---
title: Financial Derivatives Usage and Stock Price Risk in China
url: https://www.ml-quant.com/papers/ssrn/5031452/
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 5031452
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5031452
featured: 2024-11-27
citations: unknown
topic: Derivatives & Volatility
---


# Financial Derivatives Usage and Stock Price Risk in China

The use of financial derivatives has been found to lower stock price crash risk in the Chinese market by controlling self-interested managerial behaviors and improving information disclosure.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5031452
- Identifier: SSRN 5031452
- Released: 2024-11-23
- First featured: Quant Letter No. 76 (2024-11-27): https://www.ml-quant.com/issues/2024-11-27/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
- [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
- [Deep Hedging Bermudan Swaptions](https://www.ml-quant.com/papers/arxiv/2411.10079/): The article introduces a new method for Bermudan swaption hedging using the deep hedging framework, improving profit and loss management.
- [A Risk Sensitive Contract-unified Reinforcement Learning Approach for Option Hedging](https://www.ml-quant.com/papers/arxiv/2411.09659/): The paper proposes a risk-sensitive reinforcement learning approach for dynamic hedging of options, reducing tail risk using historical market data.
- [Spanning Multi‐Asset Payoffs With ReLUs](https://www.ml-quant.com/papers/arxiv/2403.14231/): The article suggests a novel solution to the multi-asset payoff spanning issue using one-hidden-layer feedforward neural networks, improving hedging results with vanilla basket options.
- [Filling in Missing FX Implied Volatilities with Uncertainties: Improving VAE-Based Volatility Imputation](https://www.ml-quant.com/papers/arxiv/2411.05998/): The study explores enhancing the prediction of missing implied volatilities in FX options using modified variational autoencoders (VAEs), which better manage data uncertainty.