Event Title

Detecting Major Economic Events from Short Financial Return Series Using Singular Spectrum Analysis

Research Mentor(s)

Noguchi, Kimihiro

Description

Although the majority of financial time series analysis focuses on the volatility (e.g., standard deviation) of returns, several recent studies focus on the return series themselves to understand their behaviors. Daily return series which consist of at least one thousand observations tend to contain useful information about short- and long-term cyclical patterns, reflecting weekend, intramonth, quarterly, and annual effects. In this study, we focus on short return series with less than three hundred observations to examine cyclical patterns as well as short-lived trend patterns. Using singular spectrum analysis (SSA), we suggest that the structure of short return series is rather complex, possibly consisting of more than twenty significant cyclical and trend components. Moreover, by examining swaps in the relative significance of these components, we investigate the relationship between major economic events and periods at which these swaps occur frequently. The results suggest that an appropriately weighted mean squared difference caused by possible swaps in the components well correlates with major economic events, supporting the view of interpreting volatility as a consequence of underlying structural changes in the trend or cyclical components of return series.

Document Type

Event

Start Date

May 2020

End Date

May 2020

Department

Statistics, Mathematics

Genre/Form

student projects, posters

Type

Image

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

This document is currently not available here.

Share

COinS
 
May 18th, 9:00 AM May 22nd, 5:00 PM

Detecting Major Economic Events from Short Financial Return Series Using Singular Spectrum Analysis

Although the majority of financial time series analysis focuses on the volatility (e.g., standard deviation) of returns, several recent studies focus on the return series themselves to understand their behaviors. Daily return series which consist of at least one thousand observations tend to contain useful information about short- and long-term cyclical patterns, reflecting weekend, intramonth, quarterly, and annual effects. In this study, we focus on short return series with less than three hundred observations to examine cyclical patterns as well as short-lived trend patterns. Using singular spectrum analysis (SSA), we suggest that the structure of short return series is rather complex, possibly consisting of more than twenty significant cyclical and trend components. Moreover, by examining swaps in the relative significance of these components, we investigate the relationship between major economic events and periods at which these swaps occur frequently. The results suggest that an appropriately weighted mean squared difference caused by possible swaps in the components well correlates with major economic events, supporting the view of interpreting volatility as a consequence of underlying structural changes in the trend or cyclical components of return series.