---
title: Filtering without Recursion and Some of Its Uses in Financial Economics
url: https://www.ml-quant.com/papers/repec/tin-wpaper-20260068/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-23
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:tin:wpaper:20260068
source_url: https://econpapers.repec.org/RePEc:tin:wpaper:20260068
featured: 2026-10-23
citations: unknown
topic: Trading, Microstructure & Execution
---


# Filtering without Recursion and Some of Its Uses in Financial Economics

A new parallel filter removes price microstructure noise from high-frequency trade data without bias, producing flat volatility signatures down to one-second frequencies.

- Source: https://econpapers.repec.org/RePEc:tin:wpaper:20260068
- Identifier: RePEc:tin:wpaper:20260068
- Released: 2026-10-10
- First featured: Quant Letter No. 136 (2026-10-23): https://www.ml-quant.com/issues/2026-10-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution
- Authors: Simon Donker van Heel, Neil Shephard

## 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.
- [FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions](https://www.ml-quant.com/papers/arxiv/2505.05784/): High-Frequency Trading: FlowHFT, a new imitation learning framework, outperforms traditional high-frequency trading models by learning from multiple expert models.
- [High-frequency intraday trading for battery storages](https://www.ml-quant.com/papers/arxiv/2504.06932/): The paper presents an automated trading strategy for battery energy storage systems in the power intraday market, showing increased revenue potential dependent on trading speed.
- [Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making](https://www.ml-quant.com/papers/arxiv/2502.17417/): An event-driven Limit Order Book model using a Neural Hawkes process is proposed to simulate high-frequency dynamics in financial markets, offering a more accurate depiction of trade execution.
- [High-frequency lead-lag relationships in the Chinese stock index futures market: tick-by-tick dynamics of calendar spreads](https://www.ml-quant.com/papers/arxiv/2501.03171/): The research examines the relationships between different maturity stock index futures contracts in China's financial futures market, uncovering a regular pattern of price discovery and a profitable trading strategy based on this pattern.