Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2023-12-02
Number: 23-077/III
Author-Name: Francisco Blasques
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Siem Jan Koopman
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Karim Moussa
Author-Workplace-Name: Vrije Universiteit Amsterdam
Title: Asymmetric Stable Stochastic Volatility Models: Estimation, Filtering, and Forecasting
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.
Classification-JEL: C22, C46, C58
Keywords: Filtering, Forecasting, Indirect Inference, Extremum Monte Carlo, Leverage, Bitcoin
ExperimentFile-URL: https://papers.tinbergen.nl/23077.pdf
File-Format: application/pdf
File-Size:974.033 bytes
Handle: RePEc:tin:wpaper:20230077