Degree Type
Creative Component
Semester of Graduation
Fall 2020
Department
Statistics
First Major Professor
Cindy Long Yu
Degree(s)
Master of Science (MS)
Major(s)
Statistics
Abstract
Stochastic volatility (SV) is known to be advantageous to capture important stylized features in real financial markets. Based on a time-discretized return dynamics model involving SV, we develop a Markov Chain Monte Carlo (MCMC) method for the estimation of the structure parameters and SV as the latent variable in the model. Simulation studies are conducted to test the performance of the MCMC method in terms of the convergence to the true parameters and volatility. Applying the method to the time series data of two distinct assets in the real markets, we empirically assess the inference that provides evidence that the mixture of the two assets helps to reduce volatility without compromising long-term returns. According to the model diagnostics, we find out that the inclusion of a jump in the model is likely to enhance the estimation.
Copyright Owner
Jang, Minsung
Copyright Year
2020
File Format
Embargo Period (admin only)
11-12-2020
Recommended Citation
Jang, Minsung, "MCMC Estimation of Return Dynamics with Stochastic Volatility" (2020). Creative Components. 650.
https://lib.dr.iastate.edu/creativecomponents/650
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