2 citations · 2 across the 17 of their papers we have counts for
7 papers · 1 filter
The Mathematics of Volatility Surfaces
Miquel Noguer i Alonso
This paper develops a unified mathematical theory of implied, local, and learned volatility surfaces. Total variance is an infinite-dimensional state constra…
Financial Epiplexity: A Theory of Learnable Market Structure under Bounded Computation
Miquel Noguer i Alonso
Financial markets are hard to predict, not because price moves are purely random, but because structure is strategic, capacity-constrained, and computationally difficult. Classical…
Tail Risk Management with Puts and Trend Following: A CVaR Framework for Crashes and Drawdowns
Miquel Noguer I Alonso, Ali Al Fallouji
Tail-risk management is not only an instrument-selection problem. It is an allocation problem across loss mechanisms: abrupt crash states, volatility repricing, and persistent draw…
Pretrained Time-Series Foundation Models for Financial Return Forecasting
Miquel Noguer I Alonso, Rodolfo Pereira Franklin
Financial return forecasting is a difficult test case for time-series foundation models (TSFMs) due to low signal-to-noise ratios, structural breaks, heavy tails, and weak persiste…
The Mathematics of Modeling the Future
Miquel Noguer i Alonso
Modeling the future requires specifying conditional laws relative to an evolving information flow and describing their movement across time. This paper provides a unified mathemati…
Markets Are Not Random, They Are Hard to Predict
Miquel Noguer i Alonso
Financial returns are often called ``random,'' but the word conflates ontic chance, epistemic ignorance, strategic feedback, and model instability. This essay argues that financial…
Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Control
Miquel Noguer I Alonso
Financial decision systems require fast surrogate models for pricing, calibration, hedging, XVA, stress testing, and portfolio optimization. Standard neural surrogates reproduce pr…