Nikolakopoulos, Efthimios (2025) Bayesian nonparametric modeling of stochastic volatility. Quantitative Finance. pp. 1-16. DOI https://doi.org/10.1080/14697688.2025.2509561
Nikolakopoulos, Efthimios (2025) Bayesian nonparametric modeling of stochastic volatility. Quantitative Finance. pp. 1-16. DOI https://doi.org/10.1080/14697688.2025.2509561
Nikolakopoulos, Efthimios (2025) Bayesian nonparametric modeling of stochastic volatility. Quantitative Finance. pp. 1-16. DOI https://doi.org/10.1080/14697688.2025.2509561
Abstract
This paper introduces a novel discrete-time stochastic volatility model that employs a countably infinite mix- ture of AR(1) processes, with a Dirichlet process prior, to nonparametrically model the latent volatility. Realized variance (RV) is incorporated as an ex post signal to enhance volatility estimation. The model effectively cap- tures fat tails and asymmetry in both return and log(RV) conditional distributions. Empirical analysis of three major stock indices provides strong evidence supporting the nonparametric stochastic volatility. The results re- veal that the volatility equation components exhibit significant variation over time, enabling the estimation of a more dynamic volatility process that better accommodates extreme returns and variance shocks. The new model delivers out-of-sample density forecasts with strong evidence of improvement, particularly for returns, log(RV), and the left region of the return distribution, including negative returns and extreme movements below −1% and −2%. The new approach provides improvements in forecasting the tail-risk measures of value-at-risk and expected shortfall.
Item Type: | Article |
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Uncontrolled Keywords: | Stochastic volatility, Realized variance, Bayesian nonparametrics, Dirichlet process mixture, Density forecasting |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School Faculty of Social Sciences > Essex Business School > Essex Finance Centre |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 29 May 2025 15:15 |
Last Modified: | 25 Jun 2025 12:04 |
URI: | http://repository.essex.ac.uk/id/eprint/40911 |
Available files
Filename: Bayesian nonparametric modelling of stochastic volatility.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0