Estimation and forecasting of long memory stochastic volatility models

Abbara Omar () and Mauricio Zevallos
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Abbara Omar: Canvas Capital, Sao Paulo, Brazil

Studies in Nonlinear Dynamics & Econometrics, 2023, vol. 27, issue 1, 1-24

Abstract: Stochastic Volatility (SV) models are an alternative to GARCH models for estimating volatility and several empirical studies have indicated that volatility exhibits long-memory behavior. The main objective of this work is to propose a new method to estimate a univariate long-memory stochastic volatility (LMSV) model. For this purpose we formulate the LMSV model in a state-space representation with non-Gaussian perturbations in the observation equation, and the estimation of parameters is performed by maximizing the likelihood written in terms derived from a Kalman filter algorithm. We also present a procedure to calculate volatility and Value-at-Risks forecasts. The proposal is evaluated by means of Monte Carlo experiments and applied to real-life time series, where an illustration of market risk calculation is presented.

Keywords: mixtures; non-Gaussian errors; value-at-risk (search for similar items in EconPapers)
JEL-codes: C22 C53 G15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/snde-2020-0106

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