The paper generalizes and refines the Fundamental Theorem of Asset Pricing of Dalang, Morton and Willinger in the following two respects: (a) the result is extended to a model with portfolio constraints; (b) versions of the no-arbitrage criterion based on the bang-bang principle in control...

Let S=(S_t), t=0,1,...,T (T being finite), be an adapted R^d-valued process. Each component process of S might be interpreted as the price process of a certain security. A trading strategy H=(H_t), t= 1,...,T, is a predictable R^d-valued process. A strategy H is called extreme if it represents a...

Risk-averse expected utility maximization implies that the pricing kernel must be a non-increasing function of aggregate wealth. However, empirical research has found that the pricing kernel frequently displays a locally increasing portion in aggregate wealth. This is known as the pricing kernel...

We show that if sophisticated institutional managers and individual investors perceive tail-risks differently, then a new explanation for the pricing kernel puzzle emerges. We show, by example, that even a tiny difference in tail-risk perception by the two investor types can explain the pricing...

The objective of this paper is to analyze the procedures used by multinational enterprises to distribute the income generated by its foreign subsidiaries, and how they allocate the taxes paid on this income through the fiscal jurisdiction in which they operate, from the institution based view....

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...

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 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...

Many asset pricing theories treat the cross-section of returns volatility and correlations as two intimately related quantities driven by common factors, which hinders achieving a neat definition of a correlation premium. We formulate a model without factors, but with a continuum of securities...

Taking a portfolio perspective on option pricing and hedging, we show that within the standard Black-Scholes-Merton framework large portfolios of options can be hedged without risk in discrete time. The nature of the hedge portfolio in the limit of large portfolio size is substantially different...