Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a...

We establish the existence and characterization of a primal and a dual facelift - discontinuity of the value function at the terminal time - for utility maximization in incomplete semimartingale-driven financial markets. Unlike in the lower- and upper-hedging problems, and somewhat unexpectedly,...

In the standard model for insurance demand, the risk is totally exogenous and the insurance premium is paid for out of riskless wealth. This model yields results that are mostly in contradiction to everyday observation and have been used to question the pertinence of expected utility theory on...

A P-sigma-martingale density for a given stochastic process S is a local P-martingale Z0 starting at 1 such that the product ZS is a P-sigma-martingale. Existence of a P-sigma-martingale density is equivalent to a classic absence-of-arbitrage property of S, and it is invariant if we replace the...

In light of the growing use, acceptance of, and demand for, machine learning in many fields, notably data science, but also other fields such as finance -- and this in both industry and academics, some university departments might wish, or find themselves forced to, accord to the winds of change...

We investigate the compensation of counterparty exposure in the prices of structured products. Our analysis reveals that product issuers do not compensate retail investors for counterparty exposure before the Lehman default. Post-Lehman, retail prices no longer neglect this risk. We also measure...

We introduce a novel class of credit risk models in which the drift of the survival process of a firm is a linear function of the factors. The prices of defaultable bonds and credit default swaps (CDS) are linear-rational in the factors. The price of a CDS option can be uniformly approximated by...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...