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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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1122 · Jul 201719922001200920172026
13 results for Brexit

The study quantifies Brexit risk using SABR dynamics and Bayesian methods.

problem Measuring tail risk in EUR-GBP options related to Brexit.
method Data-driven statistical indicator, lognormal SABR dynamics, Bayesian estimation, inverse calibration problem, Markov chain Monte Carlo.
result A closed-form expression for the martingale defect quantifying tail risk.

We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd June 2016. Our implementation includes an enhanced model calibration using Genetic Algorithms. We selected a few historical financial series sensitive to the Brexit/Brem…

2016-06-22abs ↗pdf ↗

Using 1-min returns of Bitcoin prices, we investigate statistical properties and multifractality of a Bitcoin time series. We find that the 1-min return distribution is fat-tailed, and kurtosis largely deviates from the Gaussian expectation. Although for large sampling periods, kurtosis is anticipated to approach the G…

2017-07-24abs ↗pdf ↗

Much significant research has been done to investigate various facets of the link between Bitcoin price and its fundamental sources. This study goes beyond by looking into least to most influential factors-across the fundamental, macroeconomic, financial, speculative and technical determinants as well as the 2016 event…

2017-07-05abs ↗pdf ↗

QGMS framework detects market endpoints using geometric patterns.

problem Identifying market endpoints in large-scale movements.
method Hybrid of geometric pattern recognition and quantitative modeling.
result Consistently identifies market endpoints before major reversals.

Study of negative ads on social media during U.S. midterm elections.

problem Understanding the effectiveness and mechanisms of negative advertising on social media.
method Machine learning for sentiment analysis, AI image recognition, ordinal regressions.
result Negative ads are less effective than previously thought, anger is a key mechanism.