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Parameter estimation for multivariate diffusion systems

Listed author(s):
  • Varughese, Melvin M.
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    Diffusion processes are widely used for modelling real-world phenomena. Except for select cases however, analytical expressions do not exist for a diffusion process’ transitional probabilities. It is proposed that the cumulant truncation procedure can be applied to predict the evolution of the cumulants of the system. These predictions may be subsequently used within the saddlepoint procedure to approximate the transitional probabilities. An approximation to the likelihood of the diffusion system is then easily derived. The method is applicable for a wide range of diffusion systems — including multivariate, irreducible diffusion systems that existing estimation schemes struggle with. Not only is the accuracy of the saddlepoint comparable with the Hermite expansion — a popular approximation to a diffusion system’s transitional density — it also appears to be less susceptible to increasing lags between successive samplings of the diffusion process. Furthermore, the saddlepoint is more stable in regions of the parameter space that are far from the maximum likelihood estimates. Hence, the saddlepoint method can be naturally incorporated within a Markov Chain Monte Carlo (MCMC) routine in order to provide reliable estimates and credibility intervals of the diffusion model’s parameters. The method is applied to fit the Heston model to daily observations of the S&P 500 and VIX indices from December 2009 to November 2010.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 57 (2013)
    Issue (Month): 1 ()
    Pages: 417-428

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    Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:417-428
    DOI: 10.1016/j.csda.2012.07.010
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    1. Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382.
    2. O. E. Barndorff-Nielsen, 1999. "Tail Exactness of Multivariate Saddlepoint Approximations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(2), pages 253-264.
    3. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters,in: Theory Of Valuation, chapter 5, pages 129-164 World Scientific Publishing Co. Pte. Ltd..
    4. D. Kleinhans & R. Friedrich, 2006. "Maximum Likelihood Estimation of Drift and Diffusion Functions," Papers physics/0611102,, revised Mar 2007.
    5. Osnat Stramer & Matthew Bognar & Paul Schneider, 2010. "Bayesian Inference for Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(4), pages 450-480, Fall.
    6. Ai[dieresis]t-Sahalia, Yacine & Yu, Jialin, 2006. "Saddlepoint approximations for continuous-time Markov processes," Journal of Econometrics, Elsevier, vol. 134(2), pages 507-551, October.
    7. Yacine Ait-Sahalia, 2002. "Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach," Econometrica, Econometric Society, vol. 70(1), pages 223-262, January.
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