Key result
SETAR, a threshold model accounting for nonlinearities, yielded more accurate long forecasts of asset prices in emerging and developed stock markets compared to linear autoregressive models.
SETAR models provide more accurate long-term forecasting of asset prices in regime-switching markets compared to traditional linear autoregressive models.
May improve long-horizon forecasts in regime-switching markets; leaves open real-world trading utility and robustness.
Linear autoregressive (LAR) models poorly predict asset prices in nonlinear, regime-switching markets. In this article, the authors use SETAR, a threshold model that accounts for nonlinearities, to test for the existence of regime-switching in global equity markets. A comparison of SETAR’s predictive power against that of LAR models suggests that SETAR yields more accurate long forecasts, in both emerging and developed stock markets. The authors discuss extensions of threshold models into portfolio management, corporate valuation, and the long-term forecasting of financial indicators. TOPICS: Portfolio management/multi-asset allocation, security analysis and valuation, factor-based models
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Pereiro et al. (2015) studied Asset prices in global equity markets. SETAR (threshold model) vs. Linear autoregressive (LAR) models was evaluated on Predictive power for long forecasts. SETAR, a threshold model accounting for nonlinearities, yielded more accurate long forecasts of asset prices in emerging and developed stock markets compared to linear autoregressive models.
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