Paper: arXiv 2411.13558
Abstract
The strong relative arbitrage problem in Stochastic Portfolio Theory seeks an investment strategy that almost surely outperforms a benchmark portfolio at the end of a given time horizon. The highest relative return in relative arbitrage opportunities is characterized by the smallest nonnegative continuous solution of a Cauchy problem for a partial differential equation (PDE). However, solving this type of PDE poses analytical and numerical challenges, due to the high dimensionality and its non-unique solutions. In this paper, we discuss numerical methods to address the relative arbitrage problem and the associated PDE in a volatility-stabilized market, using time-changed Bessel bridges. We present a practical algorithm and demonstrate numerical results through an example in volatility-stabilized markets.
Complexity vs Empirical Score
- Math Complexity: 9.5/10
- Empirical Rigor: 4.0/10
- Quadrant: Lab Rats — theoretically deep, empirically untested
Why this score: The paper is dense with advanced mathematics including high-dimensional PDEs, Cauchy problems, and stochastic differential equations, but the empirical component is limited to numerical simulations without real-world data or backtesting.
Research Flowchart
flowchart TD Goal["Research Goal:<br>Find nonnegative minimal solution for Cauchy PDE<br>in volatility-stabilized markets"] Data["Input Data:<br>Volatility-stabilized market parameters<br>Equity price dynamics<br>Time horizon T"] Method["Methodology:<br>Time-changed Bessel Bridges<br>Transform PDE into heat equation"] Process["Computational Process:<br>Solve transformed PDE numerically<br>Apply inverse time-change"] Outcome["Key Outcome:<br>Identification of smallest nonnegative solution<br>Characterizes highest relative return<br>Practical numerical algorithm"] Goal --> Data Data --> Method Method --> Process Process --> Outcome
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1 of 63 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.