On the Estimation of Asymmetric Long Memory Stochastic Volatility Models
-
Omar Abbara
and
Mauricio Zevallos
Abstract
The asymmetric long memory stochastic volatility (A-LMSV) model has two attractive features for modeling financial returns: i) the autocorrelation function of the log-variance presents hyperbolic decay; and ii) the two random noises that define the model have nonzero correlation. In this work, we present a maximum likelihood method for estimating both the parameters and the unobserved components, together with a method for value-at-risk (VaR) forecasting. Our method takes advantage of a state-space representation of the model which is written as a dynamic linear model with Markov switching. Then, the likelihood can be readily calculated by the Kalman filter. The proposed method is assessed by Monte Carlo experiments, and illustrations of risk forecasting with real time series returns are provided.
Funding source: Fundação de Amparo à Pesquisa do Estado de São Paulo
Award Identifier / Grant number: 2018/04654-9
Award Identifier / Grant number: 2023/01728-0
Award Identifier / Grant number: 2023/02538-0
Acknowledgments
The authors thank the referee for their constructive comments and suggestions, which allow us to improve the paper. The second author acknowledges financial support from the São Paulo State Research Foundation (FAPESP), grant numbers 2018/04654-9, 2023/02538-0 and 2023/01728-0. The authors also acknowledge the support of the Center for Applied Research on Econometrics, Finance and Statistics (CAREFS).
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/snde-2024-0092).
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