Research Mentor(s)
Noguchi, Kimihiro
Description
This project aims to produce accurate volatility forecasts, using high-frequency financial time series data. The primary mathematical methods used are Functional Data Analysis, time series analysis techniques such as Autoregressive Models and a comparison between Multi-variate and Uni-variate Singular Spectrum Analysis. These results aim to be useful for financial risk quantification.
Document Type
Event
Start Date
16-5-2018 12:00 PM
End Date
16-5-2018 3:00 PM
Department
Mathematics
Genre/Form
student projects, posters
Subjects – Topical (LCSH)
Finance--Mathematical models
Type
Image
Keywords
Singular Spectrum Analysis
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Included in
Short-Term Volatility Curve Predictions Using Singular Spectrum Analysis
This project aims to produce accurate volatility forecasts, using high-frequency financial time series data. The primary mathematical methods used are Functional Data Analysis, time series analysis techniques such as Autoregressive Models and a comparison between Multi-variate and Uni-variate Singular Spectrum Analysis. These results aim to be useful for financial risk quantification.
Comments
Outstanding Poster Award Recipient