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Aggregation and Model Construction for Volatility Models

Author

Listed:
  • Barndorf-Nielsen, O.E.
  • Shephard, N.

Abstract

In this paper we will rigourously study some of the properties of continuous time stochastic volatility models. We have five main results, including: the stochastic volatility class can be linked to Cox process based models of tick-by-tick financial data; we characterise the moments, autocorrelation function and spectrum of squared returns; based only on discrete time returns, we give a simple consistent and asymptotically normally distributed estimator of continuous time volatility models without any simulation or discretisation error.

Suggested Citation

  • Barndorf-Nielsen, O.E. & Shephard, N., 1998. "Aggregation and Model Construction for Volatility Models," Economics Papers 141, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:141
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    Citations

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    Cited by:

    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-059, New York University, Leonard N. Stern School of Business-.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," Multinational Finance Journal, Multinational Finance Journal, vol. 4(3-4), pages 159-179, September.
    3. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    4. Degiannakis, Stavros & Xekalaki, Evdokia, 2007. "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models," MPRA Paper 96324, University Library of Munich, Germany.
    5. Panagiotis Samartzis & Nikitas Pittis & Nikolaos Kourogenis & Phoebe Koundouri, 2013. "Factor Models of Stock Returns: GARCH Errors versus Autoregressive Betas," DEOS Working Papers 1318, Athens University of Economics and Business.
    6. Phoebe Koundouri & Nikolaos Kourogenis & Nikitas Pittis & Panagiotis Samartzis, 2016. "Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 445-461, August.
    7. Phoebe Koundouri & Nikolaos Kourogenis & Nikitas Pittis & Panagiotis Samartzis, 2015. "Factor Models as 'Explanatory Unifiers' versus 'Explanatory Ideals' of Empirical Regularities of Stock Returns," DEOS Working Papers 1507, Athens University of Economics and Business.
    8. Neil Shephard & Tina Hviid Rydberg, 1999. "A modelling framework for the prices and times of trades made on the New York stock exchange," Economics Series Working Papers 1999-W14, University of Oxford, Department of Economics.
    9. Yue Fang, 2000. "When Should Time be Continuous? Volatility Modeling and Estimation of High-Frequency Data," Econometric Society World Congress 2000 Contributed Papers 0843, Econometric Society.
    10. Rüdiger Frey & Wolfgang J. Runggaldier, 1999. "Risk-minimizing hedging strategies under restricted information: The case of stochastic volatility models observable only at discrete random times," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 50(2), pages 339-350, October.
    11. Burc Kayahan & Thanasis Stengos & Burak Saltoglu, 2002. "Intra-Day Features of Realized Volatility: Evidence from an Emerging Market," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(1), pages 17-24, April.

    More about this item

    Keywords

    MODELS;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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