Generalized Exchange Option Pricing and Empirical Analysis Based on Asset Liquidity Risk

In this paper, we integrate liquidity risk into the generalized exchange option pricing model. Based on Esscher’s transformation theory, we calculate the expectation and variance of random variables to derive an explicit expression of the generalized exchange option pricing formula considering the liquidity risk of underlying assets, which avoids the complicated calculation of the optimal parameter vector h*. In addition, we plot time-series liquidity curves to screen underlying stocks that fit the model framework, and employ the simulated annealing algorithm to estimate model parameters based on historical data. Finally, numerical simulations are conducted with the estimated parameters and actual market data as inputs, confirming the internal consistency of the model. Relevant parameters are then adjusted to intuitively illustrate how liquidity risk affects option pricing and the associated price movements.

Authors

Institutions

Publication Details

Journal
Mathematics
Published
2026-09-06
DOI
https://doi.org/10.3390/math14173223
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generalized Exchange Option Pricing and Empirical Analysis Based on Asset Liquidity Risk

Sisi Wan, Zi Wang, Qing Wang
Mathematics
Stochastic processes and financial applications
article

Generalized Exchange Option Pricing and Empirical Analysis Based on Asset Liquidity Risk

Sisi Wan, Zi Wang, Qing Wang
article en

Abstract

In this paper, we integrate liquidity risk into the generalized exchange option pricing model. Based on Esscher’s transformation theory, we calculate the expectation and variance of random variables to derive an explicit expression of the generalized exchange option pricing formula considering the liquidity risk of underlying assets, which avoids the complicated calculation of the optimal parameter vector h*. In addition, we plot time-series liquidity curves to screen underlying stocks that fit the model framework, and employ the simulated annealing algorithm to estimate model parameters based on historical data. Finally, numerical simulations are conducted with the estimated parameters and actual market data as inputs, confirming the internal consistency of the model. Relevant parameters are then adjusted to intuitively illustrate how liquidity risk affects option pricing and the associated price movements.

MathematicsVol. 14(17)
Harbin Normal University (CN)
Openalex Percentile: Top 7%
Stochastic processes and financial applications
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.