Volatility estimation for COVID-19 daily rates using Kalman filtering technique
This paper discusses the use of stochastic modeling in the prognosis of Corona Virus-Infected Disease 2019 (COVID-19) cases. COVID-19 is a new disease that is highly infectious and dangerous. It has deeply shaken the world, claiming the lives of over a million people and bringing the world to a lock...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-07-01
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| Series: | Results in Physics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379721004241 |
| _version_ | 1818462221663993856 |
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| author | Md Al Masum Bhuiyan Suhail Mahmud Md Romyull Islam Nishat Tasnim |
| author_facet | Md Al Masum Bhuiyan Suhail Mahmud Md Romyull Islam Nishat Tasnim |
| author_sort | Md Al Masum Bhuiyan |
| collection | DOAJ |
| description | This paper discusses the use of stochastic modeling in the prognosis of Corona Virus-Infected Disease 2019 (COVID-19) cases. COVID-19 is a new disease that is highly infectious and dangerous. It has deeply shaken the world, claiming the lives of over a million people and bringing the world to a lockdown. So, the early detection of COVID is essential for the patients’ timely treatment and preventive measures. A filtering technique with time-varying parameters is presented to predict the stochastic volatility (SV) of COVID-19 cases. The time-varying parameters are estimated using the Kalman filtering technique based on the stochastic component of data volatility. Kalman filtering is essential as it removes insignificant information from the data. We forecast one-step-ahead predicted volatility with ±3 standard prediction errors, which is implemented by Maximum Likelihood Estimation. We conclude that Kalman filtering in conjunction with the SV model is a reliable predictive model for COVID-19 since it is less constrained by the past autoregressive information. |
| first_indexed | 2024-12-14T23:58:36Z |
| format | Article |
| id | doaj.art-f9f58bdac0ae4841b42811d52329cd11 |
| institution | Directory Open Access Journal |
| issn | 2211-3797 |
| language | English |
| last_indexed | 2024-12-14T23:58:36Z |
| publishDate | 2021-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj.art-f9f58bdac0ae4841b42811d52329cd112022-12-21T22:43:01ZengElsevierResults in Physics2211-37972021-07-0126104291Volatility estimation for COVID-19 daily rates using Kalman filtering techniqueMd Al Masum Bhuiyan0Suhail Mahmud1Md Romyull Islam2Nishat Tasnim3Austin Peay State University, USAThe Pennsylvania State University, USA; Corresponding author.Daffodil International University, BangladeshDaffodil International University, BangladeshThis paper discusses the use of stochastic modeling in the prognosis of Corona Virus-Infected Disease 2019 (COVID-19) cases. COVID-19 is a new disease that is highly infectious and dangerous. It has deeply shaken the world, claiming the lives of over a million people and bringing the world to a lockdown. So, the early detection of COVID is essential for the patients’ timely treatment and preventive measures. A filtering technique with time-varying parameters is presented to predict the stochastic volatility (SV) of COVID-19 cases. The time-varying parameters are estimated using the Kalman filtering technique based on the stochastic component of data volatility. Kalman filtering is essential as it removes insignificant information from the data. We forecast one-step-ahead predicted volatility with ±3 standard prediction errors, which is implemented by Maximum Likelihood Estimation. We conclude that Kalman filtering in conjunction with the SV model is a reliable predictive model for COVID-19 since it is less constrained by the past autoregressive information.http://www.sciencedirect.com/science/article/pii/S2211379721004241Kalman filteringCOVID-19 time seriesMaximum likelihood estimationVolatility modelWhittle likelihood |
| spellingShingle | Md Al Masum Bhuiyan Suhail Mahmud Md Romyull Islam Nishat Tasnim Volatility estimation for COVID-19 daily rates using Kalman filtering technique Results in Physics Kalman filtering COVID-19 time series Maximum likelihood estimation Volatility model Whittle likelihood |
| title | Volatility estimation for COVID-19 daily rates using Kalman filtering technique |
| title_full | Volatility estimation for COVID-19 daily rates using Kalman filtering technique |
| title_fullStr | Volatility estimation for COVID-19 daily rates using Kalman filtering technique |
| title_full_unstemmed | Volatility estimation for COVID-19 daily rates using Kalman filtering technique |
| title_short | Volatility estimation for COVID-19 daily rates using Kalman filtering technique |
| title_sort | volatility estimation for covid 19 daily rates using kalman filtering technique |
| topic | Kalman filtering COVID-19 time series Maximum likelihood estimation Volatility model Whittle likelihood |
| url | http://www.sciencedirect.com/science/article/pii/S2211379721004241 |
| work_keys_str_mv | AT mdalmasumbhuiyan volatilityestimationforcovid19dailyratesusingkalmanfilteringtechnique AT suhailmahmud volatilityestimationforcovid19dailyratesusingkalmanfilteringtechnique AT mdromyullislam volatilityestimationforcovid19dailyratesusingkalmanfilteringtechnique AT nishattasnim volatilityestimationforcovid19dailyratesusingkalmanfilteringtechnique |