---
title: Power Exchange Options Pricing
url: https://www.ml-quant.com/papers/ssrn/5104729/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5104729
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5104729
featured: 2025-02-05
citations: unknown
topic: Derivatives & Volatility
---


# Power Exchange Options Pricing

The article introduces a pricing model for power exchange options that takes into account liquidity risk and counterparty default risk, demonstrating the influence of market liquidity on options prices.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5104729
- Identifier: SSRN 5104729
- Released: 2025-01-20
- First featured: Quant Letter No. 84 (2025-02-05): https://www.ml-quant.com/issues/2025-02-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [A statistical technique for cleaning option price data](https://www.ml-quant.com/papers/arxiv/2501.11164/): The article addresses three common issues in recorded option price datasets and suggests solutions to ensure the reliability of analyses based on these datasets.
- [Option Pricing with Time-Varying Volatility Risk Aversion](https://www.ml-quant.com/papers/arxiv/2204.06943/): The paper presents a pricing kernel with fluctuating volatility risk aversion to account for changes in the pricing kernel's shape, showing reduced pricing errors in an empirical application to the S&P 500 index, the CBOE VIX, and option prices.
- [Neural Term Structure of Additive Process for Option Pricing](https://www.ml-quant.com/papers/arxiv/2408.01642/): The research introduces the neural term structure model for option pricing, providing benefits in creating the risk-neutral measure, pricing formula, and fitting the implied volatility surface.
- [Neural Jumps for Option Pricing](https://www.ml-quant.com/papers/arxiv/2506.05137/): A neural jump stochastic differential equation model is proposed for option pricing, combining neural networks with the traditional jump diffusion model, enhancing pricing accuracy.
- [Applying Informer for Option Pricing: A Transformer-Based Approach](https://www.ml-quant.com/papers/doi/10-5220-0013320900003890/): The research uses the Informer neural network for option pricing in financial markets, showing its improved performance over traditional models and improving financial forecasting.
- [Short-maturity asymptotics for VIX and European options in local-stochastic volatility models](https://www.ml-quant.com/papers/arxiv/2407.16813/): The article explores the derivation of short-term predictions for European and VIX option prices in local-stochastic volatility models, and tests these predictions against numerical simulations.