Andrew D. Sanford

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This paper demonstrates the application of Bayesian simulation-based estimation to a class of interest rate models known as Affine Term Structure (ATS) models. The technique used is based on a Markov Chain Monte Carlo algorithm, with the discrete observations on yields augmented by additional higher frequency latent data. The introduction of augmented yield(More)
Working Abstract: It is only recently that banks have begun to take seriously the measurement and management of operational risks. The inclusion of operational risk within Basel II is evidence of its increased importance. Although much previous research into operational risk has been directed at methods for determining levels of economic capital, our(More)
A Bayesian simulation-based method is developed for estimating a class of interest rate models known as Affine Term Structure (ATS) models. The technique is based on a Markov Chain Monte Carlo algorithm, with the discrete observations on yields augmented by additional higher frequency latent data. The introduction of augmented yield data reduces the bias(More)
The business unit level operational risk manager is responsible for measuring, recording, predicting, communicating and controlling operational risks within their organizational units. In support of the risk manager's role, Bayesian networks have been recommended as a tool for operational risk management. In this research, we describe the development of a(More)
This paper provides an empirical analysis of a range of alternative single-factor continuous time models for the Australian short-term interest rate. The models are indexed by the level effect parameter for the volatility in the short rate process. The inferential approach adopted is Bayesian, with estimation of the models proceeding via a Markov Chain(More)
AbstrAct A differential item functioning analysis is performed on a cohort of E-Learning students undertaking a unit in computational finance. The motivation for this analysis is to identify differential item functioning based on attributes of the student cohort that are unobserved. The authors find evidence that a model containing two distinct latent(More)
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