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Estimating for Discretely Observed Diffusions Using Transform Functions

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Abstract

This paper introduces a new estimation technique for discretely observed diffusion processes. Transform functions are applied to transform the data to obtain good and easily calculated estimators of both the drift and diffusion coefficients. Consistency and asymptotic normality of the resulting estimators is investigated. Power transforms are used to estimate the parameters of affine diffusions, for which explicit estimators are obtained.

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  • Leah Kelly & Eckhard Platen & Michael Sorensen, 2003. "Estimating for Discretely Observed Diffusions Using Transform Functions," Research Paper Series 96, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:96
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    File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp96.pdf
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    Cited by:

    1. Wolfgang Breymann & Leah Kelly & Eckhard Platen, 2005. "Intraday Empirical Analysis and Modeling of Diversified World Stock Indices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(1), pages 1-28, March.
    2. Eckhard Platen & Renata Rendek, 2007. "Empirical Evidence on Student-t Log-Returns of Diversified World Stock Indices," Research Paper Series 194, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Xiao Huang, 2011. "Quasi‐maximum likelihood estimation of discretely observed diffusions," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 241-256, July.
    4. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.
    5. Leah Kelly, 2004. "Inference and Intraday Analysis of Diversified World Stock Indices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2004.
    6. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    7. Huang Xiao, 2013. "Quasi-maximum likelihood estimation of multivariate diffusions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 179-197, April.

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