This research paper delves into the application of Deep Reinforcement Learning (DRL) in
asset-class agnostic portfolio optimization, integrating industry-grade methodologies with …

Recently, there has been considerable interest in machine learning (ML) applications for the
valuation of options. The main motivation is the speed of calibration or, for example, the …

Its intuitiveness and the simplicity of its calculations make the seminal Black-Scholes-Merton
option pricing model the most commonly known and used among all asset pricing models …

We investigate the approximation of path functionals. In particular, we advocate the use of
the Karhunen--Loève expansion, the continuous analogue of principal component analysis …

The computation of fair values for exotic options often necessitates complex pricing
techniques, which remain sparsely addressed in academic literature. Predominantly, the …

We develop a novel method to generate future possible implied volatility surfaces given a
historical sequence of surfaces and extra features such as historical returns. The proposed …

American-type financial instruments are often priced with specific Monte Carlo techniques
whose efficiency critically depends on the dimensionality of the problem and the available …

The spread options are widely used in commodities market and one of the most notable
options is crush spread. This paper investigates the spread option pricing based on Black …

Recently, there has been considerable interest in machine learning (ML) applications for the
valuation of options. The main motivation is the speed of calibration or, for example, the …

The main goal of this paper is to compare the classical MCMC estimation method with a
universal Neural Network (NN) approach to estimate unknown parameters of the Heston …