Essays on the theory of corporate finance [electronic resource]
說明
120 p
附註
Source: Dissertation Abstracts International, Volume: 69-01, Section: A, page: 0319
Advisers: Harald Hau; Roman Inderst
Thesis (Ph.D.)--Institut Europeen d'Administration des Affaires (France), 2007
The first essay analyzes how two of the key tasks of (division) managers interact: the task to grow the business by creating new investment opportunities and the task to provide accurate information about these opportunities in the corporate budgeting process. We show how the interaction of these two tasks endogenously biases managers towards overinvesting in their own projects. This bias is further exacerbated if managers have to compete in an internal capital market. We show that incentive pay, which mitigates this bias, becomes steeper for managers of riskier and less profitable divisions. Finally, our model lends itself to a new and parsimonious theory of the firm
The second essay introduces incomplete information and Bayesian learning into a structural model of default. Equity and debt are interpreted as contingent claims written on an earnings process whose growth rate is unknown. I characterize the value of equity by the means of a bivariate partial differential equation and solve learning shareholders endogenous default problem, both analytically and numerically, as a free boundary problem. A numerical simulation of the model helps to reconcile the structural approach to credit risk with the empirical evidence by predicting (i) lower leverage, (ii) higher default probabilities, (iii) wider credit spreads. In particular, spreads at the upper end of the ratings spectrum rise relative to spreads at the lower end
The third essay introduces incomplete information and Bayesian learning into two standard models of irreversible investment. The payoff of the investment opportunity is perfectly observable but governed by a drift parameter that is unknown to the decision maker ex ante. I characterize the value of the option to invest by the means of a bivariate partial differential equation and solve the investment timing problem of a learning decision maker, both analytically and numerically, as a free boundary problem. My results suggest that the impact of incomplete information on both the value of the option to invest and the optimal investment policy is ambiguous. In particular, I show that - counter to the classic real options effect - drift uncertainty does not necessarily increase the value of the option to invest
