Quantile Double AR Time Series Models for Financial Returns
نویسندگان
چکیده
In this paper we develop a novel quantile double AR model for modelling financial time series. This is done by specifying a generalized lambda distribution to the quantile function of the location-scale double autoregressive model developed in Ling (2004, 2007). Model parameter estimation uses MCMC Bayesian methods. A novel simulation technique is introduced for forecasting the conditional distribution of financial returns m-periods ahead, and hence any predictive quantities of interest. The application to forecasting Value-at-Risk at different time horizons and coverage probabilities for Dow Jones Industrial Average shows that our method works very well in practice.
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