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Stochastic Volatility: Origins and Overview

In: Handbook of Financial Time Series

Author

Listed:
  • Neil Shephard

    (University of Oxford, Department of Economics, University of Oxford, Oxford-Man Institute)

  • Torben G. Andersen

    (Northwestern University, Kellogg School of Management)

Abstract

Stochastic volatility is the main way time-varying volatility is modelled in financial markets. The development of stochastic volatility is reviewed, placing it in a modeling and historical context. Some recent trends in the literature are highlighted.

Suggested Citation

  • Neil Shephard & Torben G. Andersen, 2009. "Stochastic Volatility: Origins and Overview," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 10, pages 233-254, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-71297-8_10
    DOI: 10.1007/978-3-540-71297-8_10
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    Cited by:

    1. Haakon Kavli & Kevin Kotzé, 2014. "Spillovers in Exchange Rates and the Effects of Global Shocks on Emerging Market Currencies," South African Journal of Economics, Economic Society of South Africa, vol. 82(2), pages 209-238, June.
    2. Fuest, Angela & Schmidt, Torsten, 2017. "Inflation expectation uncertainty, inflation and the output gap," Ruhr Economic Papers 673, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Rossi, E. & Spazzini, F., 2010. "Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2786-2800, November.
    4. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.
    5. Chrysanthakopoulos, Christos & Konstantinou, Panagiotis & Tagkalakis, Athanasios, 2025. "Government spending cyclicality, economic stability and uncertainty," Economic Systems, Elsevier, vol. 49(4).
    6. McAdam, Peter & Muck, Jakub & Growiec, Jakub, 2015. "Will the true labor share stand up?," Working Paper Series 1806, European Central Bank.
    7. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    8. Natsumi Ochiai & Hisashi Tanizaki, 2026. "Intraday volatility of 24-hour trading pattern in Japanese market," SN Business & Economics, Springer, vol. 6(1), pages 1-17, January.
    9. Sergey S. Stepanov, 2009. "Resilience of Volatility," Papers 0911.5048, arXiv.org.
    10. Fernández-Villaverde, Jesús & Guerrón-Quintana, Pablo & Rubio-Ramírez, Juan F., 2015. "Estimating dynamic equilibrium models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 185(1), pages 216-229.
    11. Jakub Mućk & Peter McAdam & Jakub Growiec, 2018. "Will The “True” Labor Share Stand Up? An Applied Survey On Labor Share Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 961-984, September.

    More about this item

    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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