[In a Lucas exchange economy with standard power utility, we study asset prices under learning and ambiguous information. In contrast with models featuring only learning or ambiguity, our model is successful in matching the equity premium, the interest rate, and the volatility of stock returns under empirically reasonable parameters. Our closed-form formulas also show that a severe downward bias arises in the empirical relation between stock returns and return volatility. We quantify this bias in simulations and show that our model can explain why such a relation is difficult to detect in the data.]

We study the impact of economic policy uncertainty on the term structure of nominal interest rates. In a general equilibrium model populated by an uncertainty averse agent, we show that political uncertainty not only affects the yield curve and the corresponding volatility term structure but also bond risk premia carry a premium for political uncertainty. Our model simultaneously captures both the shape of the yield curve and the hump shape of yield volatilities, a stylized feature that is hard to match with a theoretical model. Our model gives rise to a set of testable predictions for which we find strong support in the data: Higher policy uncertainty leads to a significant decline in yield levels and increases bond yield volatilities. Moreover, policy uncertainty predicts future short rates and has an ambiguous effect on term premia. Finally, short (long) maturity bond risk premia respond negatively (positively) to increases in policy uncertainty.

To explore the rationality and competitiveness of the mutual fund industry, we analyze the alpha of active and index mutual funds from a global sample of more than 60,000 equity and fixed income funds and test the null hypothesis that alphas to investors are zero. We distinguish between institutional and retail investors since there are significant differences in management fees, economies of scale, and information asymmetries between these two groups. Using a new robust statistical test, we cannot reject our null hypothesis for the majority of investment categories. We find that the average active fund has less exposure to traditional risk factors, but higher sensitivity to alternative risk premia. Fund persistence and the impact of size and fees add further support to our conclusion that the mutual fund industry is highly competitive, except for US domestic funds. This set of funds is excessively overfunded compared with other fund categories.

Despite the enormous growth of the asset management industry during the past decades, little is known about the asset pricing implications of investment intermediaries. Standard models of investment theory neither address the distinction between individual and institutional investors nor the potential implications of direct investing and delegated investing. In a model with endogenous delegation, the authors find that delegation leads to a more informative price system and lower equity premia. In the presence of relative return objectives, stocks exhibiting high correlations with the benchmark have significantly lower returns than stocks with low correlations. The authors' empirical results support the model's predictions.

We propose a novel time-changed Lévy LIBOR (London Interbank Offered Rate) market model for jointly pricing of caps and swaptions. The time changes are split into three components. The first component allows matching the volatility term structure, the second generates stochastic volatility, and the third accommodates for stochastic skew. The parsimonious model is flexible enough to accommodate the behavior of both caps and swaptions. For the joint estimation we use a comprehensive data set spanning the financial crisis of 2007–2010. We find that, even during this period, neither market is as fragmented as suggested by the previous literature.

We present a prediction model to forecast corporate defaults. In a theoretical model, under incomplete information in a market with publicly traded equity, we show that our approach must outperform ratings, Altman’s Z -score, and Merton’s distance to default. We reconcile the statistical and structural approaches under a common framework; that is, our approach nests Altman’s and Merton’s approaches as special cases. Empirically, the combined approach is indeed the most powerful predictor, and the numbers of observed defaults align well with the estimated probabilities. With a new transformation method, we obtain cycle-adjusted forecasts that still outperform ratings.

Classical option pricing theories are usually built on the law of one price, neglecting the impact of market liquidity that may contribute to significant bid-ask spreads. Within the framework of conic finance, we develop a stochastic liquidity model, extending the discrete-time constant liquidity model of Madan (2010). With this extension, we can replicate the term and skew structures of bid-ask spreads typically observed in option markets. We show how to implement such a stochastic liquidity model within our framework using multidimensional binomial trees and we calibrate it to call and put options on the S&P 500.

We investigate the implications of technological innovation and non-diversifiable risk on entrepreneurial entry and optimal portfolio choice. In a real options model where two risk-averse individuals strategically decide on technology adoption, we show that the impact of non-diversifiable risk on the option timing decision is ambiguous and depends on the frequency of technological change. Compared to the complete market case, non-diversifiable risk may accelerate or delay the optimal investment decision. Moreover, strategic considerations regarding technology adoption play a central role for the entrepreneur’s optimal portfolio choice in the presence of non-diversifiable risk.

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 study the economic benefits from using credit scoring models. We contribute to the literature by relating the discriminatory power of a credit scoring model to the optimal credit decision. Given the receiver operating characteristic (ROC) curve, we derive (a) the profit-maximizing cutoff and (b) the pricing curve. Using these two concepts and a mixture thereof, we study a stylized loan market model with banks differing in the quality of their credit scoring model. Even for small quality differences, the variation in profitability among lenders is large and economically significant. We end our analysis by quantifying the impact on profits when information leaks from a competitor’s scoring model into the market.