We analyze the impact of funding costs and margin requirements on index options traded on the CBOE. Assuming differential borrowing and lending rates, we derive no-arbitrage bounds for European options. We show that funding costs and the CBOE’s margin requirements lead to a price increase, which translates into skew and smile patterns for implied volatility curves even under constant volatilities. Empirical tests confirm that our model-implied slopes have significant statistical power in explaining the slopes observed in the market. Hence, at least in part, funding costs and collateral requirements offer an institutional explanation of the volatility smile phenomenon.
We analyze the impact of monetary policy on the supply of bank credit. Monetary policy affects both loan supply and demand, thus making identification a steep challenge. We therefore analyze a novel, supervisory dataset with loan applications from Spain. Accounting for time-varying firm heterogeneity in loan demand, we find that tighter monetary and worse economic conditions substantially reduce loan granting, especially from banks with lower capital or liquidity ratios; responding to applications for the same loan, weak banks are less likely to grant the loan. Finally, firms cannot offset the resultant credit restriction by applying to other banks. (JEL E32, E44, E52, G21, G32)
Banks are important providers of external finance to firms. In order to solve asymmetric information problems, firms and banks often engage in bank-firm relationships. Relationship banking occurs when a bank and a borrower enter multiple mutual interactions and both parties invest in obtaining some counterparty specific information, binding bank and firm, to a certain degree, to each other. This chapter starts with a discussion of reasons for having exclusive versus non-exclusive relationships. It provides a concise overview on the determinants of the number and intensity of bank-firm relationships, and reviews how relationship banking generates costs and benefits for both banks and firms. We show that on average bank-firm relationships generate value for both. The costs and benefits of bank-firm relationships, however, vary substantially with whether an economy is in normal or crisis times.
This paper utilizes means of game theory and option pricing to compute a bankruptcy triggering asset value. Combination of these two fields of economic study serves to separating the given problem into valuation of the payoffs, where we use option pricing and the analysis of strategic interactions between parties of a contract which could be designed and solved with the use of game theory. First of all, we design a contract between three parties each having a stake in the company, but with different rights reflected in the boundary conditions of the Black-Scholes equation. Then we will compute the values of debts and the whole value of the company. From here we directly compute the value of the firm’s equity and optimize it from the point of view of managing shareholders. The theoretically computed bankruptcy triggering asset value is then compared to the actual stock price. Depending on this relation, we may say whether the company is likely to go under or not. In addition, this article also provides reader with a real-life case study of the investment bank Bear Stearns and the optimal bankruptcy strategy in this particular case. As we will observe, the bankruptcy trigger computed in this example could have served as a good guide for predicting fall of this investment bank.
Cooper and Nyborg (2008) derive a tax-adjusted discount rate formula under a constant proportion leverage policy, investor taxes and risky debt. However, their analysis assumes zero recovery in default. We extend their framework to allow for positive recovery rates. We also allow for differences in bankruptcy codes with respect to the order of priority of interest payments versus repayment of principal in default, which may have tax consequences. The general formula we derive differs from that of Cooper and Nyborg when recovery rates in default are anticipated to be positive. However, under continuous rebalancing, the formula collapses to that of Cooper and Nyborg. We provide an explanation for why the effect of the anticipated recovery rate is not directly visible in the general continuous rebalancing formula, even though this formula is derived under the assumption of partial default. The errors from using the continuous approximation formula are sensitive to the anticipated recovery in default, yet small. The ‘cost of debt’ in the tax adjusted discount rate formula is the debt’s yield rather than its expected rate of return.
