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Discrete-Time Approximation of Functionals in Models of Ornstein–Uhlenbeck Type, with Applications to Finance

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  • Michael Schröder

Abstract

The paper provides benchmark approximation techniques for handling in models in continuous-time functional relationships in variables that are sampled discretely over time. The methods are demonstrated for value-functional-type of expectations in models of Ornstein–Uhlenbeck type. Based on Laguerre reduction series, a three-step program for these functionals is shown to result in both a continuous and a discrete time setting. The program is illustrated in the options case and in models based on GIG-distributions, yielding novel series representations for calibration when variance is discretely-sampled in particular. By numerical examples it is shown how the series enable computation accuracies of some 3 decimal places, for example, with just a single digit number of terms; for this the paper considers discretely-sampled situations with dimensions of up to 4 digits, and even in these dimensions significant discrepancies with the continuously-sampled values are found to persist.

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  • Michael Schröder, 2015. "Discrete-Time Approximation of Functionals in Models of Ornstein–Uhlenbeck Type, with Applications to Finance," Methodology and Computing in Applied Probability, Springer, vol. 17(2), pages 285-313, June.
    • Handle: RePEc:spr:metcap:v:17:y:2015:i:2:d:10.1007_s11009-013-9351-x
    DOI: 10.1007/s11009-013-9351-x
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    References listed on IDEAS

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    1. Ernst Eberlein & Sebastian Raible, 1999. "Term Structure Models Driven by General Lévy Processes," Mathematical Finance, Wiley Blackwell, vol. 9(1), pages 31-53, January.
    2. Daniel Dufresne, 2000. "Laguerre Series for Asian and Other Options," Mathematical Finance, Wiley Blackwell, vol. 10(4), pages 407-428, October.
    3. Elisa Nicolato & Emmanouil Venardos, 2003. "Option Pricing in Stochastic Volatility Models of the Ornstein‐Uhlenbeck type," Mathematical Finance, Wiley Blackwell, vol. 13(4), pages 445-466, October.
    4. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    5. Neil Shephard & Ole E. Barndorff-Nielsen, 2000. "Modelling by Levy Processes for Financial Econometrics," Economics Series Working Papers 2000-W03, University of Oxford, Department of Economics.
    6. S. Benaim & P. Friz, 2009. "Regular Variation And Smile Asymptotics," Mathematical Finance, Wiley Blackwell, vol. 19(1), pages 1-12, January.
    7. Friedrich Hubalek & Petra Posedel, 2011. "Joint analysis and estimation of stock prices and trading volume in Barndorff-Nielsen and Shephard stochastic volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 917-932.
    8. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    9. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
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