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Over the last few decades, there has been vast interest in the modelling of asset returns using jump diffusion processes.
This was in part as a result of the realization that the standard diffusion processes, which do not allow for jumps, were not able to capture the stylized facts that return distributions are leptokurtic and have heavy tails.
Although jump diffusion models have been identified as being useful to capture these stylized facts, there has not been consensus as to how these jump diffusion models should be calibrated.
This dissertation tackles this calibration issue by considering the basic jump diffusion model of Merton (1976) applied to South African equity and interest rate market data.
As there is little access to frequently updated volatility surfaces and option price data in South Africa, the calibration methods that are used in this dissertation are those that require historical returns data only.
The methods used are the standard Maximum Likelihood Estimation (MLE) approach, the likelihood proling method of Honore (1998), the Method of Moments Estimation (MME) technique and the Expectation Maximisation (EM) algorithm.
I was born in South Africa.
I have a Business Science degree in Actuarial Science from UCT and an MPhil in Mathematical Finance from UCT.
I enjoy researching on statistical models and post most of my work on my blog: www.wilsonmongwe.co.za.
I am passionate about teaching and I am a co-founder of MOTS: www.motstutoring.co.za.
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