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Financial volatility: an introduction

Author

Listed:
  • Philip Hans Franses

    (Econometric Institute Erasmus University Rotterdam)

  • Michael McAleer

    (Department of Economics University of Western Australia)

Abstract

It is now 20 years since the publication of Engle's (1982) seminal paper, which introduced ARCH to the world. The ARCH paper had an enormous influence on both theoretical and applied econometrics, and was influential in the establishment of the discipline of Financial Econometrics. In this paper we provide an introduction to the special issue on modelling and forecasting financial volatility, which commemorates the Twentieth Anniversary of the publication of ARCH. Financial econometrics has become a mature discipline over the last two decades, and one of its major research objects is the modelling and forecasting of volatility. This special issue presents ten papers, all of which focus on volatility and risk. The papers examine issues such as the new frontiers of volatility, the selection of models for observed and unobserved volatility, the potential long-memory property of volatility, and the measurement of volatility. The commonality of papers is that they do not examine the extant literature, which has been reviewed elsewhere, but rather outline a number of important issues that are not only of current interest, but are likely to remain so for many years to come. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Philip Hans Franses & Michael McAleer, 2002. "Financial volatility: an introduction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 419-424.
    • Handle: RePEc:jae:japmet:v:17:y:2002:i:5:p:419-424
    DOI: 10.1002/jae.693
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    References listed on IDEAS

    as
    1. Bollerslev, Tim, 2001. "Financial econometrics: Past developments and future challenges," Journal of Econometrics, Elsevier, vol. 100(1), pages 41-51, January.
    2. Engle, Robert, 2001. "Financial econometrics - A new discipline with new methods," Journal of Econometrics, Elsevier, vol. 100(1), pages 53-56, January.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ling, Shiqing & McAleer, Michael & Tong, Howell, 2015. "Frontiers in Time Series and Financial Econometrics: An overview," Journal of Econometrics, Elsevier, vol. 189(2), pages 245-250.
    2. Junru Zhang & Hadrian Geri Djajadikerta & Zhaoyong Zhang, 2018. "Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    3. Ooms, M., 2008. "Trends in Applied Econometrics Software Development 1985-2008, an analysis of Journal of Applied Econometrics research articles, software reviews, data and code," Serie Research Memoranda 0021, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    4. Ekong, Christopher N. & Onye, Kenneth U., 2017. "Application of Garch Models to Estimate and Predict Financial Volatility of Daily Stock Returns in Nigeria," MPRA Paper 88309, University Library of Munich, Germany.
    5. Caginalp, Carey & Caginalp, Gunduz & Swigon, David, 2021. "Stochastic asset flow equations: Interdependence of trend and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    6. Ling, S. & McAleer, M.J. & Tong, H., 2015. "Frontiers in Time Series and Financial Econometrics," Econometric Institute Research Papers EI 2015-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Caginalp, Carey & Caginalp, Gunduz, 2020. "Derivation of non-classical stochastic price dynamics equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    8. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    9. Carey Caginalp & Gunduz Caginalp, 2019. "Derivation of non-classical stochastic price dynamics equations," Papers 1908.01103, arXiv.org, revised Aug 2020.
    10. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    11. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    12. Liu, Heping & Erdem, Ergin & Shi, Jing, 2011. "Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed," Applied Energy, Elsevier, vol. 88(3), pages 724-732, March.

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