Abstract

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.

Details

Title
Maximum likelihood estimation of stock volatility using jump-diffusion models
Author
Chekenya, Nixon S 1 

1  Midland State University, Gweru, Zimbabwe 
Publication year
2019
Publication date
Jan 2019
Publisher
Taylor & Francis Ltd.
e-ISSN
23322039
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1080/23322039.2019.1582318
ProQuest document ID
2351041993
Copyright
© 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.