Stochastic Volatility Models with Skewness Selection
Published in Entropy, 2024
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.In a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks’ mandates.
Recommended citation: Martins, Igor, and Hedibert Freitas Lopes. 2024. "Stochastic Volatility Models with Skewness Selection" Entropy 26, no. 2: 142.
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