Implementation of Bivariate Unspanned Stochastic Volatility Models

Unspanned stochastic volatility term structure models have gained popularity in the literature. This dissertation focuses on the challenges of implementing the simplest case – bivariate unspanned stochastic volatility models, where there is one state variable controlling the term structure, and one scaling the volatility. Specifically, we consider the Log-Affine Double Quadratic (1,1) model of Backwell (2017). In the class of affine term structure models, state variables are virtually always spanned and can therefore be inferred from bond yields. When fitting unspanned models, it is necessary to include option data, which adds further challenges. Because there are no analytical solutions in the LADQ (1,1) model, we show how options can be priced using an Alternating Direction Implicit finite difference scheme. We then implement an Unscented Kalman filter — a non-linear extension of the Kalman filter, which is a popular method for inferring state variable values — to recover the latent state variables from market observable data

Grantor dc:publisher.institution

Department of Mathematics and Applied Mathematics

Year dc:date.issued

2018

Author dc:creator

• Cullinan, Cian

Advisor dc:contributor.advisor

• Backwell, Alex

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/29266

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/29266