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Bayesian return forecasts using realised range and asymmetric CARR model with various distribution assumptions

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  • Chan, Jennifer So-Kuen
  • Ng, Kok-Haur
  • Ragell, Rachel

Abstract

A popular technique for measuring financial risk is to apply generalised autoregressive conditional heteroskedastic (GARCH)-type models to return-based time series. However recent studies are more focused on estimating volatility using the realised range calculated from high-frequency data. Making use of this efficient volatility measure, this paper analyses returns using a two-stage model: the first stage fits the realised range measures to the conditional autoregressive range (CARR) model whereas the second stage inputs the fitted values as observed volatilities in the return model. On modelling choices, we investigate how the model performance can be improved by different choices of error distributions and mean functions. We also study the effect of interval size on the realised range measures. A Bayesian Markov chain Monte Carlo approach via Rstan is used to estimate the parameters of these models. Empirical applications are based on three market indices. Results show that the CARR model with generalised beta type II distribution provides the most efficient modelling of volatility for all data. We also find that the realised range calculated using the most frequent 5 min intervals provides accurate estimates and forecasts of value-at-risk (VaR) and tail conditional VaR for both range and return than the daily range for all market indices.

Suggested Citation

  • Chan, Jennifer So-Kuen & Ng, Kok-Haur & Ragell, Rachel, 2019. "Bayesian return forecasts using realised range and asymmetric CARR model with various distribution assumptions," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 188-212.
    • Handle: RePEc:eee:reveco:v:61:y:2019:i:c:p:188-212
    DOI: 10.1016/j.iref.2019.01.003
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    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
    3. Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023. "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    4. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

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