A TEST OF THE PREDICTIVE POWER OF VOLATILITY FORECASTING MODELS
Open Access
Author:
Binder, Christian N
Area of Honors:
Finance
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Louis Gattis Jr., Thesis Supervisor Dr. Brian Spangler Davis, Thesis Honors Advisor
Keywords:
Finance Volatility Forecasting Econometrics Statistics Volatility Time Series Analysis GARCH Forecasting Implied Volatility
Abstract:
In this paper, a quantitative evaluation and ranking of commonly used volatility forecasting models and metrics is presented. The models and metrics included are Generalized Autoregressive Heteroscedasticity (GARCH), Exponentially-Weighted Moving Average (EWMA), Implied Volatility, long-term historical mean volatility, and a moving average volatility. The metrics were tested on a randomly-selected sample of US equities, international equity indices, currencies, and commodities on both historical data and in real-time, making this study unique. Models are ranked and compared via various statistical loss functions.
