Point forecasting of intraday volume using Bayesian autoregressive conditional volume models. (9th November 2018) - Huptas, Roman
Abstract: In this paper, we apply Bayesian inference to model and forecast intraday trading volume, using autoregressive conditional volume (ACV) models, and we evaluate the quality of volume point forecasts. In the empirical application, we focus on the analysis of both in‐ and out‐of‐sample performance of Bayesian ACV models estimated for 2‐minute trading volume data for stocks quoted on the Warsaw Stock Exchange in Poland. We calculate two types of point forecasts, using either expected values or medians of predictive distributions. We conclude that, in general, all considered models generate significantly biased forecasts. We also observe that the considered models significantly outperform such benchmarks as the naïve or rolling means forecasts. Moreover, in terms of root mean squared forecast errors, point predictions obtained within the ACV model with exponential distribution emerge superior compared to those calculated in structures with more general innovation distributions, although in many cases this characteristic turns out to be statistically insignificant. On the other hand, when comparing mean absolute forecast errors, the median forecasts obtained within the ACV models with Burr and generalized gamma distribution are found to be statistically better than other forecasts.