This article considers the daily yield of a financial asset for the purpose of modeling and comparing its stochastic volatility probability density. To do so, ARCH models and their extensions in discrete time are proposed as well as the empirical stochastic volatility mo-del developed by Paul Wilmott. For the discrete case, the models that enable estimating the conditional heterocedastic volatility in an instant t of time, t∈[1,T] are shown. For the continuous case, an Itô dissemination process is associated with the stochastic volatility of the financial series; that enables making said process discrete and simulating it, to obtain empirical volatility probability densities. Finally, the results are illustrated and compared to the methodologies discussed in the case of the financial series United Status S&P 500, the Mexican Stock Exchange Price and Quote Index (IPC is the Mexican acronym), and the Colombian Stock Exchange General Index (IGBC is the Colombian acronym).
|Título traducido de la contribución||A continuous model and a discrete model for estimating the stochastic volatility probability density of financial series yields|
|Idioma original||Español (Colombia)|
|Número de páginas||20|
|Publicación||Cuadernos de Administracion|
|Estado||Publicada - 1 ene 2008|
- Itô dissemination processes
- Probability density function