Smoothing Variances Across Time
Adaptive Stochastic Volatility
Bibliographic Data
| ID | 19418202 |
|---|---|
| Authors | Jason B Cho (0000-0002-4801-5514, Cornell University, corresponding author), David S Matteson (0000-0002-2674-0387, Cornell University) |
| Year | 2026 |
| Pages | 1-14 |
| Publication date | 2026-03-06 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2025.2602846 |
| OpenAlex | W7117475076 |
| Language | EN |
| References cited | 60 |
We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We further enhance the model by incorporating a nugget effect, allowing it to flexibly capture small-scale variability while preserving smoothness elsewhere. We derive the theoretical properties of the global-local shrinkage prior DSP. Simulation studies demonstrate that ASV is highly robust to misspecification, consistently recovering the latent volatility structure across a wide range of data-generating processes. Furthermore, ASV’s capacity to yield locally smooth and interpretable estimates facilitates a clearer understanding of the underlying patterns and trends in volatility. As an extension, we develop the Bayesian Trend Filter with ASV (BTF-ASV) which allows joint modeling of the mean and volatility with abrupt changes. Finally, our proposed models are applied to time series data from finance, econometrics, and environmental science, highlighting their flexibility and broad applicability
Smoothing · Stochastic process · Stochastic volatility · Financial Risk and Volatility Modeling · Risk and Portfolio Optimization · Stochastic processes and financial applications
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| Citation velocity | historical |
|---|---|
| Highly cited | No |