In a discrete-time setting, we study arbitrage concepts in the presence of convex trading constraints. We show that solvability of portfolio optimization problems is equivalent to absence of arbitrage of the first kind, a condition weaker than classical absence of arbitrage opportunities. We...

In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and Bayesian optimization. Gaussian processes can be seen as...

Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based...

Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based...

Purpose of this paper was to research portfolio optimization problem on Croatian capital market using Markowitz theory. Research systematically investigated the selection of securities, and defined the importance of using fundamental analysis when selecting the best combination of securities....

This paper studies the topic of cost-efficiency in incomplete markets. A portfolio payoff is called cost-efficient if it achieves a given probability distribution at some given investment horizon with a minimum initial budget. Extensive literature exists for the case of a complete financial...

We consider the problem of maximizing expected utility for a power investor who can allocate his wealth in a stock, a defaultable security, and a money market account. The dynamics of these security prices are governed by geometric Brownian motions modulated by a hidden continuous time finite...

The purpose of this paper is to solve a stochastic control problem consisting of optimizing the management of a trading system. Two model free machine learning algorithms based on Reinforcement Learning method are compared: the Q-Learning and the SARSA ones. Both these models optimize their...

This paper deals with the problem of the cost of uncertainty associated with the utility maximization problem in a complete market with multiple risky assets and unobservable dividends. This leads naturally to a partial information setup from which filtering techniques can be applied. Using a...

In this paper we review some standard and more recent filtering techniques, based on Random Matrix Theory (RMT), that can reduce the “empirical” noise and slightly improve standard Markowitz model's predictions based on dynamics of a covariance matrix