This paper considers multiple market agents who have distinct distributional opinions about the state price density. We first determine the optimal trading positions of a utility maximizing market taker who trades Arrow-Debreu securities for prices set by the market maker. We use calculus of...

This paper considers some univariate and multivariate operational risk models, in which the loss severities are modelled by some weakly tail dependent and heavy-tailed positive random variables, and the loss frequency processes are some general counting processes. In such models, we derive some...

Do hedge funds hedge? In negative states of the world, often not as much as they should. For several styles, we report larger market betas when market returns are low (i.e., “beta in the tails”). We justify this finding through a combination of negative-mean jumps in the market returns and...

We study the out-of-sample properties of robust empirical optimization problems with smooth φ-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative “robustness parameter” δ that controls the size of the deviations...

In many applications, data exhibit skewness and in this paper we present a new family of density functions modeling skewness based on a transformation, analagous to those of location and scale. Here we note that location will always refer to mode. Hence, in order to model data to include shape,...

We study an adverse-selection model in which the distribution of the asset is affected by unobservable actions of the seller. The seller's equilibrium behavior is characterized by a risk-seeking disposition, and a competitive market, in which the underlying distribution of the traded asset is...

Recent work by Medeiros et al. (2019, Journal of Business & Economic Statistics) shows that point forecasts of the random forest machine learning algorithm systematically outperform well-established benchmarks at predicting U.S. inflation. This article extends their work from point to density...

The majority of financial data exhibit asymmetry and heavy tails, which makes forecasting the entire density critically important. Recently, a forecast combination methodology has been developed to combine predictive densities. We show that combining individual predictive densities that are...

Accurate estimation and optimal control of tail risk is important for building portfolios with desirable properties, especially when dealing with a large set of assets. In this work, we consider optimal asset allocations strategies based on the minimization of two asymmetric deviation measures,...

This brief note extends our working paper [1]. A central claim in [1] is that the first passage time densities in 1-3 dimensions have upper bounds that allow the implementation of efficient von Neumann rejection schemes for generating random first passage times. This approach was adopted, among...