# Stochastic Volatility Models with Skewness Selection

## TL;DR

This paper introduces a new method for modeling financial volatility that automatically determines if skewness is dynamic, static, or absent.

## Contribution

The novelty lies in using sparsity-inducing priors to select skewness dynamically without overparameterization.

## Key findings

- Dynamic skewness in bond yields reflects central bank monetary policies.
- Currency carry factors show no skewness after accounting for volatility.
- Carry crashes are better explained by volatility surges than by skewness.

## Abstract

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.