  Time-series with time-varying variance pose a unique challenge to uncertainty
quantification (UQ) methods. Time-varying variance, such as volatility
clustering as seen in financial time-series, can lead to large mismatch between
predicted uncertainty and forecast error. Building on recent advances in neural
network UQ literature, we extend and simplify Deep Evidential Regression and
Deep Ensembles into a unified framework to deal with UQ under the presence of
volatility clustering. We show that a Scale Mixture Distribution is a simpler
alternative to the Normal-Inverse-Gamma prior that provides favorable
complexity-accuracy trade-off. To illustrate the performance of our proposed
approach, we apply it to two sets of financial time-series exhibiting
volatility clustering: cryptocurrencies and U.S. equities.
