This thesis deals with two different topics, both related to modelling time-varying variances in high frequency financial time series. The first topic concerns the estimation of unobserved component models with autoregressive conditional heteroscedastic (ARCH) effects. The second topic concerns the quasi-maximum likelihood estimation of stochastic variance processes. These are an alternative to ARCH processes for modelling conditionally heteroscedastic time series. The motivation of the work is based on the increasing interest in the financial area in modelling volatility. In financial markets, many decisions are based on the volatility of a specific stock or index, which is closely related to the variance. Therefore, it is important to develop good statistical models able to describe time-varying variances. 2
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:308386 |
| Date | January 1992 |
| Creators | Ruiz Ortega, Esther |
| Publisher | London School of Economics and Political Science (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
Page generated in 0.0049 seconds