptom status; functional status; and perception of health status. The data were analyzed by quantile regression. Results: The overall level of HRQoL was 0.87. Factors related to HRQoL in the 10% quantile were higher education level, higher economic status, economic activity, more walking days, fewer diseases, lower stress, less activity limitation, and higher perceived health status. Factors related to the 25% quantile of HRQoL were more walking days, fewer diseases, less activity limitation, and higher perceived health status. Factors related to the 50% quantile were age, economic activity, more walking days, fewer disease, lower stress, less activity limitation, and higher perceived health status. Factors related to the 75% quantile of HRQoL were smoking, more walking days, fewer diseases, lower stress, less activity limitation, and higher perceived health status. Conclusion: While differing parameters were identified according to the level of HRQoL of elderly women in Korea, there were five common factors. Interventions that focus on increasing walking, mitigating diseases, stress, and activity limitations, and improving perceived health status can improve HRQoL.

• Management & Information Systems Review
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• v.37 no.1
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• pp.155-175
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• 2018

Lots of previous researches on determinants of apartment prices in Korea consider spatial dependence while few studies regard endogeneity of spatial lag by adding a spatial lag to an OLS regression. Thus, this study intends to include this spatial lag in its analysis of determinants of apartment price in Busan by using a two-stage quantile regression. The empirical results are : the coefficient of spatial lag variable is more than 0.5 and is statistically significant at 1% level. From this result we can confirm that the effect of the price of nearby apartment on that of another apartment is very big. We also find that apartment buyers prefer larger size, height in both the total floors and living floor, south-facing living room with a ocean view, and proximity to metros, high school and coast. Unlike our expectation, however, mountain view is less favored than building view, which we can guess is because apartments with mountain views are mostly located in the low-priced apartment area where some of their living rooms face north. Quantile regression also explains the effect of hedonic characteristics on apartment price better than OLS estimation. For instance, the effect of south facing living room variable on the price is twice larger in high-price apartments than in low-price counterparts. And the effect of vicinity to the coast or the ocean is ten times bigger in high priced apartments.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.859-871
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• 2003

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L$_1$ (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L$_1$ as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.

• Human Ecology Research
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• v.60 no.2
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• pp.289-302
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• 2022

This study examined the effect of private educational expenditure on adolescent depression and somatic symptoms. The sample comprised 2,589 first-grade middle-school students who completed the 2018 Korea Children and Youth Panel Survey. Data were analyzed using ANOVA (the generalized linear model), multiple regression, and quantile regression analysis. The principal results were as follows. First, 15.15% of adolescents reported depression symptoms, and 15.57% reported somatic symptoms. Second, levels of depression were significantly different among classes with a different level of private educational expenditure. Third, depression level was significantly negatively associated with private educational expenditure, in that the higher the private educational expenditure, the lower the depression level. Fourth, the effect of private educational expenditure on adolescent depression was significant at the 70~90th quantile regression, suggesting that private educational expenditure was associated with a higher level of depression symptoms. The results indicate that private education was viewed as a consumption commodity rather than a complementary educational practice or investment in human capital. Private education as a commodity might induce the highly developed and costly private education market. In turn, there is an increased financial burden for education at one end of the social-economic continuum and depression caused by relative deprivation at the other end.

• Journal of Coastal Disaster Prevention
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• v.4 no.3
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• pp.119-131
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• 2017

Global warming under the influence of climate change and its direct impact on glacial and sea level are known issue. However, there is a lack of research on an indirect impact of climate change such as coastal structure design which is mainly based on a frequency analysis of water level under the stationary assumption, meaning that maximum sea level will not vary significantly over time. In general, stationary assumption does not hold and may not be valid under a changing climate. Therefore, this study aims to develop a novel approach to explore possible distributional changes in annual maximum sea levels (AMSLs) and provide the estimate of design water level for coastal structures using a multiple non-crossing quantile regression based nonstationary frequency analysis within a Bayesian framework. In this study, 20 tide gauge stations, where more than 30 years of hourly records are available, are considered. First, the possible distributional changes in the AMSLs are explored, focusing on the change in the scale and location parameter of the probability distributions. The most of the AMSLs are found to be upward-convergent/divergent pattern in the distribution, and the significance test on distributional changes is then performed. In this study, we confirm that a stationary assumption under the current climate characteristic may lead to underestimation of the design sea level, which results in increase in the failure risk in coastal structures. A detailed discussion on the role of the distribution changes for design water level is provided.

• Korea Real Estate Review
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• v.28 no.1
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• pp.79-90
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• 2018

The purpose of this study was to estimate the pricing factors of residential lands in new cities by estimating the pricing model of residential lands. For this purpose, hedonic equations for each quantile of the conditional distribution of land prices were estimated using quantile regression methods and the sale price date of Jangyu New Town in Gimhae. In this study, a quantile regression method that models the relation between a set of explanatory variables and each quantile of land price was adopted. As a result, the differences in the effects of the characteristics by price quantile were confirmed. The number of years that elapsed after the completion of land construction is the quadratic effect in the model because its impact may give rise to a non-linear price pattern. Age appears to decrease the price until certain years after the construction, and increases the price afterward. In the estimation of the quantile regression, land age appears to have a statistically significant impact on land price at the traditional level, and the turning point appears to be shorter for the low quantiles than for the higher quantiles. The positive effects of the use of land for commercial and residential purposes were found to be the biggest. Land demand is preferred if there are more than two roads on the ground. In this case, the amount of sunshine will improve. It appears that the shape of a square wave is preferred to a free-looking land. This is because the square land is favorable for development. The variables of the land used for commercial and residential purposes have a greater impact on low-priced residential lands. This is because such lands tend to be mostly used for rental housing and have different characteristics from residential houses. Residential land prices have different characteristics depending on the price level, and it is necessary to consider this in the evaluation of the collateral value and the drafting of real estate policy.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.235-245
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• 2000

In this paper, we consider the Regression Quantiles Estimators in nonlinear regression models. This paper provides the sufficient conditions for strong consistency and asymptotic normality of proposed estimation and drives asymptotic relative efficiency of proposed estimatiors with least square estimation. We give some examples and results of Monte Carlo simulation to compare least square and regression quantile estimators.

• KDI Journal of Economic Policy
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• v.44 no.4
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• pp.43-78
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• 2022

The arena of strategic competition between the US and China is expandable from international politics, trade and commerce to finance. What would happen if financial sanctions against China are imposed by the US? Would US financial sanctions lead to a sudden outflow of foreign capital and a liquidity crisis in China? We try to address these questions by estimating China's capital flows-at-risk with the CDS premium on Chinese sovereign funds. We follow Gelos et al. (2019) in setting up a quantile regression model from which China's foreign capital flow-at-risks are estimated. Based on our analysis of China's monthly capital flow data, we find that a rise in the CDS premium has statistically significant negative impacts on China's foreign capital flows-at-risk, mainly in banking flows. However, the analysis also found that due to favorable global conditions, an increase in the CDS premium is unlikely to trigger a shift to a sudden outflow of foreign capital at the moment. Meanwhile, this study found no statistically significant correlation between Korea's capital flows-at-risk and the CDS premium, suggesting that the negative impact of US financial sanctions on China would not increase the probability of capital flight from Korea in a significant manner.