Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282890 
Year of Publication: 
2023
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2023-077/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
This paper considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation-based methods to address key challenges in parameter estimation, the filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to estimate the static parameters, and the extremum Monte Carlo method to extract latent volatility. Both methods can be easily adapted to modifications of the model, such as having other distributions for the errors and other dynamic specifications for the volatility process. Illustrations are presented for a simulated dataset and for an empirical application to a time series of Bitcoin returns.
Subjects: 
Filtering
Forecasting
Indirect Inference
Extremum Monte Carlo
Leverage
Bitcoin
Document Type: 
Working Paper

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