On strong binomial approximation for stochastic processes and applications for financial modelling
MetadataShow full item record
This paper considers binomial approximation of continuous time stochastic processes. It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by binomial processes, i.e., by processes with fixed size binary increments at sampling points. Moreover, this approximation can be causal, i.e., at every time it requires only past historical values of the underlying process. In addition, possibility of approximation of solutions of stochastic differential equations by solutions of ordinary equations with binary noise is established. Some consequences for the financial modelling and options pricing models are discussed.
Showing items related by title, author, creator and subject.
Chai, Qinqin (2013)In this thesis, we develop new computational methods for three classes of dynamic optimization problems: (i) A parameter identification problem for a general nonlinear time-delay system; (ii) an optimal control problem ...
Reducing the dimensionality of hyperspectral remotely sensed data with applications for maximum likelihood image classificationSantich, Norman Ty (2007)As well as the many benefits associated with the evolution of multispectral sensors into hyperspectral sensors there is also a considerable increase in storage space and the computational load to process the data. ...
Size exclusion chromatography as a tool for natural organic matter characterisation in drinking water treatmentAllpike, Bradley (2008)Natural organic matter (NOM), ubiquitous in natural water sources, is generated by biogeochemical processes in both the water body and in the surrounding watershed, as well as from the contribution of organic compounds ...