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  • The University of Sydney
  • University of Sydney Business School
  • Discipline of Business Analytics
  • Business Analytics Working Paper Series
  • Recent submissions
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Business Analytics Working Paper Series: Recent submissions

    • Does the Box-Cox transformation help in forecasting macroeconomic time series? 

      Proietti, Tommaso; Lütkepohl, Helmut
      Published 2011-10-01
      The paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the Box-Cox power transformation, which applies to series measured ...
      Open Access
      Working Paper
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    • Mixed strategies in discriminatory divisible-good auctions 

      Anderson, E.J.; Holmberg, P.; Philpott, A.B.
      Published 2009-11-01
      Using the concept of market-distribution functions, we derive general optimality conditions for discriminatory divisible-good auctions, which are also applicable to Bertrand games and non-linear pricing. We introduce the ...
      Open Access
      Working Paper
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    • The Two-sided Weibull Distribution and Forecasting Financial Tail Risk 

      Gerlach, Richard; Chen, Qian
      Published 2011-01-01
      A two-sided Weibull is developed to model the conditional financial return distribution, for the purpose of forecasting Value at Risk (VaR) and conditional VaR. A range of conditional return distributions are combined with ...
      Open Access
      Working Paper
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    • Australian Residential Housing Market & Hedonic Construction of House Price Indices for Metropolitan 

      Knight, Eva; Cottet, Remy
      Published 2011-02-01
      A Semiparametric spatial model is used as it allows nonlinear estimation of both mean and variance. A Bayesian approach is used for inference via a Markov Chain Monte Carlo sampling scheme. A distinct advantage of using ...
      Open Access
      Working Paper
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    • Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis 

      Gerlach, Richard; Chen, Cathy W.S.; Lin, Edward M.H.; Lee, Wcw
      Published 2011-03-01
      Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus ...
      Open Access
      Working Paper
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