| Author | : Yacine Aït-Sahalia |
| File Size | : 49,5 Mb |
| Publisher | : Unknown |
| Language | : English |
| Release Date | : 02 June 2024 |
| ISBN | : OCLC:249834367 |
| Pages | : 42 pages |
Maximum Likelihood Estimation of Stochastic Volatility Models by Yacine Aït-Sahalia Book PDF Summary
We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.