Modelling Different Volatility Components in High-Frequency Financial Returns

Feng, Yuanhua

Modelling Different Volatility Components in High-Frequency Financial Returns

Files

dp02_18.pdf(905.4 KB)

Date

2002

Authors

Feng, Yuanhua

Series

CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie; 2002/18

Publication type

Working Paper/Technical Report

Abstract

This paper considers simultaneous modelling of seasonality, slowly changing un- conditional variance and conditional heteroskedasticity in high-frequency financial returns. A new approach, called a seasonal SEMIGARCH model, is proposed to perform this by introducing multiplicative seasonal and trend components into the GARCH model. A data-driven semiparametric algorithm is developed for estimat- ing the model. Asymptotic properties of the proposed estimators are investigated brie y. An approximate significance test of seasonality and the use of Monte Carlo confidence bounds for the trend are proposed. Practical performance of the pro- posal is investigated in detail using some German stock price returns. The approach proposed here provides a useful semiparametric extension of the GARCH model.

Subject (DDC)

330 Economics

Keywords

High-frequency financial data,nonparametric regression,seasonality in volatility,semiparametric GARCH model,trend in volatility

Cite This

ISO 690

FENG, Yuanhua, 2002. Modelling Different Volatility Components in High-Frequency Financial Returns

BibTex

@techreport{Feng2002Model-12080,
  year={2002},
  series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie},
  title={Modelling Different Volatility Components in High-Frequency Financial Returns},
  number={2002/18},
  author={Feng, Yuanhua}
}

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