Beyond the Sharpe Ratio: A Sequential Functional Limit Theory and Calibrated Path Monitoring

A terminal Sharpe ratio discards the path by which performance was achieved. This paper develops one statistical consequence of that observation. For independent and identically distributed returns with a finite fourth moment, the entire path of prefix Sharpe estimates, suitably scaled, converges weakly to a nonstationary Gaussian process on any horizon bounded away from the start of the sample. The limit is a Brownian motion divided by elapsed time, so its variance falls in proportion to the fraction of the sample used. It is not a Brownian bridge, and treating it as one misstates both the shape of the resulting band and its width. A uniform-prefix lemma establishes the required asymptotic linearization, and an explicit influence function identifies the limiting scale as a fourth-moment functional of the return distribution, which is where the theorem’s boundary originates. The closest methodological relatives are the recursive-estimates fluctuation and sequential monitoring tests of structural-break econometrics; what is new is not the shape of that argument but the object, a nonlinear ratio whose scale inherits a moment condition a linear-model fluctuation test does not carry. Monte Carlo evidence converts the limit into simultaneous prefix bands, using ten thousand paths per design and a critical value simulated on the same grid over which the bands are evaluated. Two volatility designs that share the persistence of a heavy-tailed stress design, but satisfy the fourth-moment condition, cover at the nominal rate, whereas the stress design covers about ninety percent; the failure is therefore attributable to the moment condition rather than to near-integrated volatility. The paper also gives a conditional asymptotic confidence sequence under a named strong-approximation assumption, with explicit boundary slack and a deterministic burn-in limit, so that compact-horizon convergence is not mislabelled as anytime validity. Independently calibrated monitoring simulations compare path and terminal alarms under the same heavy-tail stress. The result is deliberately narrow: a functional limit, an honest failure boundary, and operational monitoring implications

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-25
DOI
https://doi.org/10.5281/zenodo.21544428
Primary Topic
Financial Risk and Volatility Modeling
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Beyond the Sharpe Ratio: A Sequential Functional Limit Theory and Calibrated Path Monitoring

Miquel Noguer Alonso
Zenodo (CERN European Organization for Nuclear Research)
Financial Risk and Volatility Modeling
preprint

Beyond the Sharpe Ratio: A Sequential Functional Limit Theory and Calibrated Path Monitoring

Miquel Noguer Alonso
preprint en

Abstract

A terminal Sharpe ratio discards the path by which performance was achieved. This paper develops one statistical consequence of that observation. For independent and identically distributed returns with a finite fourth moment, the entire path of prefix Sharpe estimates, suitably scaled, converges weakly to a nonstationary Gaussian process on any horizon bounded away from the start of the sample. The limit is a Brownian motion divided by elapsed time, so its variance falls in proportion to the fraction of the sample used. It is not a Brownian bridge, and treating it as one misstates both the shape of the resulting band and its width. A uniform-prefix lemma establishes the required asymptotic linearization, and an explicit influence function identifies the limiting scale as a fourth-moment functional of the return distribution, which is where the theorem’s boundary originates. The closest methodological relatives are the recursive-estimates fluctuation and sequential monitoring tests of structural-break econometrics; what is new is not the shape of that argument but the object, a nonlinear ratio whose scale inherits a moment condition a linear-model fluctuation test does not carry. Monte Carlo evidence converts the limit into simultaneous prefix bands, using ten thousand paths per design and a critical value simulated on the same grid over which the bands are evaluated. Two volatility designs that share the persistence of a heavy-tailed stress design, but satisfy the fourth-moment condition, cover at the nominal rate, whereas the stress design covers about ninety percent; the failure is therefore attributable to the moment condition rather than to near-integrated volatility. The paper also gives a conditional asymptotic confidence sequence under a named strong-approximation assumption, with explicit boundary slack and a deterministic burn-in limit, so that compact-horizon convergence is not mislabelled as anytime validity. Independently calibrated monitoring simulations compare path and terminal alarms under the same heavy-tail stress. The result is deliberately narrow: a functional limit, an honest failure boundary, and operational monitoring implications

Zenodo (CERN European Organization for Nuclear Research)
Allen Institute for Artificial Intelligence (US)
Financial Risk and Volatility Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.