How do lenders use their reputation when participating in syndicated loans? I address this question by focusing on syndicate composition with respect to participants' reputation and its impact on loan spreads. I find that lender reputation enables it to compete in terms of choosing the types of...

How households shift spending across firms in response to income fluctuations is an important source of risk to individual firms. Using transaction-level data, we study how households interact with the universe of retailers following changes in income. We find that increases in income, both...

The distribution of firm sizes is known to be heavy tailed. In order to account for this stylized fact, previous studies have focused mainly on growth through investments in a company's own operations (internal growth). Thereby, the impact of mergers and acquisitions (M&A) on the firm size...

We provide a model and empirical tests showing how an active acquisition market positively affects firm incentives to innovate and conduct R&D. Our model shows how the incentives of small firms to conduct R&D in order to innovate increase with competition, demand and the probability that they...

We consider an economy populated by CARA investors who trade, accounting for their price impact, multiple risky assets with arbitrary distributed payoffs. We propose a constructive solution method: finding the equilibrium reduces to solving a linear ordinary differential equation. With market...

Multi-period-ahead forecasts of returns' variance are used in most areas of applied finance where long horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this paper, we compare several approaches of...

We develop theory of a novel fast bootstrap for dependent data. Our scheme deploys i.i.d. resampling of smoothed moment indicators. We characterize the class of parametric and semiparametric estimation problems for which the method is valid. We show the asymptotic re refinements of the new...

We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection...

We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's major cities. Building on the Log-Periodic Power Law Singular (LPPLS) model of self-reinforcing feedback loops, we use the quantile...

Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this. The threshold effect is introduced in the autoregressive and/or the fractional...