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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.

168,657 papers · 148 categories

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4 results for “NBBO”

Push-response anomalies in high-frequency S&P 500 price seriesq-fin.TR

Study finds anomalies in high-frequency S&P 500 price changes.

problem Anomalies in high-frequency S&P 500 price changes.

method Using NBBO event-time data, the study forms pairs of backward and forward price increments, standardizes them, and estimates expected responses on a fine grid of push magnitudes.

result Persistent structural shift in expected responses: near zero for short lags, pronounced tails for long lags, indicating correlation between larger historical pushes and nonzero responses.

high-frequency trading S&P 500 price anomalies NBBO liquidity

2025-11-09abs ↗pdf ↗

The Cost of a Free Lunch: Evidence from U.S. Derivatives Marketsq-fin.GN

Study reveals a hidden cost in derivatives markets through option-implied discount factors.

problem The hidden cost in derivatives markets, not visible in price space.

method Minute-level NBBO data on options, reduced-form specification linking carry gap to implementation risk, trading frictions, and financial conditions.

result An annualized carry gap exists, linked to implementation risk and financial conditions.

derivatives markets option-implied discount factors carry gap implementation risk financial conditions

2026-04-21abs ↗pdf ↗

Scaling of inefficiencies in the U.S. equity markets: Evidence from three market indices and more than 2900 securitiesq-fin.TR

Using the most comprehensive, commercially-available dataset of trading activity in U.S. equity markets, we catalog and analyze quote dislocations between the SIP National Best Bid and Offer (NBBO) and a synthetic BBO constructed from direct feeds. We observe a total of over 3.1 billion dislocation segments in the Russ…

2019-02-13abs ↗pdf ↗

NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Processcs.LG

NeuroMem-FHP framework estimates FHP parameters efficiently.

problem Estimating parameters of fractional Hawkes process (FHP) with long-range dependence.

method Developed LSTM and Transformer neural architectures to estimate FHP parameters directly from inter-arrival times.

result Transformer achieves highest estimation accuracy (MSE = 0.1634) compared to classical MLE (MSE = 2.8032).

Fractional Hawkes Process Long-range Dependence Deep Learning Transformer Long Short-Term Memory (LSTM)

2026-07-13abs ↗pdf ↗