Graduation Year
2026
Date of Submission
4-2026
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Economics
Reader 1
Benjamin Gillen
Abstract
This thesis studies volatility modeling in the context of risk parity portfolio construction. I compare three risk parity portfolios that differ only in their underlying volatility model: a historical covariance baseline, a Bayesian stochastic volatility model, and a GRU–GARCH hybrid neural network. Using daily returns on Kenneth French’s five industry portfolios from January 2016 through December 2025, I construct monthly rebalanced portfolios under each model, with the SV and GRU forecasts embedded in hybrid covariance matrices that combine forecasted volatilities with rolling historical correlations. The results document a divergence between forecast accuracy and portfolio performance: the SV model is the most accurate forecaster by every standard error metric, yet the GRU, the worst forecaster among the three, produces the best Sharpe and Sortino ratios. I diagnose this divergence through four channels: directional bias in forecast errors, portfolio concentration, turnover, and rank skill. Standard forecast error metrics are an incomplete guide to volatility model selection in a portfolio construction context.
Recommended Citation
Hou, Kenneth, "Volatility Modeling with an Application to Risk Parity Portfolios" (2026). CMC Senior Theses. 4183.
https://scholarship.claremont.edu/cmc_theses/4183
Included in
Data Science Commons, Econometrics Commons, Finance Commons, Other Economics Commons, Statistical Models Commons
