Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models
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 ob...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Econometrics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2225-1146/11/1/1 |
| _version_ | 1797858456721424384 |
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| author | Omar Abbara Mauricio Zevallos |
| author_facet | Omar Abbara Mauricio Zevallos |
| author_sort | Omar Abbara |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T21:13:42Z |
| format | Article |
| id | doaj.art-05bc18db1a8d4ff5a1b6c39c082f7fac |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-04-09T21:13:42Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-05bc18db1a8d4ff5a1b6c39c082f7fac2023-03-28T13:28:47ZengMDPI AGEconometrics2225-11462022-12-01111110.3390/econometrics11010001Maximum Likelihood Inference for Asymmetric Stochastic Volatility ModelsOmar Abbara0Mauricio Zevallos1Canvas Capital S.A., Sao Paulo 04538-000, BrazilDepartment of Statistics, University of Campinas, Campinas 13083-859, BrazilIn 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.https://www.mdpi.com/2225-1146/11/1/1non-Gaussian errorsleverage effectvalue-at-risk |
| spellingShingle | Omar Abbara Mauricio Zevallos Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models Econometrics non-Gaussian errors leverage effect value-at-risk |
| title | Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models |
| title_full | Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models |
| title_fullStr | Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models |
| title_full_unstemmed | Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models |
| title_short | Maximum Likelihood Inference for Asymmetric Stochastic Volatility Models |
| title_sort | maximum likelihood inference for asymmetric stochastic volatility models |
| topic | non-Gaussian errors leverage effect value-at-risk |
| url | https://www.mdpi.com/2225-1146/11/1/1 |
| work_keys_str_mv | AT omarabbara maximumlikelihoodinferenceforasymmetricstochasticvolatilitymodels AT mauriciozevallos maximumlikelihoodinferenceforasymmetricstochasticvolatilitymodels |