Research Mentor(s)

Kimihiro Noguchi

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

Location

Mathematics

Keywords

Singular Spectrum Analysis

Comments

Outstanding Poster Award Recipient

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

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May 16th, 12:00 PM May 16th, 3:00 PM

Short-Term Volatility Curve Predictions Using Singular Spectrum Analysis

Mathematics

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.

 

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