This paper investigates the efficiency of stock index options traded over-the-counter (OTC) and on the exchanges in Hong Kong and Japan. Our findings suggest that implied volatility is superior to either historical volatility or a GARCH-type volatility forecast in predicting future volatility in both the OTC and exchange markets. This paper is also one of the first to compare the predictive power of the implied volatility of stock index options traded OTC to that of exchange-traded stock index options. Our evidence suggests that the OTC market is more efficient than the exchanges in Japan, but that the opposite is true in Hong Kong.

Credit rating agencies do not only disclose simple ratings but announce watchlists (rating reviews) and outlooks as well. This paper analyzes the economic function underlying the review procedure. Using Moody’s rating data between 1982 and 2004, we find that for borrowers of high creditworthiness, rating agencies employ watchlists primarily in order to improve the delivery of information. For low-quality borrowers, in contrast, the review procedure seems to have developed into an implicit contract á la Boot et al. (2006), inducing the companies “on watch” to abstain from risk-augmenting actions. The agencies’ economic role hence appears to have been enhanced from a pure information certification towards an active monitoring function.

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2’s of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.

This study examines the role of reputation stretching in the context of mutual funds. We show that the reputation stretching strategy increases net fund inflows to new funds run by well-performing fund managers and yields a net increase of fund inflows to fund families. Reputable fund managers exhibit one-year performance persistence for managing new funds, which can help investors assess managers when selecting funds. We also find that the decrease in information asymmetry associated with managerial reputation benefits investors by leading to an increase in new fund returns in the short run, compared to those of new funds run by managers without track records. Overall, the reputation stretching strategy benefits both investors, by reducing information asymmetry and improving investment returns, and fund families, by increasing net fund inflows to new equity funds.

We study the profitability of Covered Interest Parity (CIP) arbitrage violations and their relationship with market liquidity and credit risk using a novel and unique dataset of tick-by-tick firm quotes for all financial instruments involved in the arbitrage strategy. The empirical analysis shows that positive CIP arbitrage deviations include a compensation for liquidity and credit risk. Once these risk premia are taken into account, small arbitrage profits only accrue to traders who are able to negotiate low trading costs. The results are robust to stale pricing and the nonsynchronous trading occurring in the markets involved in the arbitrage strategy.

We model claim arrival and loss uncertainties jointly in a doubly-binomial framework to price an Asian-style catastrophe (CAT) option with a non-traded underlying loss index using the no-arbitrage martingale pricing methodology. We span these uncertainties by benchmarking to the shadow price of a one-claim bond and the premium of a reinsurance contract. We implement a stochastic time change from calendar time to claim time to more efficiently price the CAT option as a random sum – a binomial sum of claim time binomial Asian option prices. This choice of the operational time dimension allows us to incorporate different patterns of catastrophe arrivals by adjusting the claim arrival probability. We demonstrate this versatility by incorporating a mean-reverting Ornstein-Uhlenbeck intensity arrival process. Simulation results verify our model predictions and demonstrate how the claim arrival probability varies with the expected claim arrival intensity.