Journal of Computational Finance

We develop a new algorithm that allows us to compute pairwise-correlation sensitivities in a Monte Carlo framework by modifying only one trajectory at a time, resulting in a significant decrease in Brownian noise, computing time and memory requirements. We apply this algorithm to the case of the risk management of a large portfolio of options on baskets of equities, but the same algorithm can be used for computing correlation sensitivities in any Monte Carlo framework.

We discuss applications of policy gradient methods for the optimal execution of an asset position via limit orders. We study two examples in-depth: a parametric limit order book (LOB) model and a realistic generative adversarial neural network (GAN) LOB model.

By using portfolio replication and dynamic hedging techniques, we deduce different models for pricing financial derivatives in multicurrency markets and in the presence of counterparty credit risk, as well as the associated valuation adjustments. For this purpose, we consider that the foreign exchange rates between the different currencies follow stochastic dynamics, while the credit spread is governed by mean-reversion dynamics.

In this paper we propose a machine learning algorithm for time-inconsistent portfolio optimization. The proposed algorithm builds on neural-network-based trading schemes, in which the asset allocation at each time point is determined by a neural network. The loss function is given by an empirical version of the objective function of the portfolio optimization problem.

Motivated by the equations of cross valuation adjustments (XVAs) accounting for the fungibility of capital at risk with variation margin, we introduce a simulation/regression scheme for a class of anticipated backward stochastic differential equations, where the coefficient entails a conditional expected shortfall of the martingale part of the solution.

We adopt the least squares Monte Carlo (LSMC) method to price time-capped American options. The cap can be an independent random variable or dependent on the asset price at a random time. We investigate various time caps. In particular, we give an algorithm for pricing the American options capped by the first drawdown epoch, focusing on the geometric Lévy market.

We study the pricing problem for American options under a regime-switching model with the possibility of a nonoptimal exercise policy (ie, an early or late exercise time), referred to as an irrational strategy.

This paper leverages the equal risk pricing (ERP) framework for the valuation of illiquid financial derivatives. Such a method sets the derivative price as the premium, which leads to equal residual optimal hedging risk for agents hedging the long and short positions on the derivative.

In quantitative finance, stochastic volatility models have gradually become a dominant trend since the publication of Heston's seminal 1993 paper, supported by some very convincing empirical evidence (eg, that obtained in 2009 by Christoffersen et al).

We consider the hedging of European options when the price of the underlying asset follows a single-factor Markovian framework. By working in such a setting, in 2014 Carr and Wu derived a spanning relation between a given option and a continuum of shorter-term options written on the same asset. We extend their approach to simultaneously include options over multiple short maturities.