Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given...

We present a highly mechanical method for the construction of implied volatility surface and implied transition probability density function that satisfies the no arbitrage condition(NAC) under missing data environment. This paper assumes a Dupire's model which is a simple extension of the...

This paper considers the production planning problem of a firm that produces a single product using a process that has multiple production lines (or machines) in parallel, each with a finite production capacity. Specifically, the firm has m parallel production lines, each with capacity of P...

A new algorithm for calibrating agent-based models is proposed, which employs a popular gradient boosting framework. Machine learning techniques are not used to develop a surrogate model, but rather assist in narrowing down the parameter space during the search for optimal parameters. Our...

Libor is arguably the world's most important number with more than USD 350 trillion of loans and financial contracts referencing this rate. Libor benchmark interest rates are being replaced with alternative reference rates (ARRs). There is no guarantee Libor rates will continue to be quoted...

This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning...

Starting from the Avellaneda–Stoikov framework, we consider a market maker who wants to optimally set bid/ask quotes over a finite time horizon, to maximize her expected utility. The intensities of the orders she receives depend not only on the spreads she quotes, but also on unobservable...

Nonzero-sum stochastic differential games with impulse controls offer a realistic and far-reaching modelling framework for applications within finance, energy markets, and other areas, but the difficulty in solving such problems has hindered their proliferation. Semi-analytical approaches make...

This work presents a novel policy iteration algorithm to tackle nonzero-sum stochastic impulse games arising naturally in many applications. Despite the obvious impact of solving such problems, there are no suitable numerical methods available, to the best of our knowledge. Our method relies on...