Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models

Autor:	Omar Abbara, Mauricio Zevallos
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	non-Gaussian errors leverage effect value-at-risk Economics as a science HB71-74
Zdroj:	Econometrics, Vol 11, Iss 1, p 1 (2022)
Druh dokumentu:	article
ISSN:	2225-1146
DOI:	10.3390/econometrics11010001
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/05bc18db1a8d4ff5a1b6c39c082f7fac Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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