Interest rate models with non-gaussian driven stochastic volatility
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In this thesis, we consider some two-factor short rate models that incorporate stochastic volatility with jumps. The motivation for studying such kinds of model is to overcome the shortcomings of di usion-based stochastic models and to provide a more accurate description of the empirical characteristics of the short rates. In our rst model, a jump process for the short-rate volatility is described with jump times generated by a Poisson process and with jump sizes following exponential distribution. Secondly, we extend the volatility model further by taking a superposition of two independent jump processes. We present the corresponding Markov chain Monte Carlo estimation algorithm and provide estimation results of candidate model parameters, latent volatility processes and the jump processes using the 3- month U.S. Treasury Bill rates. Finally, we apply our models to price fixed-income products through Monte Carlo simulation.