---
title: Pricing Errors Impact Options Prices & Greeks
url: https://www.ml-quant.com/papers/ssrn/4552151/
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 4552151
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4552151
featured: 2023-08-30
citations: unknown
topic: Derivatives & Volatility
---


# Pricing Errors Impact Options Prices & Greeks

Pricing errors in the base asset can inflate options prices and affect option Greeks, leading to inefficient risk management and hedging if not considered.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4552151
- Identifier: SSRN 4552151
- Released: 2021-06-15
- First featured: Quant Letter No. 14 (2023-08-30): https://www.ml-quant.com/issues/2023-08-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- ['I'm Stranded': Transition Risk Information in CDS and Options](https://www.ml-quant.com/papers/ssrn/4551442/): The research uses CDS and put option prices to gauge the likelihood and impact of climate change policy-related transition risk events, creating a reliable CDS-based index.
- [On Sparse Grid Interpolation for American Option Pricing with Multiple Underlying Assets](https://www.ml-quant.com/papers/arxiv/2309.08287/): The first article introduces a new method for pricing American options with multiple assets, combining dynamic programming and sparse grid-based polynomial interpolation.
- [Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models](https://www.ml-quant.com/papers/arxiv/2309.14784/): The paper offers quantitative error limits for deep neural networks approximating option prices on a risky asset, demonstrating that DNNs can learn option prices with minimal error without the curse of dimensionality.
- [Machine learning for option pricing: an empirical investigation of network architectures](https://www.ml-quant.com/papers/arxiv/2307.07657/): A study finds that the generalized highway network and a DGM variant improve the accuracy and training time of machine learning algorithms for option pricing.
- [A fast Monte Carlo scheme for additive processes and option pricing](https://www.ml-quant.com/papers/arxiv/2112.08291/): The article introduces a quick Monte Carlo method for additive processes, improving accuracy in pricing options that depend on a specific path.
- [Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing](https://www.ml-quant.com/papers/arxiv/2105.10467/): New ultra-fast and highly accurate neural Transition Probability Density Function generators have been developed for use in computational finance.
