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Author NameVolfson, Alexander
URNetd-042910-153817
TitleExploring the optimal Transformation for Volatility
DegreeMS
DepartmentMathematical Sciences
Advisors
  • Balgobin Nandram, Advisor
  • Keywords
  • Metropolis-Hastings algorithm
  • Bayes
  • Empirical Bayes
  • stochastic volatility
  • Box-Cox transformation
  • Date of Presentation/Defense2010-04-29
    Availability unrestricted

    Abstract

    This paper explores the fit of a stochastic volatility model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian distribution, to the continuously compounded daily returns of the Australian stock index. Estimation was difficult, and over-fitting likely, because more variables are present than data. We developed a revised model that held a couple of these variables fixed and then, further, a model which reduced the number of variables significantly by grouping trading days. A Metropolis-Hastings algorithm was used to simulate the joint density and derive estimated volatilities. Though autocorrelations were higher with a smaller Box-Cox transformation parameter, the fit of the distribution was much better.

    Files
  • avolfson.pdf

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