Most of the empirical applications of the stochastic volatility (SV) model are based on the assumption that the conditional distribution of returns, given the latent volatility process, is normal. In this paper, the SV model based on a conditional normal distribution is compared with SV speciยฎcation
Modelling the tail of a normal distribution
โ Scribed by A.A. Zhiglyavskii
- Publisher
- Elsevier Science
- Year
- 1983
- Weight
- 203 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0041-5553
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
An exact asymptotic formula for the tail probability of a multivariate normal distribution is derived. This formula is applied to establish two asymptotic results for the maximum deviation from the mean: the weak convergence to the Gumbel distribution of a normalized maximum deviation and the precis
Investigating random discrete structures, one often needs upper bounds on the tail of the hypergeometric distribution, with k = (p+ t)n for p = M/N and some t 20. A particularly useful bound, [l] W. Ha&ding, Probability inequalities far sums af bounded randam variables, J. Am, Statist.