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VaR forecasting for PM10 data using time series models

DC Field Value Language
dc.contributor.advisor이상열-
dc.contributor.author허영실-
dc.date.accessioned2017-07-19T08:46:56Z-
dc.date.available2017-07-19T08:46:56Z-
dc.date.issued2016-08-
dc.identifier.other000000136528-
dc.identifier.urihttp://hdl.handle.net/10371/131318-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 8. 이상열.-
dc.description.abstractThis 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.-
dc.description.tableofcontentsChapter 1 Introduction 1

Chapter 2 Reviews 3
2.1 Value at risk 3
2.2 Quantile Regression 4

Chapter 3 Data description and model building 5
3.1 Data description 5
3.2 ARMA model fitting for the PM10 data 5
3.3 Entropy-based goodness of fit test for residuals 6

Chapter 4 Conditional VaR forecasting 13
4.1 Parametric method 13
4.2 Quantile regression method 15
4.3 Evaluation 15

Chapter 5 Conclusions 18

References 19

국문 초록 22
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dc.formatapplication/pdf-
dc.format.extent2881531 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectARMA model-
dc.subjectgoodness of fit test-
dc.subjectPM10-
dc.subjectquantile regression-
dc.subjectVaR forecasting-
dc.subject.ddc519-
dc.titleVaR forecasting for PM10 data using time series models-
dc.typeThesis-
dc.contributor.AlternativeAuthorXU YINGSHI-
dc.description.degreeMaster-
dc.citation.pages21-
dc.contributor.affiliation자연과학대학 통계학과-
dc.date.awarded2016-08-
Appears in Collections:
College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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