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Chan, J.S., et al. (2012) A Bayesian Conditional Autoregressive Geometric Process Model for Range Data. Computational Statistics & Data Analysis, 56, 3006-3019.
https://doi.org/10.1016/j.csda.2011.01.006

has been cited by the following article:

• TITLE: Modelling and Forecasting Unbiased Extreme Value Volatility Estimator: A Study Based on EUR/USD Exchange Rate

  AUTHORS: Dilip Kumar

  KEYWORDS: Volatility Modeling, Volatility Forecasting, Forecast Evaluation, Economic Significance Analysis, Bias-Corrected Extreme Value Estimator

  JOURNAL NAME: Theoretical Economics Letters, Vol.8 No.9, June 13, 2018

  ABSTRACT: The paper provides a framework to model and forecast volatility of EUR/USD exchange rate based on the unbiased AddRS estimator as proposed by Kumar and Maheswaran [1]. The framework is based on the heterogeneous auto-regressive (HAR) model to capture the heterogeneity in a market and to ac-count for long memory in data. The results indicate that the framework based on the unbiased extreme value volatility estimator generates more accurate forecasts of daily volatility in comparison to alternative volatility models.