Analyzing the effects of demand uncertainty on inventory in six United States retail sectors using multivariate GARCH-M models and life and property-casualty insurance industry comparisons four years after the enactment of GLBA

Chapter 1. This study examines the relationship between inventory and demand uncertainty in six retail sectors and compares the results with previous studies that focused on the more aggregate retail data. Using the constant correlation bivariate GARCH-in-Mean with the vector error correction model (VECM) as the form of mean equation and assuming the GARCH(1,1) process as the measure of demand uncertainty, previous studies find no significant relationships between inventory and demand volatility in aggregate retail. Using the same model for six retail sectors, this study finds the same results. This study shows positive effects of demand uncertainty on inventory holdings by replacing the VAR for VECM as the form of mean equation, particularly in building material, furniture, auto dealers and general merchandise, using both constant correlation and diagonal-BEKK models and in food using the diagonal-BEKK model. A significantly negative relation exists in food retail using a constant correlation model. A mixed result is observed in apparel and general merchandise between different scenarios within the VAR diagonal models. In food, furniture, auto-dealers, and general merchandise retailers, the demand volatilities are time-varying across models. In building material retail, the time-varying demand volatility is not observed in the VECM with a constant correlation. In apparel, the time-varying demand volatility is not observed in both the VAR and VECM with constant correlation. In addition, interrelationships exist between inventory and sales in all six retail sectors in one or in a combination of these forms: (1) their dependence on each other's level, (2) their being influenced by each other's volatility, and (3) their significant coefficient of correlations. Previous studies show that aggregate retail inventory and demand are cointegrated and the same results are shown in this study in the six retail sectors.; Chapter 2. Following GLBA of 1999, previous studies recommended that banks enter the life insurance industry rather than property casualty insurance based on their prediction that the combined firm would have a less volatile return. Their recommendations are based on industry return and volatility data before the enactment of GLBA in which the life insurance firms had less volatile returns than property-casualty insurance. The theoretical background links a higher revenue or demand for financial institution's products with a safer or lower-risk financial institution. This study uses different data and finds that the return and volatility in each and between these two industry segments do not differ significantly upon the enactment of GLBA. This study uses regression models to analyze the premiums earned by the company and the loss ratio to see whether life insurance is more attractive for entrants than the property-casualty industry. The regression results show that during the four-year period after the enactment of GLBA, premiums earned by a life insurance company are higher than premiums earned by property-casualty insurance. The loss ratio equations show that during the four-year period, the loss ratio experienced by life insurance is higher than that experienced by property-casualty insurance.