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0111 · Nov 201319922001200920172026
13 results for SSR

Model-free expression for SSR derived in terms of characteristic function.

problem Calculating the skew-stickiness-ratio (SSR) in financial markets.
method Model-free expression using characteristic function, focusing on diffusion and affine forward variance cases.
result General formula for SSR simplifies and becomes particularly tractable in affine forward variance cases, with a limit of H+3/2H+3/2 for short-term limit.

Unsupervised scheme ranks sentences in text documents based on semantic importance.

problem Ranking sentences in text documents without labeled data.
method Extracts essential words and phrases, constructs semantic phrase and sentence graphs, applies PageRank, combines scores, and optimizes for topic diversity.
result SSR outperforms individual judges and compares favorably with combined rankings on benchmarks.

CAPITAL algorithm identifies optimal patient subgroups for better treatment.

problem Identify maximum number of patients benefiting from better treatment.
method Constrained Policy Tree Search (CAPITAL) algorithm to find optimal subgroup selection rule (SSR).
result Maximizes the number of patients with enhanced treatment effects.

We revisit the ``Smile Dynamics'' problem, which consists in relating the implied leverage (i.e. the correlation of the at-the-money volatility with the returns of the underlying) and the skew of the option smile. The ratio between these two quantities, called ``Skew-Stickiness Ratio'' (SSR) by Bergomi (Smile Dynamics …

2013-11-16abs ↗pdf ↗

A new method for generating SPX and VIX risk scenarios using perturbed optimal transport.

problem Generating accurate risk estimates for SPX and VIX without full recalibration.
method A joint optimal transport calibration with perturbation methodology for sensitivities, combined with Skew Stickiness Ratio dynamics.
result The proposed method produces accurate risk estimates relative to full recalibration and is computationally faster.

In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which…

2018-07-31abs ↗pdf ↗

GA-MSSR optimizes forex trading rules for higher returns and reduced risk.

problem Noisy market data affects the consistency and profitability of trading algorithms.
method Optimized trading rules derived from technical indicators using a Genetic Algorithm.
result GA-MSSR achieved superior performance with significant positive returns and reduced risk factors.