Researchers released a new paper on implied volatility surface compilation and prediction
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Deepak Bastola and Eli Prushansky released a research paper on implied volatility surface construction and derivatives risk prediction combining finite population survey sampling, tempered inverse probability sequence learning, and causal activity clocks. The study uses split-adjusted NVIDIA stock and options panel data from 2018 to 2025, covering a price expansion range where the underlying asset rose 6.3x from start to end and nearly 20x from trough to peak. The study concludes that among the tested models, short-term prices cannot be predicted beyond a drift-adjusted random walk; sequence models did not outperform the linear HAR-IV benchmark.
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- arXiv q-finImplied Volatility Surface Construction and Forecasting via Survey Sampling, Tempered Inverse-Probability Sequence Learning, and Causal Activity Clocks
A quantitative finance study based on a split-adjusted panel of NVIDIA (NVDA) stocks and options from 2018 to 2025, integrating finite population survey sampling, tempered inverse-probability sequence learning, and causal activity clock methods to conduct implied volatility surface construction and derivatives risk prediction analysis.
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