In this paper, we introduce and develop the theory of semimartingale optimal transport in a path dependent setting. Instead of the classical constraints on marginal distributions, we consider a general framework of path dependent constraints. Duality results are established, representing the...

Many problem classes in Finance lead to high-dimensional partial differential equations (PDEs), which need to be solved efficiently. Currently, several methods exist to either circumnavigate the curse of dimensionality or use parallel High Performance Computing to calculate solutions despite it....

We give a method based on convex programming to calculate the optimal super-replicating and sub-replicating prices and corresponding hedging portfolios of a financial derivative in terms of other financial derivatives in a discrete-time setting. Our method produces a model that matches the...

We present efficient partial differential equation (PDE) methods for continuous time mean-variance portfolio allocation problems when the underlying risky asset follows a jump-diffusion. The standard formulation of mean-variance optimal portfolio allocation problems, where the total wealth is...

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk....

One of the most important factors to control for the achievements of investment portfolio returns is risk. If we only think that a 100% positive return is needed to recover a portfolio loss of 50%, we can understand why. With the advent of the exponential growth of technology usage in markets,...

We propose a simple risk-adjusted linear approximation to solve a large class of dynamic models with time-varying and non-Gaussian risk. Our approach generalizes lognormal affine approximations commonly used in the macro-finance literature and can be seen as a first-order perturbation around the...

We propose a simple risk-adjusted linear approximation to solve a large class of dynamic models with time-varying and non-Gaussian risk. Our approach generalizes lognormal affine approximations commonly used in the macro-finance literature and can be seen as a first-order perturbation around the...

Bernardo and Ledoit (2000) develop a very appealing framework to compute pricing bounds based on the so-called gain-loss ratio. Their method has many advantages and very interesting properties and so far one important drawback: the complexity of the numerical computation of the pricing bounds....

When estimating higher-order derivatives of a partial differential equation, it is often essential to compute approximations for artificial boundaries. In this paper we formulate an explicit discretization model for approximation of beta-coefficient of Capital Asset Pricing Model (CAPM). The...