Work

Volatility analysis for high frequency time series via dimension reduction and functional ARCH

Year: 2025

Type: article

Abstract: This article carries out volatility analysis of high frequency financial time series data.Based on squares of intraday returns, both multi-dimensional and infinite dimensional volatility analysis are discussed.Specifically, for a multi-dimensional approach, interpretable dimension reduction for volatility is pursued using the so called partially-quantified principal component analysis (PCA).To illustrate infinite dimensional volatility analysis, functional volatility model, viz., functional ARCH (fARCH) is entertained for high frequency financial time series data.Interesting applications are made using post Covid-19 financial time series.Volatility for one minute high frequency KOSPI (Korea stock prices index) data ranging from January 2022 to July 2024 is analyzed to obtain both an interpretable partially-quantified PCA and functional ARCH (fARCH) configuration.

Source: Korean Journal of Applied Statistics

DOI: 10.5351/kjas.2025.38.4.455

Authors: Jae Eun Yoon, Eun ji Han, Sun Young Hwang

Institution: Sookmyung Women's University

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

Funders: National Research Foundation, National Research Foundation of Korea

Award: G5406466233