Modeling CAC40 Volatility Using Ultra-high Frequency Data

MPRA Paper No. 80445

Research in International Business and Finance No. 28 (2013)

27 Pages Posted: 25 Oct 2018

See all articles by Stavros Antonios Degiannakis

Stavros Antonios Degiannakis

Department of Economic and Regional Development, Panteion University of Political and Social Sciences

Christos Floros

Technological Educational Institute of Crete

Date Written: 2013

Abstract

Fractionally integrated autoregressive moving average (ARFIMA) and Heterogeneou Autoregressive (HAR) models are estimated and their ability to predict the one-trading-day-ahead CAC40 realized volatility is investigated. In particular, this paper follows three steps: (i) The optimal sampling frequency for constructing the CAC40 realized volatility is examined based on the volatility signature plot. Moreover, the realized volatility is adjusted to the information that flows into the market when it is closed. (ii) We forecast the one-day-ahead realized volatility using the ARFIMA and the HAR models. (iii) The accuracy of the realized volatility forecasts is investigated under the superior predictive ability framework. According to the predicted mean squared error, a simple ARFIMA model provides accurate onetrading day-ahead forecasts of CAC40 realized volatility. The evaluation of model's predictability illustrates that the ARFIMA (1,d,0) forecasts of realized volatility (i) are statistically superior compared to its competing models, and (ii) provide adequate one-trading-day-ahead Value-at-Risk forecasts.

Keywords: Intra-Day Data, Long Memory, Predictability, Realized Volatility, Ultrahigh Frequency Modeling, Value-at-Risk

JEL Classification: G17, G15, C15, C32, C53

Suggested Citation

Degiannakis, Stavros Antonios and Floros, Christos, Modeling CAC40 Volatility Using Ultra-high Frequency Data (2013). MPRA Paper No. 80445, Research in International Business and Finance No. 28 (2013), Available at SSRN: https://ssrn.com/abstract=3259831

Stavros Antonios Degiannakis (Contact Author)

Department of Economic and Regional Development, Panteion University of Political and Social Sciences ( email )

136 Sygrou
Athens
Greece

Christos Floros

Technological Educational Institute of Crete ( email )

Department of Accounting & Finance
School of Management & Economics
Heraklion, Crete GR 71004
Greece

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