Modeling Stock Return Distributions and Pricing Options

This article provides evidence that stock returns, after truncation, might be modeled by a special type of continuous mixtures of normals, so-called <italic>q</italic>-Gaussians. Negative binomial distributions might model the counts for extreme returns. A generalized jump-diffusion model is proposed, and an explicit option-pricing formula is obtained.

Authors

Institutions

Publication Details

Journal
The Journal of Derivatives
Published
2025-12-03
DOI
https://doi.org/10.3905/jod.2025.1.244
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling Stock Return Distributions and Pricing Options

Xinxin Jiang
The Journal of Derivatives
Complex Systems and Time Series Analysis
article

Modeling Stock Return Distributions and Pricing Options

Xinxin Jiang
article en

Abstract

This article provides evidence that stock returns, after truncation, might be modeled by a special type of continuous mixtures of normals, so-called <italic>q</italic>-Gaussians. Negative binomial distributions might model the counts for extreme returns. A generalized jump-diffusion model is proposed, and an explicit option-pricing formula is obtained.

The Journal of DerivativesVol. 33(3)
Suffolk University (US)
Openalex Percentile: Top 55%
Complex Systems and Time Series Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Modeling Stock Return Distributions and Pricing Options — Xinxin Jiang · The Journal of Derivatives (2025) | TGRS Research Map | TGRS