Multiple time scales and the empirical models for stochastic volatility
β Scribed by G.L. Buchbinder; K.M. Chistilin
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 328 KB
- Volume
- 379
- Category
- Article
- ISSN
- 0378-4371
No coin nor oath required. For personal study only.
β¦ Synopsis
The most common stochastic volatility models such as the Ornstein-Uhlenbeck (OU), the Heston, the exponential OU (ExpOU) and Hull-White models define volatility as a Markovian process. In this work we check the applicability of the Markovian approximation at separate times scales and will try to answer the question which of the stochastic volatility models indicated above is the most realistic. To this end we consider the volatility at both short (a few days) and long (a few months) time scales as a Markovian process and estimate for it the coefficients of the Kramers-Moyal expansion using the data for Dow-Jones Index. It has been found that the empirical data allow to take only the first two coefficients of expansion to be non-zero that define form of the volatility stochastic differential equation of ItoΛ. It proved to be that for the long time scale the empirical data support the ExpOU model. At the short time scale the empirical model coincides with ExpOU model for the small volatility quantities only.
π SIMILAR VOLUMES
A flexible design algorithm for hierarchical multiple model adaptive control, under the assumption that the central and any local stations have different knowledge of the hypotheses on the unknown parameters, can be applied for constructing a failure detection and identification system in a decentra
## Abstract The use of multiple models for adaptively controlling an unknown continuousβtime linear system was proposed in Narendra and Balakrishnan (__IEEE Transactions on Automatic Control__ 1994; **39**(9):1861β1866). and discussed in detail in Narendra and Xiang (__IEEE Transactions on Automati