Verification of Forecast Effectiveness for Selected Volatility Estimators
DOI:
https://doi.org/10.15611/fins.2025.1.03Keywords:
volatility, volatility forecasting, volatility estimators, OHLC pricesAbstract
Aim: The aim of this study was to determine which volatility forecasting method produces results that are closest to the actual and whether the use of estimators with OHLC prices affects forecast accuracy.
Methodology: This study examined five models – a historical model, GARCH(1,1) and three GARCH models with selected volatility estimators (Parkinson, Garman-Klass and Rogers-Satchell). The sample used daily prices, with each instrument having 2001 observations and a 20-day forecast horizon. Forecast accuracy was assessed using RMSE and MAE.
Findings: The empirical results determined that no specific approach is universally regarded as superior. It is recommended that naive methods or the standard GARCH method be used as they are simpler than the complex models with selected estimators and save operating time. Volatility estimators enhanced accuracy for stocks but not for other instruments. For stocks, estimator-based models obtained better results; for others, classical methods were more effective.
Implications and recommendations: This study can assist researchers in selecting the appropriate model for specific data and indicate whether the use of a different estimator would enrich the results of forecasts. Further research could investigate the impact of higher frequency data on the performance of volatility estimators.
Originality/value: The study examined whether the Polish market responds to volatility estimators similarly to global markets. It also confirmed that the best model varies by instrument: the model with Rogers-Satchell estimator for stocks, GARCH(1,1) for currencies, and the historical method for commodities.
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Copyright (c) 2025 Martyna Frankowicz
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Accepted 2025-05-22
Published 2025-07-09
