Maximum likelihood estimation of stock volatility using jump-diffusion models
We investigate whether there are systematic jumps in stock prices using the Brownian motion approach and Poisson processes to test diffusion and jump risk, respectively, on Johannesburg Stock Exchange and whether these jumps cause asset return volatility. Using stock market data from June 2002 to Se...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-01-01
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| Series: | Cogent Economics & Finance |
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| Online Access: | http://dx.doi.org/10.1080/23322039.2019.1582318 |
| _version_ | 1818415397830918144 |
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| author | Nixon S. Chekenya |
| author_facet | Nixon S. Chekenya |
| author_sort | Nixon S. Chekenya |
| collection | DOAJ |
| description | We investigate whether there are systematic jumps in stock prices using the Brownian motion approach and Poisson processes to test diffusion and jump risk, respectively, on Johannesburg Stock Exchange and whether these jumps cause asset return volatility. Using stock market data from June 2002 to September 2016, we hypothesize that stocks with high positive (negative) slopes are more likely to have large positive (negative) jumps in the future. As such, we expect to observe salient properties of volatility on listed stocks. We also conjecture that it is valid to use maximum likelihood procedures in estimating jumps in stocks. |
| first_indexed | 2024-12-14T11:34:21Z |
| format | Article |
| id | doaj.art-90c1ecd26c1f4ad8890f69c97ade46fe |
| institution | Directory Open Access Journal |
| issn | 2332-2039 |
| language | English |
| last_indexed | 2024-12-14T11:34:21Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Economics & Finance |
| spelling | doaj.art-90c1ecd26c1f4ad8890f69c97ade46fe2022-12-21T23:03:06ZengTaylor & Francis GroupCogent Economics & Finance2332-20392019-01-017110.1080/23322039.2019.15823181582318Maximum likelihood estimation of stock volatility using jump-diffusion modelsNixon S. Chekenya0Midland State UniversityWe investigate whether there are systematic jumps in stock prices using the Brownian motion approach and Poisson processes to test diffusion and jump risk, respectively, on Johannesburg Stock Exchange and whether these jumps cause asset return volatility. Using stock market data from June 2002 to September 2016, we hypothesize that stocks with high positive (negative) slopes are more likely to have large positive (negative) jumps in the future. As such, we expect to observe salient properties of volatility on listed stocks. We also conjecture that it is valid to use maximum likelihood procedures in estimating jumps in stocks.http://dx.doi.org/10.1080/23322039.2019.1582318merton jump diffusion modelblack scholes volatility (iv) curvesweiner processmaximum likelihood estimation |
| spellingShingle | Nixon S. Chekenya Maximum likelihood estimation of stock volatility using jump-diffusion models Cogent Economics & Finance merton jump diffusion model black scholes volatility (iv) curves weiner process maximum likelihood estimation |
| title | Maximum likelihood estimation of stock volatility using jump-diffusion models |
| title_full | Maximum likelihood estimation of stock volatility using jump-diffusion models |
| title_fullStr | Maximum likelihood estimation of stock volatility using jump-diffusion models |
| title_full_unstemmed | Maximum likelihood estimation of stock volatility using jump-diffusion models |
| title_short | Maximum likelihood estimation of stock volatility using jump-diffusion models |
| title_sort | maximum likelihood estimation of stock volatility using jump diffusion models |
| topic | merton jump diffusion model black scholes volatility (iv) curves weiner process maximum likelihood estimation |
| url | http://dx.doi.org/10.1080/23322039.2019.1582318 |
| work_keys_str_mv | AT nixonschekenya maximumlikelihoodestimationofstockvolatilityusingjumpdiffusionmodels |