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  • Recent submissions
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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

    • Supplemental Material for GEL Estimation for Heavy-Tailed GARCH Models with Robust Empirical Likelihood Inference 

      Hill, Jonathan B.; Prokhorov, Artem
      Published 2015-09-11
      The following supplemental material contains an omitted simulation experiment, and omitted proofs of theorems and preliminary lemmata. Section S contains simulation results, and Section A contains an appendix with omitted proofs.
      Open Access
      Working Paper
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    • GEL Estimation for Heavy-Tailed GARCH Models with Robust Empirical Likelihood Inference 

      Hill, Jonathan B.; Prokhorov, Artem
      Published 2015-09-11
      We construct a Generalized Empirical Likelihood estimator for a GARCH(1,1) model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating equations allowing for over-identifying conditions, asymptotic ...
      Open Access
      Working Paper
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    • Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Range and Realized Measures 

      Gerlach, Richard; Wang, Chao
      Published 2015-09-11
      A new framework named Realized Conditional Autoregressive Expectile (Realized- CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a framework analogous to Realized-GARCH. ...
      Open Access
      Working Paper
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    • Generalized Variance: A Robust Estimator of Stock Price Volatility 

      Sutton, M; Vasnev, A; Gerlach, R
      Published 2015-04-30
      This paper proposes an ex-post volatility estimator, called generalized variance, that uses high frequency data to provide measurements robust to the idiosyncratic noise of stock markets caused by market microstructures. ...
      Open Access
      Working Paper
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    • Endogeneity in Stochastic Frontier Models 

      Amsler, Christine; Artem, Prokhorov; Peter, Schmidt
      Published 2015-02-17
      Stochastic frontier models are typically estimated by maximum likelihood (MLE) orcorrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the ...
      Open Access
      Working Paper
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