A Bayesian Dirichlet autoregressive conditional heteroskedasticity model for forecasting currency shares
针对Airbnb四个区域的货币手续费份额数据，提出贝叶斯狄利克雷ARMA模型，通过时变精度成分捕捉波动性，在预测准确性和区间校准上优于传统方法，适用于成分数据的预测与不确定性量化。
In marketplace finance, the daily mix of billing currencies is compositional data that drive forecasting, reporting, and treasury risk. We study Airbnb’s currency-fee shares across four regions and present a Bayesian Dirichlet ARMA model with a time-varying precision component. The model keeps predictions on the simplex, captures mean dynamics on the additive log-ratio scale, and lets volatility spike during disruptions and settle as conditions normalize. We evaluate against standard Dirichlet and transformed-Gaussian alternatives using simulations with misreported observations and temporary regime shifts, and then validate on held-out data. Across all settings, our approach delivers more accurate forecasts, better-calibrated intervals, and weaker residual persistence. Modeling precision as a dynamic process provides a practical, interpretable way to forecast proportions and quantify uncertainty when the noise itself moves.