We demonstrate how machine learning based recommender systems can be effectively employed by market makers to filter the information embedded in Requests for Quote (RFQs) to identify the set of clients most likely to be interested in a given bond, or, conversely, the set of bonds that are most...

Using the path-integral formalism we develop an accurate and easy-to-compute semi-analytical approximation to transition probabilities and Arrow-Debreu densities for arbitrary diffusions. We illustrate the accuracy of the method by presenting results for the Black-Karasinski model for which the...

We present an accurate and easy-to-compute approximation of the transition probabilities and the associated Arrow-Debreu (AD) prices for the Inhomogeneous Geometric Brownian Motion (IGBM) model for interest rates, default intensities or volatilities. Through this procedure, dubbed exponent...

We show how Adjoint Algorithmic Differentiation (AAD) can be used to calculate price sensitivities in regression-based Monte Carlo methods reliably and orders of magnitude faster than with standard finite-difference approaches. We present the AAD version of the celebrated least-square algorithms...

We apply adjoint algorithmic differentiation (AAD) to the risk management of derivative securities in the situation where the dynamics of securities prices are given in terms of partial differential equations (PDE). With simple examples, we show how AAD can be applied to both forward and...

We show how Adjoint Algorithmic Differentiation can be combined with the so-called Pathwise Derivative and Likelihood Ratio Method to construct efficient Monte Carlo estimators of second order price sensitivities of derivative portfolios. We demonstrate with a numerical example how the proposed...

We present an arbitrage-free valuation framework for the counterparty exposure of credit derivatives portfolios based on a Clayton dynamical default dependency approach. The method is able to capture consistently the effects of credit spread volatility and credit correlations. By introducing...

In this paper we propose a novel, analytically tractable, one-factor stochastic model for the dynamics of credit default swap (CDS) spreads and their returns, which we refer to as the spread-return mean-reverting (SRMR) model. The SRMR model can be seen as a hybrid of the Black-Karasinski model...