Forecasting Stock Prices in a Frontier Capital Market: A Comparison of Naïve, Drift, ETS, ARIMA and ARIMA–GARCH Models for Listed Joint-Stock Companies in Uzbekistan
Keywords:
stock price forecasting, ARIMA, GARCHAbstract
This study evaluates and compares the out-of-sample forecasting performance of classical and volatility-adjusted time-series models for the equity prices of eight joint-stock companies listed in Uzbekistan: HMKB, SQB, IPKY, ALKB, QZSM, UZTL, URTS and CBSK. Using daily closing prices from 9 September 2022 to 17 April 2026 (883 observations per stock with no missing values), five competing specifications are estimated and assessed: the Naïve (random-walk) benchmark, the Drift model, exponential smoothing (ETS), the autoregressive integrated movingaverage model (ARIMA), and an ARIMA–GARCH model with Student-t innovations that explicitly captures volatility clustering. Models are identified on a three-year training window and evaluated on a subsequent six-month hold-out period using the mean absolute error (MAE), the root mean squared error (RMSE) and the mean absolute percentage error (MAPE); the model with the lowest hold-out RMSE is retained for each stock and re-estimated on the full sample to produce quarterly forecasts
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