We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps...

This paper establishes a general relation between investor's ambiguity and non-Gaussianity of financial asset returns. Based on that relation and utilizing a flexible non-Gaussian returns model for the joint distribution of portfolio and currency returns, we develop an ambiguity-adjusted dynamic...

We propose a novel methodology that jointly estimates the proportions of skilled/unskilled funds and the cross-sectional distribution of skill in the mutual fund industry. We model this distribution as a three-component mixture of a point mass at zero and two components — one negative, one...

The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and nonellipticity. It introduces a so-called...

Using properties of the cdf of a random variable defined as a saddle-type point of a real valued continuous stochastic process, we derive first-order asymptotic properties of tests for stochastic spanning w.r.t. a stochastic dominance relation. First, we define the concept of Markowitz...

We develop a new approach for evaluating performance across hedge funds. Our approach allows for performance comparisons between models that are misspecified – a common feature given the numerous factors that drive hedge fund returns. The empirical results show that the standard models used in...

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We develop a novel approach to jointly examine skill, scale, and value added across individual funds. We find that the value added is (i) positive for the vast majority of funds, and (ii) close to its optimal level after an adjustment period possibly due to investors' learning. We also show that...

We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity" in all asset classes that we study (US equities, international equities, bonds,...