Predicting New Energy Prices: Are Technical Indicators and Regime-Switching Models Helpful?

Area of Science:

• Quantitative Finance
• Econometrics
• Financial Forecasting

Background:

• Traditional technical indicators often provide limited predictive power for financial markets.
• Previous research using the Three-Pass Regression Filter (3PRF) has not fully captured the complexity of technical measurement strategies.
• Financial markets often exhibit distinct states or regimes, influencing price dynamics.

Purpose of the Study:

• To develop and validate a novel aligned technical index for predicting new energy price returns.
• To assess the predictive performance of the new index compared to traditional methods.
• To enhance forecasting by integrating a regime-switching model with time-varying transition probabilities.

Main Methods:

• Development of a novel aligned technical index using multiple technical indicators.
• Extraction of the index via the Three-Pass Regression Filter (3PRF) method.
• Application of a regime-switching model with time-varying transition probabilities to the forecasting framework.

Main Results:

• The novel technical index demonstrates significant predictive power for new energy price returns in both in-sample and out-of-sample tests.
• The 3PRF-extracted index outperforms traditional methods.
• The regime-switching model, incorporating the technical index, significantly improves forecasting performance and influences regime transition probabilities.

Conclusions:

• The novel aligned technical index offers superior predictive capabilities for energy price forecasting.
• The integration of regime-switching dynamics enhances model accuracy and provides valuable insights into market behavior.
• Both the technical index and the regime-switching model offer substantial economic value for mean-variance asset allocation strategies.