Date of Graduation
8-2023
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Statistics and Analytics
Advisor/Mentor
Petris, Giovanni G.
Committee Member
Chakraborty, Avishek A.
Second Committee Member
Plummer, Sean
Keywords
Generalized Auto-Regressive Conditional Heteroskedasticity model; Stochastic Volatility model; Hamiltonian Monte Carlo; Sequential Monte Carlo
Abstract
This paper compares the predictive performance of two commonly used financial models, the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model, and the Stochastic Volatility model. Both techniques are used in the finance literature to model returns on an asset; the main difference between the two is that the former holds volatility as deterministic, whereas the latter treats it as a stochastic component. Three 10-year periods (2006-15, 2008-17, and 2010-19) of returns of the S&P-500 Index are used to train the two models. The parameter estimation is done using Hamiltonian Monte Carlo. Then, using Sequential Monte Carlo updates, returns for 2016, 2018, and 2020 are predicted, and their performance over different time frames—one month, one quarter, and one year—are compared using sum of squared errors (SSE). In addition, the percentage of observed returns for the three years that are captured between the 2.5 percentile and the 97.5 percentile of the predictions are compared. The results of the two models are practically indistinguishable by both criteria.
Citation
Nath, S. (2023). Comparing Predictive Performance of GARCH and Stochastic Volatility Models. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4881
