Stochastic volatility models are fundamental tools in finance for accurately estimating and managing risks, primarily due to their ability to accommodate a dynamic and time-varying volatility structure. However, a notable constraint within these models is the reliance on Gaussian processes to model the latent (log-)variance, which can limit their ability to effectively capture events such as sudden jumps or spikes in the latent volatility. To address this limitation, we employ a non-Gaussian SV model utilizing an inference procedure that combines Laplace and Variational Bayes approximations. Our study showcases the significant advantages of this correction in modeling the conditional variance of Bitcoin's return series.
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