Date of Award


Degree Name

Doctor of Philosophy



First Advisor

Dr. Matthew L. Higgins

Second Advisor

Dr. Mark Wheeler

Third Advisor

Dr. Kevin Corder


Asset price bubbles, Markov regime switching, transition probability, hedge, safe haven, monetary policy


This dissertation examines various issues associated with asset price bubbles. In the first essay, a Markov regime-switching model with time-varying transition probabilities is developed to identify asset price bubbles in the S&P 500 Index. The model nests two different methodologies; a state-dependent regime-switching model and a Markov regime-switching model. Three bubble regimes are identified; dormant, explosive, and collapsing. Time-varying transition probabilities are specified for each of the nine possible transitions in the Markov regime-switching model. Estimation of the model is done using conditional maximum likelihood with the Hamilton filter. Results show that transition probabilities depend significantly on trading volume and relative size of the bubble. Overall, the model works well in detecting multiple bubbles in the S&P 500 between January 1888 and May 2010.

In the second essay, a cross-market propagation of asset price bubbles is analyzed using a three-regime multivariate Markov switching model. The three bubble regimes identified are dormant (characterized by high returns and low volatility), explosive (characterized by high returns and high volatility), and collapse (characterized by low returns and high volatility). Results show that bubbles in the price of crude oil are influenced by bubble sizes in the S&P 500 Index and the price of gold. The bubble dynamics in gold price are driven by the bubble size in the S&P 500 Index. Lastly, bubbles in the S&P 500 Index tend to be driven largely by bubbles in crude oil price. Gold appears to be the most stable asset, having the least impact from the rest of the market. The stability in gold price provides a case for gold serving as a safe haven asset in times of crisis or a hedge in normal times. The study uses monthly data from July 1989 to December 2014.

Finally, the third essay investigates the role of the Federal Reserve in the housing bubble between 2000 and 2006 as well as the eventual collapse of the bubble during the Great Recession. A mean group panel VAR is estimated for U.S states that experienced housing bubbles during the period. Two transmission channels are identified: an interest rate channel and a credit channel. The interest rate channel is traced with 30-year fixed mortgage rates whereas the credit channel is traced with real estate loans by all commercial banks in the U.S. Results show that the interest rate channel produces a greater impact on housing bubbles, following an expansionary monetary policy shock. The credit channel has a lower impact on housing bubbles following a monetary policy shock. The direct impact of a monetary policy shock on real estate loans gives evidence on the lending behavior of commercial banks in periods leading up to the recession. Overall, evidence shows that the Federal Reserve had a significant role in the housing bubble and the subsequent Great Recession. The date for the study spans 1998 to 2008.

Access Setting

Dissertation-Open Access

Included in

Economics Commons