---
title: No-arbitrage conditions and pricing from discrete-time to continuous-time strategies
url: https://www.ml-quant.com/papers/doi/10-1007-s10436-023-00426-1/
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: doi:10.1007/s10436-023-00426-1
source_url: http://dx.doi.org/10.1007/s10436-023-00426-1
featured: 2024-05-15
citations: 4
topic: Trading, Microstructure & Execution
---


# No-arbitrage conditions and pricing from discrete-time to continuous-time strategies

The paper presents a framework for continuous-time financial market models, demonstrating that no-arbitrage conditions apply in continuous time if they apply in discrete time, and super-hedging prices are the same in both times.

- Source: http://dx.doi.org/10.1007/s10436-023-00426-1
- Identifier: doi:10.1007/s10436-023-00426-1
- Released: 2024-05-10
- First featured: Quant Letter No. 49 (2024-05-15): https://www.ml-quant.com/issues/2024-05-15/
- Citations (Semantic Scholar): 4
- Published in: Annals of Finance
- Topic: Trading, Microstructure & Execution

## Related

- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://www.ml-quant.com/papers/arxiv/2501.09747/): A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
- [Deep Reinforcement Learning for Active High Frequency Trading](https://www.ml-quant.com/papers/arxiv/2101.07107/): A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
- [Inflation and Trading](https://www.ml-quant.com/papers/ssrn/4822700/): Research indicates that investors often hold unrealistic expectations about stock returns during high inflation, and lack knowledge about inflation-hedging strategies, affecting their trading decisions.
- [Can Machine Learning Unlock New Insights into High-Frequency Trading?](https://www.ml-quant.com/papers/ssrn/4826698/): Machine learning is being used to explore the link between high-frequency trading and financial market trends, offering new ways to identify different trading strategies and their effects on market information.
- [High-frequency stock market order transitions during the US-China trade war 2018: A discrete-time Markov chain analysis.](https://www.ml-quant.com/papers/doi/10-1063-5-0176892/): The research analyzes high-frequency stock market data to understand trading dynamics, revealing that similar strategies are used regardless of volatility during the 2018 USA-China trade war.
- [Price-Aware Automated Market Makers: Models Beyond Brownian Prices and Static Liquidity](https://www.ml-quant.com/papers/arxiv/2405.03496/): Advanced Models: The article presents models for improving quotes in automated market making platforms, considering complex price changes and demand fluctuations.