Akash Deep et al. Release Research Paper on NTS Distribution Probability Weighting Functions
Get up to speed
On October 20, 2026, Akash Deep and co-authors released a research paper in the Quantitative Finance section of arXiv, proposing a new method for constructing probability weighting functions from changes in return distributions rather than parameter families. The paper gives the probability weighting function as w(p)=F_post(F_prior^{-1}(p)), where both the prior and posterior return distributions follow the normal tempered stable (NTS) distribution; with variance held fixed, the posterior distribution can differ from the prior along three independent channels: scale, skewness, and tail thickness.
AI-generated from reports · 3 hr agoUpdated
Coverage timeline
Follow the coverage to understand different sides of the event.
- arXiv q-finProbability Weighting Functions for Normal Tempered-Stable Distributions: Orthogonal Channels and Information-Theoretic Indices
This arXiv paper proposes constructing probability weighting functions from changes in return distributions rather than parameter families, with the formula w(p)=F_post(F_prior^{-1}(p)). Both the prior and posterior are normal tempered-stable distributions, and the posterior differs from the prior along three independent channels—scale, skewness, and tail thickness—under the premise of fixed variance.
Trend of this event
Not enough consecutive observations yet; trend is not drawn.