Research Program in Finance

Excessive leverage, i.e. the abuse of debt financing, is considered one of the  primary factors in the default of financial institutions. Systemic risk results from correlations between individual default probabilities that cannot be considered independent. Based on the structural framework by Merton (1974), we discuss a model in which these  correlations arise from overlaps in banks' portfolios. Portfolio  diversification is used as a strategy to mitigate losses from investments in risky projects. We calculate an optimal level of  diversification that has to be reached for a given level of excessive leverage to still mitigate an increase in systemic risk. In our  model, this optimal diversification further depends on the market size and the market conditions (e.g. volatility). It allows to distinguish between a safe regime, in which excessive leverage does not result in an increase of systemic risk, and a risky regime, in which excessive leverage cannot be mitigated leading to an increased systemic risk. Our results are of relevance for financial regulators.

Dealers need to search for quotes in many of the world's largest markets (such as spot foreign exchange, US government bonds, and the London Stock Exchange). This search affects trading cost. We estimate the share of total trading cost attributable to search. Our experiments show that the share is large -- roughly one-third of the effective spread. Past work on estimating spread components typically omits the search component. Our estimates suggest this omission is important.

This paper reconsiders return-volume dependence for the U.S. and six international equity markets. We contribute to previous work by proposing surprise volume as a new proxy for private information flow and apply extreme value theory in studying dependence for large volume and return, i.e. under situations of market stress. Results from a GARCH-M model indicate that surprise volume is superior in explaining conditional variance and reveals a positive market risk premium. Under conditions of market stress, the return-volume dependence is weaker, albeit mostly significant. The results for the U.S. market are most pronounced in that surprise volume explains ARCH- as well as leverage-effects and, under market stress, the return-volume dependence remains significant and symmetric. For the European and Asian markets, however, the dependence is weaker with asymmetry under market stress, i.e. small minimal returns show lower volume dependence than large maximal returns. We argue that our results are more consistent with a Gennotte and Leland (1990) misinterpretation hypothesis for market crashes than with cascade or behavioral explanations which associate high volume with steep price declines.

Accurate modeling of extremal price changes is vital to financial risk management. We examine the small sample properties of adaptive tail index estimators under the class of student-t marginal distribution functions including GARCH and propose a model-based bias-corrected estimation approach. Our simulation results indicate that bias strongly relates to the underlying model and may be positively as well as negatively signed. The empirical study of daily exchange rate changes reveals substantial differences in measured tail-thickness due to small sample bias. As a consequence, high quantile estimation may lead to a substantial underestimation of tail risk.

Mortgage-backed securities, with their relative structural simplicity and their lack of recovery rate uncertainty if default occurs, are particularly suitable for developing and testing risky debt valuation models. In this paper, we develop a two-factor structural mortgage pricing model in which rational mortgage-holders endogenously choose when to prepay and default subject to i. explicit frictions (transaction costs) payable when terminating their mortgages, ii. exogenous background terminations, and iii. a credit related impact of the loan-to-value ratio (LTV) on prepayment. We estimate the model using pool-level mortgage termination data for Freddie Mac Participation Certificates, and find that the effect of the house price factor on the results is both statistically and economically significant. Out-of-sample estimates of MBS prices produce option adjusted spreads of between 5 and 25 basis points, well within quoted values for these securities.

Estimation of the tail index of stationary, fat-tailed return distributions is non-trivial since the well-known Hill estimator is optimal only under iid draws from an exact Pareto model. We provide a small sample simulation study of recently suggested adaptive estimators under ARCH-type dependence. The Hill estimator's performance is found to be dominated by a ratio estimator. Dependence increases estimation error which can remain substantial even in larger data sets. As small sample bias is related to the magnitude of the tail index, recent standard applications may have overestimated (underestimated) the risk of assets with low (high) degrees of fat-tailedness.

This paper presents the logic behind the increasingly neglected proposition that prices set in developed financial markets are determined as if all investors are rational. It contends that realistically, market rationality needs to be defined so as to allow investors to be uncertain about the characteristics of other investors in the market. It also argues that investor irrationality, to the extent it affects prices, is particularly likely to be manifest through overconfidence, which in turn is likely to make the market in an important sense too efficient, rather than less efficient, in reflecting information. To illustrate, the paper ends by re-examining some of the most serious evidence against market rationality: excess volatility, the risk premium puzzle, the size anomaly, calendar effects and the 1987 stock market crash

This paper shows that the binomial option pricing model, suitably parameterized, is a special case of the explicit finite difference method.

This article investigates which companies finance themselves through intermediaries and which borrow directly from arm's length investors. Our empirical results show that large companies with abundant cash and collateral tap credit markets directly; these markets cater to safe and profitable industries, and are most active when riskless rates or intermediary earnings are low. We show that determinants of lender selection sharpen during investment downturns and that there are substantial asymmetries in the way firms enter and exit capital markets. These results support a theoretical framework where intermediaries have better reorganizational skills but a higher opportunity cost of capital than bondholders.

Firms undertake a variety of actions to reduce risk through diversification, including entering diverse lines of business, taking on project partners, and maintaining portfolios of risky projects such as R&D or natural resource exploration. By a well-known argument, securities holders do not directly benefit from risk-reducing corporate diversification when they can replicate this diversification on their own. Moreover, shareholders should be risk neutral with respect to the unsystematic risk that is associated with many research projects. Some have argued that corporate risk reduction may be of value, or can otherwise be explained by, the agency relationship between securities holders and managers. We argue that the value of diversification strategies in an agency relationship derives not from its effects on risk, but rather from its effects on the principal's information about the agent's actions. We demonstrate by example that diversification activities may increase or decrease the principal's information, depending on the particular structure of the activity