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VaR forecasting for PM10 data using time series models

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Authors
허영실
Advisor
이상열
Major
자연과학대학 통계학과
Issue Date
2016-08
Publisher
서울대학교 대학원
Keywords
ARMA modelgoodness of fit testPM10quantile regressionVaR forecasting
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 8. 이상열.
Abstract
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.
Language
English
URI
http://hdl.handle.net/10371/131318
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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