Simulated Maximum Likelihood Estimation of Continuous Time Stochastic Volatility Models

• Main Authors: KLEPPE, Tore Selland, YU, Jun, SKAUG, Hans J.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1267; https://ink.library.smu.edu.sg/context/soe_research/article/2266/viewcontent/Simulated_Maximum_Likelihood_Estimation_of_Continuous_Time_Stochastic_Volatility_Models_2009.pdf
• Institution: Singapore Management University

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.