arXiv subjects

Federico M. Bandi

Publications and source records attributed to Federico M. Bandi.

3 recordsLinked to original sources

arXiv2026

(Early) AI Compute Asset Pricing

Compute (computing power) is a scarce, capital-intensive input at the center of the AI economy. Compute capital expenditure and service flow already exceed 1% of U.S. GDP and are growing rapidly. The price of compute reflects uncertainty over AI adoption. The announced launch of compute futures turns this uncertainty into a tradable risk, raising questions on the pricing of a new asset class. We provide an early asset-pricing framework for compute. We begin by discussing the underlying compute rental market and its indexation. We then turn to pricing: 1) direct no-arbitrage links between futures prices and current spot prices fail due to the non-storable nature of compute, 2) synthetic futures prices from existing term rental contracts are likely upper bounds on true futures prices and, 3) upon financialization, futures prices will be investors' expectations of spot prices at expiration net of a risk premium. Using synthetic futures as stand-ins before the compute futures market launches, we construct the first compute futures return panel sorted by GPU generation and maturity. Our preliminary evidence is consistent with a positive compute risk premium, suggesting hedging pressure on the part of compute providers.

q-fin.PR↗

arXiv2026

Ultra-short-term volatility surfaces

Options with maturities below one week, hereafter "ultra-short-term" options, have seen a sharp increase in trading activity in recent years. Yet, these instruments are difficult to price jointly using classical pricing models due to the pronounced oscillations observed in the at-the-money implied-volatility term structure across ultra-short-term tenors. We propose Edgeworth++, a parsimonious jump-diffusion model featuring a nonparametric stochastic volatility component, which provides flexibility in capturing implied-volatility smiles for each tenor, combined with a deterministic shift extension, which allows the model to fit rich at-the-money implied-volatility shapes across tenors. We derive a local (in tenor) expansion of the process characteristic function suited to value ultra-short-term options. The expansion leads to fast and accurate option pricing in closed form via standard Fourier inversion. We discuss the benefits of the proposed approach relative to benchmarks.

q-fin.MF↗

arXiv2025

Local signature-based expansions

We study the local (in time) expansion of a continuous-time process and its conditional moments, including the process' characteristic function. The expansions are conducted by using the properties of the (time-extended) Ito signature, a tractable basis composed of iterated integrals of the driving deterministic and stochastic signals: time, multiple correlated Brownian motions and multiple correlated compound Poisson processes. We show that these properties are conducive to automated expansions to any order with explicit coefficients and, therefore, to stochastic representations in which asymptotics can be conducted for a shrinking time (t to 0), as in the extant continuous-time econometrics literature, but, also, for a fixed time (such that t smaller than 1) with a diverging expansion order. The latter design opens up novel opportunities for identifying deep characteristics of the assumed process.

q-fin.MF↗