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Estimation and forecasting of long memory stochastic volatility models

Author

Listed:
  • Abbara Omar

    (Canvas Capital, Sao Paulo, Brazil)

  • Zevallos Mauricio

    (Department of Statistics, State University of Campinas, Campinas, Brazil)

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.

Suggested Citation

  • Abbara Omar & Zevallos Mauricio, 2023. "Estimation and forecasting of long memory stochastic volatility models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(1), pages 1-24, February.
    • Handle: RePEc:bpj:sndecm:v:27:y:2023:i:1:p:1-24:n:2
    DOI: 10.1515/snde-2020-0106
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    More about this item

    Keywords

    mixtures; non-Gaussian errors; value-at-risk;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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