VaR_Delta-Normal fails in two counts: subadditivity and potentially producing losses larger than its portfolio value. This paper solves the second inconsistency developing formulas derived from a put option, named PVaR_Delta-Normal and Put_Expected_Shortfall, PSF_Delta-Normal; the latter also...

We discuss the pros of adopting government-issued digital currencies as well as a supranational digital iCurrency. One such pro is to get rid of paper money (and coinage), a ubiquitous medium for spreading germs, as highlighted by the recent coronavirus outbreak. We set forth three policy...

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between...

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...

The existing replication policies at top finance journals are far weaker than the policies at top economics journals. This paper explores both the costs and benefits of having a stronger replication policy in the context of my failed 2010 initiative to develop a unified policy across all top...

The cost of systemic risk in the over-the-counter (OTC) derivatives market is described and estimated. Modern portfolio theory (MPT), applied to OTC derivatives, predicts this cost, which has been growing since 1970. This cost grew because Congress blocked MPT's predicted market forces. Without...

Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes...

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...

We discuss the idea of a purely algorithmic universal world iCurrency set forth in: "https://ssrn.com/abstract=2542541" https://ssrn.com/abstract=2542541 and expanded in: "https://ssrn.com/abstract=3059330" https://ssrn.com/abstract=3059330 in light of recent developments, including Libra. Is...