S-Space College of Natural Sciences (자연과학대학) Dept. of Statistics (통계학과) Theses (Master's Degree_통계학과)
VaR forecasting for PM10 data using time series models
- 자연과학대학 통계학과
- Issue Date
- 서울대학교 대학원
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 8. 이상열.
- This thesis analyze the particular matter (PM)-10 data in Korea using time series analysis. To this task, we use the log-transformed data of the daily averages of the PM10 values which collected from Korea Meteorological Administration to obtain an optimal ARMA model. Then, we conduct the entropy-based goodness of fit (GOF) test for the fitted residuals to check the departure from the normal and skew-t distributions, and also a conditional value-at-risk (VaR) forecasting using the parametric and quantile regression methods. The obtained result has a potential usage as a guideline for the patients with some respiratory disease to pay more attention to health care when the conditional VaR forecast goes beyond the limit values of severe health hazards.