Date of Award


Degree Type

Doctoral Dissertation

Degree Name

Doctor of Business Administration (DBA)


Jack Welch College of Business


Submitted in partial fulfillment of the requirements for the degree of Doctor of Business Administration in Finance, Sacred Heart University, Jack Welch College of Business.

Dissertation Supervisor

Dr. W. Keener Hughen

Committee Member

Dr. Lucjan T. Orlowski

Committee Member

Dr. I-Hsuan Ethan Chiang


Forecasting volatility is a critical component of asset allocation, risk management, and option pricing. Many different methods and models are used to predict volatility, and many studies have examined the efficacy of one method or another. This study investigates whether the abilities of historical volatility, GARCH models, and VIX to forecast volatility vary in different market conditions, as distinguished by levels of volatility and returns. It is found that market conditions do impact the abilities of the variables to forecast volatility. Overall, the forecasts implied by the GARCH models perform best according to the various metrics, while the VIX forecast has the worst performance. However, with a simple linear correction for the bias, the forecasts implied by VIX and the leverage GARCH model perform best. Their relative performance depends on market conditions; the VIX forecast performs best during times when volatility is low. These results indicate that practitioners who wish to forecast volatility should take current market conditions into account.

JEL Classification

C22, C52, E37, G12

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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