Hypotheses
GDX–GLD Bayesian Spread Reversion with Online Stability Monitoring
Derived by the Research Lead from Triantafyllopoulos and Montana's Dynamic modeling of mean-reverting spreads for statistical arbitrage: trade deviations in the GDX–GLD price spread only while an online Bayesian…
Hypothesis
Derived by the Research Lead from Triantafyllopoulos and Montana's Dynamic modeling of mean-reverting spreads for statistical arbitrage: trade deviations in the GDX–GLD price spread only while an online Bayesian TVAR(1) model supports local mean reversion. Use synchronized daily bars on GDX.USEQ and GLD.USEQ, preserving the paper's ETF example. The paper supplies the modeling mechanism, not a validated executable strategy; entry thresholds, exits, sizing and trading parameters below are Research Lead choices. Section 4.5's stated position direction appears inconsistent with convergence of a spread defined as primary minus hedge: this operationalization explicitly shorts a high spread and buys a low spread. Orders execute at the synchronized signal session's close; next-session-open execution is neither requested nor assumed. All performance expectations are unverified. Only one hypothesis is proposed because adaptive estimation and stability monitoring are components of the same trading mechanism.
Analysis
Dynamic modeling of mean-reverting spreads for statistical arbitrage
Backtest and paper results are hypothetical. Trading involves risk of loss.