We develop a new option pricing framework that tightly integrates with how institutional investors manage options positions. The framework starts with the near-term dynamics of the implied volatility surface and derives no-arbitrage constraints on its current shape. Within this framework, we...

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...

A credit derivative is a path dependent contingent claim on the aggregate loss in a portfolio of credit sensitive securities. We estimate the value of a credit derivative by Monte Carlo simulation of the affine point process that models the loss. We consider two algorithms that exploit the...

This paper introduces a new semi-parametric methodology for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor...

We derive a coherent multi-factor model for pricing various derivatives written on the same underlying (potentially non-tradable) asset. We show the difference between a case in which the underlying asset is self-financed and tradable and a case in which it is not. In the first case, an...

Classic option pricing theory values a derivative contract via dynamic replication, and views the derivative as redundant relative to the replicating portfolio. In practice, while dynamic replication proves highly effective in drastically reducing the risk in derivative investments, the...

This paper analyzes a family of multivariate point process models of correlated event timing whose arrival intensity is driven by an affine jump diffusion. The components of an affine point process are self- and cross-exciting, and facilitate the description of complex event dependence...

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...

This paper develops a decentralized theory that determines the fair value of the yield-to-maturity of a bond or bond portfolio based purely on the near-term dynamics of the yield itself. The theory decomposes the yield into three components: its expected change, its risk premium, and its...