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Real estate VaR estimation in Seoul and Busan, Korea
Journal of the Korean Data & Information Science Society 2019;30:469-78
Published online March 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.2.469
© 2019 Korean Data and Information Science Society.

Yeonggyu Yun1  Sangyeol Lee2

1Department of Economics, Seoul National University, 2Department of Statistics, Seoul National University
Correspondence to: Professor, Department of Statistics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea. E-mail: sylee@stats.snu.ac.kr
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2018R1A2A2A05019433).
Received February 6, 2019; Revised March 14, 2019; Accepted March 16, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this study, we estimtate the value-at-risk (VaR) of the 10-day average apartment prices of Gangnam-gu, Seoul and Haeundae-gu, Busan in Korea. For this purpose, we adopt the semiparametric quantile regression approach for the VaR calculation based on ARIMA-GARCH models, employing the well-known risk measures such as the con- ditional autoregressive value-at-risk (CAViaR) and conditional autoregressive expectile (CARE) methods. After conducting the unconditional coverage (UC) and conditional coverage (CC) tests on the estimated VaRs, we conclude that the two methods per- form similarly for the Seoul case but the CARE method shows more stability than the CAViaR method in the Busan case.
Keywords : CARE, CAViaR, quantile regression, real estate, value-at-risk.