In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We...

We present a stochastic simulation forecasting model for stress testing aimed at assessing banks' capital adequacy, financial fragility and probability of default. The paper provides a theoretical presentation of the methodology and the essential features of the forecasting model on which it is...

Italian Abstract: Presentiamo un modello stocastico multivariato, per sviluppare stress test finalizzati a valutare l'adeguatezza patrimoniale delle banche e il loro grado di fragilità finanziaria. L'articolo fornisce una descrizione teorica del metodo e delle caratteristiche essenziali del...

Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or...

We present a stochastic simulation forecasting model to stress-test banks' capital adequacy and to estimate probability of infringement of regulatory capital ratios and default probability. The stochastic methodology proposed is based on a simplified reduced model that provides a manageable...

The largest US banks and Systemically Important Financial Institutions are required by regulatory mandate to estimate the operational risk capital they must hold using an Advanced Measurement Approach (AMA) as defined by the Basel II/III Accords. Most of these institutions use the Loss...

We develop two neo-classical methods for function approximations, the generalized stochastic sampling (gSS) and the functional tensor train (fTT) methods, that are high-performing alternatives to generic deep neural networks (DNNs) currently routinely proposed for function approximations in...

M-PRESS-CreditRisk is a new top-down macro stress testing framework that can help supervisors gauge banks' capital adequacy related to credit risk. For the first time, it combines calibration of microprudential capital requirements and macroprudential buffers in a unified, coherent framework....

We present a stochastic simulation forecasting model for stress testing that is aimed at assessing banks’ capital adequacy, financial fragility, and probability of default. The paper provides a theoretical presentation of the methodology and the essential features of the forecasting model on...