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

Jennifer Chan, Kok Haur Ng and Rachel Ragell

International Review of Economics & Finance, 2019, vol. 61, issue C, 188-212

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.

Keywords: CARR model; Realised range; Return model; Bayesian analysis; Generalised beta type II distribution; VaR; Tail conditional VaR (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.iref.2019.01.003

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