Abstract
In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the estimates are obtained by the maximum likelihood method. Monte Carlo experiments are performed to assess the quality of estimation. In addition, a backtesting exercise with the real-life time series illustrates that the proposed method is a quick and accurate alternative for forecasting value-at-risk.
Funder
São Paulo Research Foundation
Subject
Economics and Econometrics
Reference26 articles.
1. A note on stochastic volatility model estimation;Abbara;Brazilian Review of Finance,2019
2. Abbara, Omar, and Zevallos, Mauricio (2022). Estimation and forecasting of long memory stochastic volatility models. Studies in Nonlinear Dynamics and Econometrics.
3. Alternative assymmetric stochastic volatility models;Asai;Econometric Reviews,2011
4. Black, Fischer (1976). Proceedings of the Business and Economics Section of the American Statistical Association, American Statistical Association.
5. Evaluating interval forecasts;Christoffersen;Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance,1998