Skip to main content
SpringerLink
Log in

HAR-type Models for Volatility Forecasting: An Empirical Investigation

  • Conference paper
  • First Online:
Computer Aided Systems Theory – EUROCAST 2019 (EUROCAST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12013))

Included in the following conference series:

  • 844 Accesses

Abstract

The paper addresses the problem of forecasting realized volatility in the context of HAR-type models. Some extensions of the basic HAR-RV model are discussed. The forecasting performance of the considerec HAR-type models are compared in terms of suitable loss functions, by using the Model Confidence Set procedure, on two real datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Corsi, F.: A simple approximate long-memory model of realized volatility. J. Fin. Econom. 7(2), 174–196 (2009)

    Google Scholar 

  2. Giot, P., Lauren, S., Petitjeanad, M.: Trading activity, realized volatility and jumps. J. Empir. Finance 17(1), 168–175 (2010)

    Article  Google Scholar 

  3. Sevi, B.: Forecasting the volatility of crude oil futures using intraday data. Eur. J. Oper. Res. 235, 643659 (2014)

    Article  MathSciNet  Google Scholar 

  4. Wang, Y., Ma, F., Wei, Y., Wu, C.: Forecasting realized volatility in a changing world: a dynamic model averaging approach. J. Bank. Finance 64, 136–149 (2016)

    Article  Google Scholar 

  5. Hansen, P.R., Lunde, A., Nason, J.M.: The model confidence set. Econometrica 79(2), 453–497 (2011)

    Article  MathSciNet  Google Scholar 

  6. Barndorff-Nielsen, O.E., Shephard, N.: Power and bipower variation with stochastic volatility and jumps. J. Financial Econom. 2(1), 1–37 (2004)

    Article  Google Scholar 

  7. Huang, X., Tauchen, G.: The relative contribution of jumps to total price variance. J. Financial Econom. 3(4), 456499 (2005)

    Article  Google Scholar 

  8. Patton, A.J.: Volatility forecast comparison using imperfect volatility proxies. J. Econom. 160(1), 246–256 (2011)

    Article  MathSciNet  Google Scholar 

  9. Wang, Y., Pan, Z., Wu, C.: Time-varying parameter realized volatility models. J. Forecast. 36, 566580 (2017)

    Article  MathSciNet  Google Scholar 

  10. Andersen, T.G., Bollerslev, T., Huang, X.: A reduced form framework for modeling volatility of speculative prices based on realized variation measures. J. Econom. 160(1), 176–189 (2011)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Political and Social Studies, University of Salerno, Fisciano, Italy

    G. Albano

  2. Department of Economics, University of Roma Tre, Rome, Italy

    D. De Gaetano

  3. SOSE-Soluzioni per il Sistema Economico, Rome, Italy

    D. De Gaetano

Authors
  1. G. Albano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. De Gaetano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Albano .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Albano, G., De Gaetano, D. (2020). HAR-type Models for Volatility Forecasting: An Empirical Investigation. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12013. Springer, Cham. https://doi.org/10.1007/978-3-030-45093-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-45093-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45092-2

  • Online ISBN: 978-3-030-45093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation