Publication Type
Journal Article
Version
submittedVersion
Publication Date
2010
Abstract
In this chapter we develop and implement a method for maximum simulated likelihood estimation of the continuous time stochastic volatility model with the constant elasticity of volatility. The approach does not require observations on option prices, nor volatility. To integrate out latent volatility from the joint density of return and volatility, a modified efficient importance sampling technique is used after the continuous time model is approximated using the Euler–Maruyama scheme. The Monte Carlo studies show that the method works well and the empirical applications illustrate usefulness of the method. Empirical results provide strong evidence against the Heston model.
Discipline
Econometrics
Research Areas
Econometrics
Publication
Advances in Econometrics
Volume
26
First Page
137
Last Page
161
ISSN
0731-9053
Identifier
10.1108/S0731-9053(2010)0000026009
Publisher
Emerald
Citation
KLEPPE, Tore Selland; YU, Jun; and SKAUG, Hans J..
Simulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models. (2010). Advances in Econometrics. 26, 137-161.
Available at: https://ink.library.smu.edu.sg/soe_research/1267
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1108/S0731-9053(2010)0000026009