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Modeling log-volatility with zero returns: empirical evidence for asymmetric SV and log-GARCH models | ||
| Journal of Mathematical Modeling | ||
| دوره 14، شماره 2، مرداد 2026، صفحه 787-801 اصل مقاله (1.06 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2026.31979.2889 | ||
| نویسندگان | ||
| Abdeljalil Settar* 1؛ Sara Chegdal1؛ Mustapha Kabil1؛ Ghassane Benrhmach2 | ||
| 1Laboratory of mathematics, computer science and applications (LMCSA), FST Mohammedia, Hassan II university, 20650 Casablanca, Morocco | ||
| 2Department of Mathematics and Statistics, College of Engineering, Abu Dhabi University, 59911 Abu Dhabi, UAE | ||
| چکیده | ||
| In this work, we address the challenges posed by zero returns in both stochastic volatility (SV) and log-GARCH models in their asymmetric form. Building upon EM imputation for handling zero returns, we propose a unified approach that enhances parameter estimation robustness for both model classes. Specifically, we employ the Quasi-Maximum Likelihood (QML) estimation, incorporating the Kalman filter for both asymmetric SV and asymmetric log-GARCH models, to ensure robust parameter estimation even in the presence of zero returns. By comparing the performance of these models under our proposed framework, we provide new insights into their relative strengths in capturing the asymmetric volatility dynamics in the presence of zero returns. This contribution extends the existing literature by proposing a computational framework applicable to such models, based on a logarithmic specification of volatility. | ||
| کلیدواژهها | ||
| Stochastic volatility؛ log-GARCH؛ zero returns؛ quasi-maximum likelihood؛ Kalman filter | ||
| مراجع | ||
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