New Model Reveals When Asset Price Swings Are Truly Predictable
Researchers have developed a statistical framework that automatically determines whether shifts in asset prices follow patterns worth trading on—or whether they're just noise. The advance matters because it cuts through overcomplex financial models that claim to predict currency crashes and interest rate moves but actually just overfit historical data.
Originaltitel: Stochastic Volatility Models with Skewness Selection
<p>This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks' mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.</p>